Nima Aghaeepour

All Publications

• AI-guided precision parenteral nutrition for neonatal intensive care units. Nature medicine Phongpreecha, T., Ghanem, M., Reiss, J. D., Oskotsky, T., Mataraso, S. J., De Francesco, D., Reincke, S. M., Espinosa, C., Chung, P., Ng, T., Costello, J. M., Sequoia, J. A., Razdan, S., Xie, F., Berson, E., Kim, Y., Seong, D., Szeto, M. Y., Myers, F., Gu, H., Feister, J., Verscaj, C. P., Rose, L. A., Sin, L. W., Oskotsky, B., Roger, J., Shu, C. H., Shome, S., Yang, L. K., Tan, Y., Levitte, S., Wong, R. J., Gaudillière, B., Angst, M. S., Montine, T. J., Kerner, J. A., Keller, R. L., Shaw, G. M., Sylvester, K. G., Fuerch, J., Chock, V., Gaskari, S., Stevenson, D. K., Sirota, M., Prince, L. S., Aghaeepour, N. 2025

  Abstract

  One in ten neonates are admitted to neonatal intensive care units, highlighting the need for precise interventions. However, the application of artificial intelligence (AI) in guiding neonatal care remains underexplored. Total parenteral nutrition (TPN) is a life-saving treatment for preterm neonates; however, implementation of the therapy in its current form is subjective, error-prone and resource-consuming. Here, we developed TPN2.0-a data-driven approach that optimizes and standardizes TPN using information collected routinely in electronic health records. We assembled a decade of TPN compositions (79,790 orders; 5,913 patients) at Stanford to train TPN2.0. In addition to internal validation, we also validated our model in an external cohort (63,273 orders; 3,417 patients) from a second hospital. Our algorithm identified 15 TPN formulas that can enable a precision-medicine approach (Pearson's R = 0.94 compared to experts), increasing safety and potentially reducing cost. A blinded study (n = 192) revealed that physicians rated TPN2.0 higher than current best practice. In patients with high disagreement between the actual prescriptions and TPN2.0, standard prescriptions were associated with increased morbidities (for example, odds ratio = 3.33; P value = 0.0007 for necrotizing enterocolitis), while TPN2.0 recommendations were linked to reduced risk. Finally, we demonstrated that TPN2.0 employing a transformer architecture enabled guideline-adhering, physician-in-the-loop recommendations that allow collaboration between the care team and AI.

  View details for DOI 10.1038/s41591-025-03601-1

  View details for PubMedID 40133525

  View details for PubMedCentralID 10593864

• A machine learning approach to leveraging electronic health records for enhanced omics analysis (vol 7, pg 293, 2025) NATURE MACHINE INTELLIGENCE Mataraso, S. J., Espinosa, C. A., Seong, D., Reincke, S., Berson, E., Reiss, J. D., Kim, Y., Ghanem, M., Shu, C., James, T., Tan, Y., Shome, S., Stelzer, I. A., Feyaerts, D., Wong, R. J., Shaw, G. M., Angst, M. S., Gaudilliere, B., Stevenson, D. K., Aghaeepour, N. 2025

  View details for DOI 10.1038/s42256-025-01021-x

  View details for Web of Science ID 001449397200001

• Epigenomic profile of GBA1 in Parkinson's disease. Parkinsonism & related disorders Berson, E., Zaghroun, R., Santoro, M., Bukhari, S., Seong, D., Shu, C. H., Perna, A., James, T., Montine, K. S., Serrano, G. E., Beach, T. G., Keene, C. D., Chang, H. Y., Corces, M. R., Cholerton, B., Aghaeepour, N., Montine, T. J. 2025; 140: 108066

  Abstract

  While genome-wide association studies have identified GBA1 as a key gene contributing to disease severity and cognitive decline in PD, its molecular effects remain poorly understood.We used integrative bulk ATAC-seq across six brain regions from autopsied individuals with PD and varying genetic risk to characterize region- and cell type-specific molecular differences. Using Cellformer, an AI-based bulk ATAC-seq-deconvolution tool, we determined cell type-specific effects of GBA1 on PD disease progression and then validated our findings using whole transcriptome data from blood samples.Epigenomic differences between PD with ("GBA+"; n = 15) and without ("GBA-", n = 15) GBA1 variants were localized in substantia nigra. Nineteen chromatin-accessible regions strictly separated GBA+ from GBA-, including the promoter sites of key genes such as CACNA1C, EHMT1, and SLC25A48. The effect in GBA + spanned the main cell types in brain, and chromatin differences between GBA- and GBA + increased with neuropathologic progression of disease. Significant differences in the epigenomic profile in GBA+ were observed in neuronal cells (AUROC = 0.8, AUPRC = 0.8, P-value<0.0001). Validation in blood samples distinguished between GBA+ and GBA-subtypes, achieving AUROC values of 0.99. Over 5000 transcripts in blood cells distinguished GBA+ from GBA-, validating key genes and pathways from our epigenomic analysis of brain regions.Our study provides novel insights into the cell type-specific epigenomic and transcriptomic landscape of GBA+ and its molecular divergence from other PD subtypes, and highlights potential therapeutic targets for this genetically defined subset of PD.

  View details for DOI 10.1016/j.parkreldis.2025.108066

  View details for PubMedID 41033114

• Leveraging electronic health records from two hospital systems identifies male infertility-associated comorbidities across time. Communications medicine Woldemariam, S. R., Xie, F., Roldan, A., Roger, J., Tang, A. S., Oskotsky, T. T., Stevenson, D. K., Lathi, R. B., Rajkovic, A., Allen, I. E., Aghaeepour, N., Eisenberg, M., Sirota, M. 2025; 5 (1): 380

  Abstract

  Male infertility (MI) is the sole cause of 20-30% of infertility cases, and it is a contributing factor for an additional 15-20% of cases. However, the full breadth of potential MI risk factors and adverse health outcomes has not been explored.We used electronic health records (EHRs) from the University of California (UC) and Stanford to identify MI-associated comorbidities. We identified 6531 and 5551 MI patients at UC and Stanford, respectively, and 8353 and 2464 vasectomy control patients at UC and Stanford, respectively. Low-dimensional embeddings of patients' diagnosis profiles based on MI status, demographics, or hospital utilization were compared using either Kruskal-Wallis tests followed by post hoc Dunn's tests or Mann-Whitney U tests. We used logistic regression to identify MI-associated comorbidities prior to or after 6 months of a patient's first MI or vasectomy-related record. Pearson correlation coefficients were used to compare primary versus sensitivity logistic regression analyses as well as UC versus Stanford logistic regression analyses. Cox regression was used to assess whether patients had a higher risk of receiving diagnoses significantly associated with MI after the 6-month cutoff at UC.Here, we identify 15 diagnoses that are positively associated with MI before the 6-month cutoff across both hospital systems and all analyses, including less expected comorbidities such as hypothyroidism and other anemias. Using Cox regression, we find that patients have a higher risk of receiving 11 out of 13 diagnoses positively associated with MI after the 6-month cutoff at UC.Our findings can set the groundwork for future studies to clarify the relationship between less expected comorbidities and MI.

  View details for DOI 10.1038/s43856-025-01071-7

  View details for PubMedID 40890520

  View details for PubMedCentralID 6919557

• Placental epigenetic clocks derived from crowdsourcing: Implications for the study of accelerated aging in obstetrics. iScience Bhatti, G., Sufriyana, H., Romero, R., Patel, T., Tekola-Ayele, F., Alsaggaf, I., Gomez-Lopez, N., Su, E. C., Done, B., Hoffmann, S., van Bommel, A., Wan, C., Albrecht, J., Novak, C., DREAM Placenta Clock Challenge Consortium, Chaiworapongsa, T., Sirota, M., Aghaeepour, N., Stolovitzky, G., Bryant, D. R., Tarca, A. L. 2025; 28 (8): 113181

  Abstract

  Epigenetic gestational age acceleration has been implicated in obstetric syndromes including preeclampsia, yet robust conclusions require accurate and unbiased epigenetic age models. Herein, we curated 1,842 public placental methylomes and organized a DREAM challenge to develop models of gestational age. Participants were blinded to the test data that we generated from 384 placentas encompassing normal and complicated pregnancies. Models developed during and post-challenge compared favorably to existing models in terms of accuracy, yet they were better calibrated throughout gestation and indicated that reports of accelerated epigenetic aging in preterm preeclampsia were likely due to modeling artifacts. The models show that accelerated aging is associated with a decrease in birthweight percentiles in male neonates delivered at term. By contrast, preterm accelerated aging was protective against delivery of a small-for-gestational-age neonate regardless of fetal sex. This work informs our understanding of the fetal sex-dimorphic role of the placenta epigenome in obstetrics.

  View details for DOI 10.1016/j.isci.2025.113181

  View details for PubMedID 40822353

• Deep learning-based cell type profiles reveal signatures of Alzheimer's disease resilience and resistance. Brain : a journal of neurology Berson, E., Perna, A., Bukhari, S., Kim, Y., Xue, L., Seong, D., Mataraso, S., Ghanem, M., Chang, A. L., Montine, K. S., Keene, C. D., Kasowski, M., Aghaeepour, N., Montine, T. J. 2025

  Abstract

  Neurological disorders result from the complex and poorly understood contributions of many cell types, essential for uncovering mechanisms behind these disorders and identifying specific therapeutic targets. Single-nucleus technologies have advanced brain disease research, but remain limited by their low nuclear transcriptional coverage, high cost, and technical complexity. To address this, we applied a transformer-based deep learning model that restores cell type-specific investigation transcriptional programs from bulk RNA-seq, significantly outperforming previous methods. This enables large-scale and cost-effective investigation of cell type-specific transcriptomes in complex and heterogeneous phenotypes such as cognitive resilience or brain resistance to Alzheimer's disease. Our analysis identified astrocytes as the major cell mediator of Alzheimer's disease resilience across cerebral cortex regions, while excitatory neurons and oligodendrocyte progenitor cells emerged as the major cell mediators of resistance, maintaining synaptic function and preserving neuron health. Finally, we show that our approach could restore the whole tissue transcriptome, offering an unbiased framework for exploring cell-specific functions beyond single nucleus data.

  View details for DOI 10.1093/brain/awaf285

  View details for PubMedID 40794555

• Benchmarking of pre-training strategies for electronic health record foundation models. JAMIA open Mataraso, S., D'Souza, S., Seong, D., Berson, E., Espinosa, C., Aghaeepour, N. 2025; 8 (4): ooaf090

  Abstract

  Objective: Our objective is to compare different pre-training strategies for electronic health record (EHR) foundation models.Materials and Methods: We evaluated three approaches using a transformer-based architecture: baseline (no pre-training), self-supervised pre-training with masked language modeling, and supervised pre-training. The models were assessed on their ability to predict both major adverse cardiac events and mortality occurring within 12 months. The pre-training cohort was 405679 patients prescribed antihypertensives and the fine tuning cohort was 5525 patients who received doxorubicin.Results: Task-specific supervised pre-training achieved superior performance (AUROC 0.70, AUPRC 0.23), outperforming both self-supervised pre-training and the baseline. However, when the model was evaluated on the task of 12-month mortality prediction, the self-supervised model performed best.Discussion: While supervised pre-training excels when aligned with downstream tasks, self-supervised approaches offer more generalized utility.Conclusion: Pre-training strategy selection should consider intended applications, data availability, and transferability requirements.

  View details for DOI 10.1093/jamiaopen/ooaf090

  View details for PubMedID 40809468

• Metabolomic Profiles During and After a Hypertensive Disorder of Pregnancy: The EPOCH Study. International journal of molecular sciences Hlatky, M. A., Shu, C. H., Bararpour, N., Murphy, B. M., Sorondo, S. M., Leeper, N. J., Wong, F., Stevenson, D. K., Shaw, G. M., Stefanick, M. L., Boyd, H. A., Melbye, M., Sedan, O., Wong, R. J., Snyder, M. P., Aghaeepour, N., Winn, V. D. 2025; 26 (13)

  Abstract

  Hypertensive disorders of pregnancy are associated with a higher risk of later cardiovascular disease, but the mechanistic links are unknown. We recruited two groups of women, one during pregnancy and another at least two years after delivery, including both cases (with a hypertensive disorder of pregnancy) and controls (with a normotensive pregnancy). We measured metabolites using liquid chromatography-mass spectroscopy and applied machine learning to identify metabolomic signatures at three time points: antepartum, postpartum, and mid-life. The mean ages of the pregnancy cohort (58 cases, 46 controls) and the mid-life group (71 cases, 74 controls) were 33.8 and 40.8 years, respectively. The levels of 157 metabolites differed significantly between the cases and the controls antepartum, including 19 acylcarnitines, 12 gonadal steroids, 11 glycerophospholipids, nine fatty acids, six vitamin D metabolites, and four corticosteroids. The machine learning model developed using all antepartum metabolite levels discriminated well between the cases and the controls antepartum (c-index = 0.96), postpartum (c-index = 0.63), and in mid-life (c-index = 0.60). Levels of 10,20-dihydroxyeicosanoic acid best distinguished the cases from the controls both antepartum and postpartum. These data suggest that the pattern of differences in metabolites found antepartum continues to distinguish women who had a hypertensive disorder of pregnancy from women with a normotensive pregnancy for years after delivery.

  View details for DOI 10.3390/ijms26136150

  View details for PubMedID 40649926

• Protocol: the International Milk Composition (IMiC) Consortium - a harmonized secondary analysis of human milk from four studies. Frontiers in nutrition Fehr, K., Mertens, A., Shu, C. H., Dailey-Chwalibóg, T., Shenhav, L., Allen, L. H., Beggs, M. R., Bode, L., Chooniedass, R., DeBoer, M. D., Deng, L., Espinosa, C., Hampel, D., Jahual, A., Jehan, F., Jain, M., Kolsteren, P., Kawle, P., Lagerborg, K. A., Manus, M. B., Mataraso, S., McDermid, J. M., Muhammad, A., Peymani, P., Pham, M., Shahab-Ferdows, S., Shafiq, Y., Subramoney, V., Sunko, D., Toe, L. C., Turvey, S. E., Xue, L., Rodriguez, N., Hubbard, A., Aghaeepour, N., Azad, M. B. 2025; 12: 1548739

  Abstract

  Human milk (HM) contains a multitude of nutritive and nonnutritive bioactive compounds that support infant growth, immunity and development, yet its complex composition remains poorly understood. Integrating diverse scientific disciplines from nutrition and global health to data science, the International Milk Composition (IMiC) Consortium was established to undertake a comprehensive harmonized analysis of HM from low, middle and high-resource settings to inform novel strategies for supporting maternal-child nutrition and health.IMiC is a collaboration of HM experts, data scientists and four mother-infant health studies, each contributing a subset of participants: Canada (CHILD Cohort, n = 400), Tanzania (ELICIT Trial, n = 200), Pakistan (VITAL-LW Trial, n = 150), and Burkina Faso (MISAME-3 Trial, n = 290). Altogether IMiC includes 1,946 HM samples across time-points ranging from birth to 5 months. Using HM-validated assays, we are measuring macronutrients, minerals, B-vitamins, fat-soluble vitamins, HM oligosaccharides, selected bioactive proteins, and untargeted metabolites, proteins, and bacteria. Multi-modal machine learning methods (extreme gradient boosting with late fusion and two-layered cross-validation) will be applied to predict infant growth and identify determinants of HM variation. Feature selection and pathway enrichment analyses will identify key HM components and biological pathways, respectively. While participant data (e.g., maternal characteristics, health, household characteristics) will be harmonized across studies to the extent possible, we will also employ a meta-analytic structure approach where HM effects will be estimated separately within each study, and then meta-analyzed across studies.IMiC was approved by the human research ethics board at the University of Manitoba. Contributing studies were approved by their respective primary institutions and local study centers, with all participants providing informed consent. Aiming to inform maternal, newborn, and infant nutritional recommendations and interventions, results will be disseminated through Open Access platforms, and data will be available for secondary analysis.ClinicalTrials.gov, identifier, NCT05119166.

  View details for DOI 10.3389/fnut.2025.1548739

  View details for PubMedID 40557242

  View details for PubMedCentralID PMC12186657

• Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics. Obstetrics and gynecology Miller, S., Lyell, D., Maric, I., Lancaster, S., Sylvester, K., Contrepois, K., Kruger, S., Burgess, J., Stevenson, D., Aghaeepour, N., Snyder, M., Zhang, E., Badillo, K., Silver, R., Einerson, B. D., Bianco, K. 2025; 145 (6): 721-731

  Abstract

  To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical characteristics with metabolomic and lipidomic profiles.This was a multicenter case-control study of patients with placenta previa with PAS (case group n=33) and previa alone (control group n=21). Maternal third-trimester plasma samples were collected and stored at -80°C. Untargeted metabolomic and targeted lipidomic assays were measured with flow-injection mass spectrometry. Univariate analysis provided an association of each lipid or metabolite with the outcome. The Benjamini-Hochberg procedure was used to control for the false discovery rate. Elastic net machine learning models were trained on patient characteristics to predict risk, and an integrated elastic net model of lipidome or metabolome with nine clinical features was trained. Performance using the area under the receiver operating characteristic curve (AUC) was determined with Monte Carlo cross-validation. Statistical significance was defined at P<.05.The mean gestational age at sample collection was 33 3/7 weeks (case group) and 35 5/7 weeks (control group) (P<.01). In total, 786 lipid species and 2,605 metabolite features were evaluated. Univariate analysis revealed 31 lipids and 214 metabolites associated with the outcome (P<.05). After false discovery rate adjustment, these associations no longer remained statistically significant. When the machine learning model was applied, prediction of PAS with only clinical characteristics (AUC 0.685, 95% CI, 0.65-0.72) performed similarly to prediction with the lipidome model (AUC 0.699, 95% CI, 0.60-0.80) and the metabolome model (AUC 0.71, 95% CI, 0.66-0.76). However, integration of metabolome and lipidome with clinical features did not improve the model.Metabolomic and lipidomic profiling performed similarly to, and not better than, clinical risk factors using machine learning to predict PAS among patients with PAS with previa and previa alone.

  View details for DOI 10.1097/AOG.0000000000005922

  View details for PubMedID 40373320

• StrokeCog-15 Is an Efficient Neuropsychological Battery to Screen for Cognitive Impairment in Chronic Stroke. Stroke Drag, L. L., Mlynash, M., Aslan, A., Musabbir, M., Bradley, A., Lansberg, M. G., Allan, S. M., Aghaeepour, N., Smith, C., Buckwalter, M. S. 2025

  Abstract

  Poststroke cognitive impairment can significantly impact functional outcomes and quality of life. While comprehensive neuropsychological evaluations are valuable in characterizing this impairment, their time-intensive nature is not always feasible. Thus, we set out to develop a brief cognitive battery that is sensitive to poststroke cognitive impairment.Neuropsychological testing was completed in a validation sample of 126 participants with chronic ischemic stroke (median days since stroke, 337 [interquartile range, 235-1057]) as part of StrokeCog, a prospective observational cohort study. This comprehensive 60-minute cognitive battery contained 9 tests covering 5 cognitive domains. A partial least square regression analysis informed the selection of a brief, 15-minute battery of 4 tests (StrokeCog-15) covering 4 cognitive domains: language, memory, working memory, and processing speed/executive functioning. We then compared StrokeCog-15 with Montreal Cognitive Assessment and an established 30-minute battery in its ability to detect cognitive impairment as identified by the comprehensive battery. Finally, we assessed the utility of StrokeCog-15 in an external validation sample of 61 participants (median days since stroke, 210 [interquartile range, 193-230]) enrolled in the parallel Stroke-IMPaCT study.Cognitive impairment was common, occurring in 50% (n=61) and 66% (n=40) of the 2 cohorts. Deficits occurred most frequently in the memory and processing speed/executive functioning domains. In the derivation sample, StrokeCog-15 demonstrated high sensitivity (0.97) and adequate specificity (0.78) in detecting cognitive impairment on the comprehensive battery, outperforming both Montreal Cognitive Assessment (sensitivity, 0.77; specificity, 0.73) and the 30-minute battery (sensitivity, 0.97; specificity, 0.35). StrokeCog-15 similarly demonstrated high sensitivity (0.93) and adequate specificity (0.67) in the validation sample.A brief 15-minute battery of tests has high sensitivity to detect cognitive impairment as identified on a longer neuropsychological test battery. StrokeCog-15 assesses multiple cognitive domains commonly impacted by stroke and represents an efficient yet effective means to identify chronic poststroke cognitive impairment.

  View details for DOI 10.1161/STROKEAHA.124.049217

  View details for PubMedID 40406877

• Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials? The Lancet. Digital health Pammi, M., Shah, P. S., Yang, L. K., Hagan, J., Aghaeepour, N., Neu, J. 2025: 100851

  Abstract

  Randomised controlled trials are the gold standard to assess the effectiveness and safety of clinical interventions; however, many paediatric trials are discontinued early due to challenges in patient enrolment. Hence, most paediatric clinical trials suffer from lack of adequate power. Additionally, trials are expensive and might expose patients to unproven therapies. Alternatives to overcome these issues using virtual patient data-namely, digital twins, synthetic patient data, and in-silico trials-are now possible due to rapid advances in digital health-care tools and interventions. However, such digital innovations have been rarely used in paediatric trials. In this Viewpoint, we propose using virtual patient data to empower paediatric trials. The use of virtual patient data has the advantages of decreased exposure of children to potentially ineffective or risky interventions, shorter trial durations leading to more rapid ascertainment of safety and effectiveness of interventions, and faster drug approvals. Use of virtual patient data could lead to more personalised treatment options with low costs and could result in faster clinical implementation of interventions in children. However, ethical and regulatory concerns, including replacing humans with digital data, data privacy, and security should be addressed and the safety and sustainability of digital data innovation ensured before virtual patient data are adopted widely.

  View details for DOI 10.1016/j.landig.2025.01.007

  View details for PubMedID 40360351

• A Foundation Model for Perioperative Outcome Prediction Han, L., Aghaeepour, N. LIPPINCOTT WILLIAMS & WILKINS. 2025: 1160-1161

  View details for Web of Science ID 001551889100442

• Modification of Post-operative Inflammation Through Infusion of a Young Donor Plasma Fraction in Older Patients Tsai, A., Xue, L., Gao, X., McAllister, T., Tingle, M., Porras, G., Feinstein, I., Feyaerts, D., Verdonk, F., Sabayev, M., Hedou, J., Ganio, E., Berson, E., Becker, M., Espinosa, C., Kim, Y., Lehallier, B., Rawner, E., Feng, C., Amanatullah, D., Huddleston, J., Goodman, S., Aghaeepour, N., Gaudilliere, B., Angst, M. LIPPINCOTT WILLIAMS & WILKINS. 2025: 965-967

  View details for Web of Science ID 001551889100369

• Parkinson's disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction. NPJ Parkinson's disease Wilson, E. N., Umans, J., Swarovski, M. S., Minhas, P. S., Mendiola, J. H., Midttun, Ø., Ulvik, A., Shahid-Besanti, M., Linortner, P., Mhatre, S. D., Wang, Q., Channappa, D., Corso, N. K., Tian, L., Fredericks, C. A., Kerchner, G. A., Plowey, E. D., Cholerton, B., Ueland, P. M., Zabetian, C. P., Gray, N. E., Quinn, J. F., Montine, T. J., Sha, S. J., Longo, F. M., Wolk, D. A., Chen-Plotkin, A., Henderson, V. W., Wyss-Coray, T., Wagner, A. D., Mormino, E. C., Aghaeepour, N., Poston, K. L., Andreasson, K. I. 2025; 11 (1): 96

  Abstract

  Recent studies demonstrate that Parkinson's disease (PD) is associated with dysregulated metabolic flux through the kynurenine pathway (KP), in which tryptophan is converted to kynurenine (KYN), and KYN is subsequently metabolized to neuroactive compounds quinolinic acid (QA) and kynurenic acid (KA). Here, we used mass-spectrometry to compare blood and cerebral spinal fluid (CSF) KP metabolites between 158 unimpaired older adults and 177 participants with PD. We found increased neuroexcitatory QA/KA ratio in both plasma and CSF of PD participants associated with peripheral and cerebral inflammation and vitamin B6 deficiency. Furthermore, increased QA tracked with CSF tau, CSF soluble TREM2 (sTREM2) and severity of both motor and non-motor PD clinical symptoms. Finally, PD patient subgroups with distinct KP profiles displayed distinct PD clinical features. These data validate the KP as a site of brain and periphery crosstalk, integrating B-vitamin status, inflammation and metabolism to ultimately influence PD clinical manifestation.

  View details for DOI 10.1038/s41531-025-00964-7

  View details for PubMedID 40287426

  View details for PubMedCentralID PMC12033312

• Author Correction: AI-guided precision parenteral nutrition for neonatal intensive care units. Nature medicine Phongpreecha, T., Ghanem, M., Reiss, J. D., Oskotsky, T. T., Mataraso, S. J., De Francesco, D., Reincke, S. M., Espinosa, C., Chung, P., Ng, T., Costello, J. M., Sequoia, J. A., Razdan, S., Xie, F., Berson, E., Kim, Y., Seong, D., Szeto, M. Y., Myers, F., Gu, H., Feister, J., Verscaj, C. P., Rose, L. A., Sin, L. W., Oskotsky, B., Roger, J., Shu, C. H., Shome, S., Yang, L. K., Tan, Y., Levitte, S., Wong, R. J., Gaudillière, B., Angst, M. S., Montine, T. J., Kerner, J. A., Keller, R. L., Shaw, G. M., Sylvester, K. G., Fuerch, J., Chock, V., Gaskari, S., Stevenson, D. K., Sirota, M., Prince, L. S., Aghaeepour, N. 2025

  View details for DOI 10.1038/s41591-025-03691-x

  View details for PubMedID 40205201

• A call to action to address escalating global threats to academic research INNOVATION Piret, G., Fung, F., Fullerton, J., Fico, G., Ponkratov, D., Chen, W., Latorre, D., Wan, K. Y., Aghaeepour, N., Welgryn, J., Razi, A., Silveyra, P., Altun, A., Jurkowska, R. Z., Hughes, A. C., Wolfram, J. 2025; 6 (4)

  View details for DOI 10.1016/j.xinn.2024.100758

  View details for Web of Science ID 001469394400001

• A call to action to address escalating global threats to academic research. Innovation (Cambridge (Mass.)) Piret, G., Fung, F. M., Fullerton, J., Fico, G., Ponkratov, D., Chen, W., Latorre, D., Wan, K. Y., Aghaeepour, N., Welgryn, J., Razi, A., Silveyra, P., Altun, A., Jurkowska, R. Z., Hughes, A. C., Wolfram, J. 2025; 6 (4): 100758

  Abstract

  This article is a call to action to address escalating threats to scientific progress that affect academic researchers across the globe. These threats include public mistrust of science, challenges in translating academic research to end-user applications, a disconnect between academics and policymakers, emerging barriers to international collaboration, and a reliance on conventional metrics to evaluate academic performance. This article presents various calls to action informed by exemplary approaches across the globe that serve as frameworks to drive beneficial transformation for researchers, academic institutions, and society.

  View details for DOI 10.1016/j.xinn.2024.100758

  View details for PubMedID 40470320

  View details for PubMedCentralID PMC12131030

• A Foundation Model for Perioperative Outcome Prediction Han, L., Aghaeepour, N. LIPPINCOTT WILLIAMS & WILKINS. 2025: 46

  View details for Web of Science ID 001588241900023

• Cellular immune changes during severe antisense oligonucleotide-associated thrombocytopenia in a nonhuman primate model. Journal of immunology (Baltimore, Md. : 1950) Gupta, S., Shen, L., Henry, S. P., Aghaeepour, N., Narayanan, P., Maecker, H. T. 2025

  Abstract

  Antisense oligonucleotides (ASOs) are a new class of single-stranded DNA-based drugs that hold great therapeutic potential. A low incidence of severe, dose-dependent, and reversible thrombocytopenia (TCP) (platelets < 50 K/μl) has been reported in nonhuman primate (NHP) populations, following treatment of monkeys with 2'-O-methoxy ethyl ASOs (2% to 4% at doses > 8 to 10 mg/kg/week). The potential mechanisms for this effect were studied using the Mauritian-sourced NHPs, which were shown to be more susceptible to ASO-induced TCP than Asian-sourced animals. In this pilot study, we used a mass cytometry-based intracellular cytokine staining assay, to evaluate the immune-phenotypic and functional changes in cryopreserved PBMCs, collected over 8 time points of ASO therapy (ISIS 405879) from 12 Cambodian and 12 Mauritian monkeys (9 treated and 3 controls). Unsupervised clustering was performed across markers used for cell type identification in the pooled dataset, followed by unsupervised comparison at each time point and then longitudinal analysis. Major immune cell types showed differential abundance between the 2 groups prior to start of ASO therapy. These included IFNg- and TNF-producing polyfunctional effector T cells (CD4+ and CD8+), which were lower, and MIP1b-producing monocytes and DCs, which were higher, in the Mauritian monkeys. Immune populations also changed over the course of this treatment, wherein IL-17- and GM-CSF-producing T cells and IgM-producing B cells increased markedly in Mauritians. Identification of these differentially abundant immune cell subsets in treatment sensitive NHPs could help decipher potential immune mechanisms contributing to severe TCP observed during administration of specific ASO sequences in humans.

  View details for DOI 10.1093/jimmun/vkae055

  View details for PubMedID 40101742

• Mitigation of outcome conflation in predicting patient outcomes using electronic health records. Journal of the American Medical Informatics Association : JAMIA Reincke, S. M., Espinosa, C., Chung, P., James, T., Berson, E., Aghaeepour, N. 2025

  Abstract

  OBJECTIVES: Artificial intelligence (AI) models utilizing electronic health record data for disease prediction can enhance risk stratification but may lack specificity, which is crucial for reducing the economic and psychological burdens associated with false positives. This study aims to evaluate the impact of confounders on the specificity of single-outcome prediction models and assess the effectiveness of a multi-class architecture in mitigating outcome conflation.MATERIALS AND METHODS: We evaluated a state-of-the-art model predicting pancreatic cancer from disease code sequences in an independent cohort of 2.3 million patients and compared this single-outcome model with a multi-class model designed to predict multiple cancer types simultaneously. Additionally, we conducted a clinical simulation experiment to investigate the impact of confounders on the specificity of single-outcome prediction models.RESULTS: While we were able to independently validate the pancreatic cancer prediction model, we found that its prediction scores were also correlated with ovarian cancer, suggesting conflation of outcomes due to underlying confounders. Building on this observation, we demonstrate that the specificity of single-outcome prediction models is impaired by confounders using a clinical simulation experiment. Introducing a multi-class architecture improves specificity in predicting cancer types compared to the single-outcome model while preserving performance, mitigating the conflation of outcomes in both the real-world and simulated contexts.DISCUSSION: Our results highlight the risk of outcome conflation in single-outcome AI prediction models and demonstrate the effectiveness of a multi-class approach in mitigating this issue.CONCLUSION: The number of predicted outcomes needs to be carefully considered when employing AI disease risk prediction models.

  View details for DOI 10.1093/jamia/ocaf033

  View details for PubMedID 40056434

• The human milk microbiome varies by environmental factors and is differentially associated with infant growth: findings from the IMiC Study Manus, M., Fehr, K., Shu, C., Mertens, A., DeBoer, M., McDermid, J., Kolsteren, P., Dailey-Chwalibog, T., Toe, L., Shafiq, Y., Jehan, F., Muhammad, A., Aghaeepour, N., Shenhav, L., Azad, M. WILEY. 2025: 105

  View details for Web of Science ID 001446236400439

• Mediterranean vs. Western diet effects on the primate cerebral cortical pre-synaptic proteome: Relationships with the transcriptome and multi-system phenotypes. Alzheimer's & dementia : the journal of the Alzheimer's Association Berson, E., Frye, B. M., Gajera, C. R., Saarunya, G., Perna, A., Phongpreecha, T., Shome, S., Negrey, J. D., Aghaeepour, N., Montine, T. J., Craft, S., Register, T. C., Shively, C. A. 2025; 21 (3): e70041

  Abstract

  Diet quality mediates aging-related risks of cognitive decline, neurodegeneration, and Alzheimer's disease (AD) through poorly defined mechanisms.The effects of diet on the presynaptic proteome of the temporal cortex were assessed in 36 female cynomolgus macaques randomized to Mediterranean or Western diets for 31 months. Associations between the presynaptic proteome, determined by synaptometry by time-of-flight (SynTOF) mass spectrometry, adjacent cortex transcriptome, and multi-system phenotypes were assessed using a machine learning approach.Six presynaptic proteins (DAT, Aβ42, calreticulin, LC3B, K48-Ubiquitin, SLC6A8) were elevated in the presynaptic proteome in Mediterranean diet consumers (p < 0.05). Transcriptomic data and multi-system phenotypes significantly predicted SynTOF markers. Selected SynTOF markers were correlated with changes in white matter volumes, hepatosteatosis, and behavioral and physiological measures of psychosocial stress.These observations demonstrate that diet composition drives cortical presynaptic protein composition, that transcriptional profiles strongly predict the presynaptic proteomic profile, and that presynaptic proteins were closely associated with peripheral metabolism, stress responsivity, neuroanatomy, and socio-emotional behavior.Mediterranean and Western diets differentially altered the cortical presynaptic proteome, which is strongly associated with neurodegeneration and cognitive decline. Presynaptic proteomic markers were predicted by transcriptomic profiles in the adjacent cortex, and by multi-system anatomical, physiologic, and behavioral phenotypes. The data demonstrate that brain phenotypes and brain-body interactions are influenced by common dietary patterns, suggesting that improving diet quality may be an effective means to maintain brain health.

  View details for DOI 10.1002/alz.70041

  View details for PubMedID 40109017

  View details for PubMedCentralID PMC11923382

• Reducing Barriers to AI Deployment in Pathology: An Image Source-Agnostic AI Pipeline for Quantifying HER2 Amplification Usman, M., Tokuyama, M., Bean, G., Dussaq, A., Montine, T., Phongpreecha, T., Aghaeepour, N., Yang, E. ELSEVIER SCIENCE INC. 2025

  View details for DOI 10.1016/j.labinv.2024.103817

  View details for Web of Science ID 001463073100019

• Persistence of a Proteomic Signature After a Hypertensive Disorder of Pregnancy. Hypertension (Dallas, Tex. : 1979) Hlatky, M. A., Shu, C. H., Stevenson, D. K., Shaw, G. M., Stefanick, M. L., Boyd, H. A., Melbye, M., Plummer, X. D., Sedan, O., Wong, R. J., Aghaeepour, N., Winn, V. D. 2025

  Abstract

  A hypertensive disorder of pregnancy is associated with a higher risk of cardiovascular disease later in life, but the potential mechanistic links are unknown.We recruited 2 groups of women, 1 during pregnancy and another at least 2 years after delivery. Cases had a hypertensive disorder of pregnancy, and controls had a normotensive pregnancy. The pregnancy cohort had study visits antepartum and postpartum; the mid-life group made a single study visit. We assayed 7228 plasma proteins, applied machine learning to identify proteomics signatures at each time point, and performed enrichment analyses to identify relevant biological pathways.The pregnancy cohort (58 cases and 46 controls) had a mean age of 33.8 years, and the mid-life group (71 cases and 74 controls) had a mean age of 40.8 years. Protein levels differed significantly between cases and controls at each time point: 6233 antepartum, 189 postpartum, and 224 in mid-life. The postpartum protein signature discriminated well between cases and controls (c-index=0.78), and it also discriminated well in the independent mid-life samples (c-index=0.72). Pathway analyses identified differences in the complement and coagulation cascades that persisted across the antepartum, postpartum, and mid-life samples. The 28 proteins present in both the postpartum and mid-life signatures included 5 complement factors (3, B, H, H-related-1, and C1r-subcomponent-like) and coagulation factor IX.Differences in protein expression persist for years after a hypertensive disorder of pregnancy. The consistent differences in the complement and coagulation pathways may contribute to the increased risk of later life cardiovascular disease.

  View details for DOI 10.1161/HYPERTENSIONAHA.124.24490

  View details for PubMedID 39981573

• Exposure to autoimmune disorders is associated with increased Alzheimer's disease risk in a multi-site electronic health record analysis. Cell reports. Medicine Ramey, G. D., Tang, A., Phongpreecha, T., Yang, M. M., Woldemariam, S. R., Oskotsky, T. T., Montine, T. J., Allen, I., Miller, Z. A., Aghaeepour, N., Capra, J. A., Sirota, M. 2025: 101980

  Abstract

  Autoimmunity has been proposed to increase Alzheimer's disease (AD) risk, but evaluating the clinical connection between autoimmune disorders and AD has been difficult in diverse populations. We investigate risk relationships between 26 autoimmune disorders and AD using retrospective observational case-control and cohort study designs based on electronic health records for >300,000 individuals at the University of California, San Francisco (UCSF) and Stanford University. We discover that autoimmune disorders are associated with increased AD risk (odds ratios [ORs] 1.4-1.7) across study designs, primarily driven by endocrine, gastrointestinal, dermatologic, and musculoskeletal disorders. We also find that autoimmune disorders associate with increased AD risk in both sexes, but the AD sex disparity remains in those with autoimmune disorders: women exhibit higher AD prevalence than men. This study identifies consistent associations between autoimmune disorders and AD across study designs and two real-world clinical databases, establishing a foundation for exploring how autoimmunity may contribute to AD risk.

  View details for DOI 10.1016/j.xcrm.2025.101980

  View details for PubMedID 39999839

• Infusion of young donor plasma components in older patients modifies the immune and inflammatory response to surgical tissue injury: a randomized clinical trial. Journal of translational medicine Gaudilliere, B., Xue, L., Tsai, A. S., Gao, X., McAllister, T. N., Tingle, M., Porras, G., Feinstein, I., Feyaerts, D., Verdonk, F., Sabayev, M., Hedou, J., Ganio, E. A., Berson, E., Becker, M., Espinosa, C., Kim, Y., Lehallier, B., Rawner, E., Feng, C., Amanatullah, D. F., Huddleston, J. I., Goodman, S. B., Aghaeepour, N., Angst, M. S. 2025; 23 (1): 183

  Abstract

  Preclinical evidence suggests that young plasma has beneficial effects on multiple organ systems in aged mice. Whether young plasma exerts beneficial effects in an aging human population remains highly controversial. Despite lacking data, young donor plasma infusions have been promoted for age-related conditions. Given the preclinical evidence that young plasma exerts beneficial effects by attenuating inflammation, this study examined whether administering a young plasma protein fraction to an elderly population would exert anti-inflammatory and immune modulating effects in humans, using surgery as a tissue injury model.This double-blind, placebo-controlled study enrolled and randomized 38 patients undergoing major joint replacement surgery. Patients received four separate infusions of a plasma protein fraction derived from young donors, or placebo one day before surgery, before and after surgery on the day of surgery, and one day after surgery. Blood specimens for proteomic and immunological analyses were collected before each infusion. Based on the high-content assessment of circulating plasma proteins with single-cell analyses of peripheral immune cells, proteomic signatures and cell-type-specific signaling responses that separated the treatment groups were derived with regression models.Elastic net regression models revealed that administration a young plasma protein fraction significantly altered the proteomic (AUC = 0.796, p = 0.002) and the cellular immune response (AUC 0.904, p < 0.001) to surgical trauma resulting in signaling pathway- and cell type-specific anti-inflammatory immune modulation. Affected proteomic pathways regulating inflammation included JAK-STAT, NF-kappa B, and MAPK (p < 0.001). These findings were confirmed at the cellular level as the MAPK and JAK/STAT signaling responses were diminished and IkB, the negative regulator of NFkB, was elevated in adaptive immune cells.Reported findings provide a first proof of principle in humans that a young plasma protein fraction actively regulates inflammatory and immune responses in an elderly population. They provide a solid rationale for elucidating active principles in young plasma that may be of therapeutic benefits for a range of age-related pathologies.ClinicalTrials.gov, NCT03981419.

  View details for DOI 10.1186/s12967-025-06215-w

  View details for PubMedID 39953524

  View details for PubMedCentralID 6764071

• PregMedNet: Multifaceted Maternal Medication Impacts on Neonatal Complications. medRxiv : the preprint server for health sciences Kim, Y., Marić, I., Kashiwagi, C. M., Han, L., Chung, P., Reiss, J. D., Butcher, L. D., Caoili, K. J., Berson, E., Xue, L., Espinosa, C., James, T., Shome, S., Xie, F., Ghanem, M., Seong, D., Chang, A. L., Reincke, S. M., Mataraso, S., Shu, C. H., Francesco, D. D., Becker, M., Kumar, W. M., Wong, R., Gaudilliere, B., Angst, M. S., Shaw, G. M., Bateman, B. T., Stevenson, D. K., Prince, L. S., Aghaeepour, N. 2025

  Abstract

  While medication intake is common among pregnant women, medication safety remains underexplored, leading to unclear guidance for patients and healthcare professionals. PregMedNet addresses this gap by providing a multifaceted maternal medication safety framework based on systematic analysis of 1.19 million mother-baby dyads from U.S. claims databases. A novel confounding adjustment pipeline was applied to systematically control confounders for multiple medication-disease pairs, robustly identifying both known and novel maternal medication effects. Notably, one of the newly discovered associations was experimentally validated, demonstrating the reliability of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms of newly identified associations were generated using a graph learning method. These findings highlight PregMedNet's value in promoting safer medication use during pregnancy and maternal-neonatal outcomes.

  View details for DOI 10.1101/2025.02.13.25322242

  View details for PubMedID 39990567

  View details for PubMedCentralID PMC11844599

• Innovations in acute and chronic pain biomarkers: enhancing diagnosis and personalized therapy. Regional anesthesia and pain medicine Mackey, S., Aghaeepour, N., Gaudilliere, B., Kao, M. C., Kaptan, M., Lannon, E., Pfyffer, D., Weber, K. 2025; 50 (2): 110-120

  Abstract

  Pain affects millions worldwide, posing significant challenges in diagnosis and treatment. Despite advances in understanding pain mechanisms, there remains a critical need for validated biomarkers to enhance diagnosis, prognostication, and personalized therapy. This review synthesizes recent advancements in identifying and validating acute and chronic pain biomarkers, including imaging, molecular, sensory, and neurophysiological approaches. We emphasize the emergence of composite, multimodal strategies that integrate psychosocial factors to improve the precision and applicability of biomarkers in chronic pain management. Neuroimaging techniques like MRI and positron emission tomography provide insights into structural and functional abnormalities related to pain, while electrophysiological methods like electroencepholography and magnetoencepholography assess dysfunctional processing in the pain neuroaxis. Molecular biomarkers, including cytokines, proteomics, and metabolites, offer diagnostic and prognostic potential, though extensive validation is needed. Integrating these biomarkers with psychosocial factors into clinical practice can revolutionize pain management by enabling personalized treatment strategies, improving patient outcomes, and potentially reducing healthcare costs. Future directions include the development of composite biomarker signatures, advances in artificial intelligence, and biomarker signature integration into clinical decision support systems. Rigorous validation and standardization efforts are also necessary to ensure these biomarkers are clinically useful. Large-scale collaborative research will be vital to driving progress in this field and implementing these biomarkers in clinical practice. This comprehensive review highlights the potential of biomarkers to transform acute and chronic pain management, offering hope for improved diagnosis, treatment personalization, and patient outcomes.

  View details for DOI 10.1136/rapm-2024-106030

  View details for PubMedID 39909549

• Association between gestational weight gain and adverse pregnancy outcomes: cohort analysis from South Asia and Sub-Saharan Africa. BMJ public health Sebastião, Y. V., Thiruvengadam, R., Khanam, R., Mehmood, U., Pervin, J., , Desiraju, B. K., Kabole, F., Ahmed, S., Aktar, S., Chowdhury, N. H., Qazi, M. F., Nisar, I., Khalid, J., Kasaro, M., Vwalika, B., Khan, W., Nu, U. T., Rahman, M., Rahman, S., Shaw, G. M., Stevenson, D. K., Xu, H., Bakari, B. A., Wadhwa, N., Zhang, G., Sazawal, S., Aghaeepour, N., Rahman, A., Jehan, F., Baqui, A. H., Stringer, J. S., Bhatnagar, S. 2025; 3 (1): e000900

  Abstract

  Studies of gestational weight gain (GWG) and adverse pregnancy outcomes seldom focus on low-to-middle-income countries (LMICs), despite their high burden of morbidity and mortality. We examined GWG patterns and adverse pregnancy outcomes in a consortium of pregnancy cohorts from LMICs.We analysed data from five observational pregnancy cohorts in Bangladesh (two cohorts), India, Pakistan and Zambia. The study population comprised 15 286 singleton pregnancies with two or more maternal antenatal weight measurements. We estimated reference values for GWG using longitudinal models and calculated weight gain for gestational age Z-scores. We then estimated the associated risks of preterm birth, low birth weight, and small for gestational age, stratified by maternal body mass index (BMI), using marginal generalised linear models and plotted non-linear trends in the associations.The median baseline maternal and gestational age were 24 years (IQR, 21-28) and 13 weeks (IQR 11-16), respectively, with 23% of participants having underweight BMI. The median GWG was 6.8 kg (4.2-9.4) and varied across cohorts from 6.1 kg (3.7-8.5; Bangladesh) to 7.0 kg (4.0-10.0; Zambia). The risk of preterm birth (13%) increased with lower GWG Z-scores among underweight (adjusted risk ratio (ARR), 1.4; 95% CI, 1.1 to 1.9 for lowest Z-score group) and normal BMI participants (ARR, 1.1; 95% CI, 1.0 to 1.2). The risk of low birth weight (25%) increased with lower GWG Z-scores in all BMI strata except obese participants (ARR, 1.7; 95% CI 1.5 to 1.9 among underweight). The risk of small for gestational age (36%) increased with lower GWG Z-scores in all BMI strata (ARR, 1.3; 95% CI 1.2 to 1.4 among underweight). In secondary analyses, alternative measures of GWG (adequacy ratio; INTERGROWTH-21st) had associations that were consistent with those from our study-specific Z-scores, except for a less clear association between preterm birth and INTERGROWTH-21st Z-score.GWG was associated with preterm birth, low birth weight and small for gestational age. Early pregnancy BMI modified the association between GWG and outcomes in the study setting.

  View details for DOI 10.1136/bmjph-2024-000900

  View details for PubMedID 40433067

  View details for PubMedCentralID PMC12107451

• Information Extraction from Clinical Texts with Generative Pre-trained Transformer Models. International journal of medical sciences Kim, M. S., Chung, P., Aghaeepour, N., Kim, N. 2025; 22 (5): 1015-1028

  Abstract

  Purpose: Processing and analyzing clinical texts are challenging due to its unstructured nature. This study compared the performance of GPT (Generative Pre-trained Transformer)-3.5 and GPT-4 for extracting information from clinical text. Materials and Methods: Three types of clinical texts, containing patient characteristics, medical history, and clinical test results extracted from case reports in open-access journals were utilized as input. Simple prompts containing queries for information extraction were then applied to both models using the Greedy Approach as the decoding strategy. When GPT models underperformed in certain tasks, we applied alternative decoding strategies or incorporated prompts with task-specific definitions. The outputs generated by GPT models were evaluated as True or False to determine the accuracy of information extraction. Results: Clinical texts containing patient characteristics (60 texts), medical history (50 texts), and clinical test results (25 texts) were extracted from 60 case reports. GPT models could extract information accurately with simple prompts to extract straightforward information from clinical texts. Regarding sex, GPT-4 demonstrated a significantly higher accuracy rate (95%) compared to GPT-3.5 (70%). GPT-3.5 (78%) outperformed GPT-4 (57%) in extracting body mass index (BMI). Utilizing alternative decoding strategies to sex and BMI did not practically improve the performance of the two models. In GPT-4, the revised prompts, including definitions of each sex category or the BMI formula, rectified all incorrect responses regarding sex and BMI generated during the main workflow. Conclusion: GPT models could perform adequately with simple prompts for extracting straightforward information. For complex tasks, incorporating task-specific definitions into the prompts is a suitable strategy than relying solely on simple prompts. Therefore, researchers and clinicians should use their expertise to create effective prompts and monitor LLM outcomes when extracting complex information from clinical texts.

  View details for DOI 10.7150/ijms.103332

  View details for PubMedID 40027192

  View details for PubMedCentralID PMC11866537

• Advancing neonatal health: the promise and challenges of universal genome sequencing in newborn screening. Pediatric research Stevenson, D. K., Wong, R. J., Reiss, J. D., Shaw, G. M., Aghaeepour, N., Mahzarnia, A., Marić, I. 2025

  View details for DOI 10.1038/s41390-025-03874-9

  View details for PubMedID 39833347

  View details for PubMedCentralID 9326622

• A machine learning approach to leveraging electronic health records for enhanced omics analysis. Nature machine intelligence Mataraso, S. J., Espinosa, C. A., Seong, D., Reincke, S. M., Berson, E., Reiss, J. D., Kim, Y., Ghanem, M., Shu, C. H., James, T., Tan, Y., Shome, S., Stelzer, I. A., Feyaerts, D., Wong, R. J., Shaw, G. M., Angst, M. S., Gaudilliere, B., Stevenson, D. K., Aghaeepour, N. 2025; 7 (2): 293-306

  Abstract

  Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electronic health record data and adaptively blending both early and late fusion strategies, COMET overcomes the limitations of existing multimodal machine learning methods. Using two independent datasets, we showed that COMET improved the predictive modelling performance and biological discovery compared with the analysis of omics data with traditional methods. By incorporating electronic health record data into omics analyses, COMET enables more precise patient classifications, beyond the simplistic binary reduction to cases and controls. This framework can be broadly applied to the analysis of multimodal omics studies and reveals more powerful biological insights from limited cohort sizes.

  View details for DOI 10.1038/s42256-024-00974-9

  View details for PubMedID 40008295

  View details for PubMedCentralID PMC11847705

• A machine learning approach to leveraging electronic health records for enhanced omics analysis NATURE MACHINE INTELLIGENCE Mataraso, S. J., Espinosa, C. A., Seong, D., Reincke, S., Berson, E., Reiss, J. D., Kim, Y., Ghanem, M., Shu, C., James, T., Tan, Y., Shome, S., Stelzer, I. A., Feyaerts, D., Wong, R. J., Shaw, G. M., Angst, M. S., Gaudilliere, B., Stevenson, D. K., Aghaeepour, N. 2025

  View details for DOI 10.1038/s42256-024-00974-9

  View details for Web of Science ID 001397087200001

• Impact of digital health interventions on glycemic control and weight management. NPJ digital medicine Veluvali, A., Dehghani Zahedani, A., Hosseinian, A., Aghaeepour, N., McLaughlin, T., Woodward, M., DiTullio, A., Hashemi, N., Snyder, M. P. 2025; 8 (1): 20

  Abstract

  This retrospective cohort study evaluates the impact of an AI-supported continuous glucose monitoring (CGM) mobile app ("January V2") on glycemic control and weight management in 944 users, including healthy individuals and those with prediabetes or type 2 diabetes (T2D). The app, leveraging AI to personalize feedback, tracked users' food intake, activity, and glucose responses over 14 days. Significant improvements in time in range (TIR) were observed, particularly in users with lower baseline TIR. Healthy users' TIR increased from 74.7% to 85.5% (p<0.0001), while T2D users' TIR improved from 49.7% to 57.4% (p<0.0004). Higher app engagement correlated with greater TIR improvements. Users also experienced an average weight reduction of 3.3 lbs over 33 days. These findings suggest that AI-enhanced digital health interventions can improve glycemic control and promote weight loss, particularly when users are actively engaged.

  View details for DOI 10.1038/s41746-025-01430-7

  View details for PubMedID 39789102

• Transgenerational associations between newborn metabolic profiles and bronchopulmonary dysplasia in neonates born to mothers with an obese phenotype. Scientific reports Reiss, J. D., Yang, W., Chang, A. L., Long, J. Z., Marić, I., Profit, J., Sylvester, K. G., Stevenson, D. K., Aghaeepour, N., Shaw, G. M. 2025; 15 (1): 1144

  Abstract

  Maternal obesity increases risk for bronchopulmonary dysplasia (BPD) by up to 42%. Identifying metabolic features that may contribute to the association between maternal pre-pregnancy body mass index (BMI) and BPD is critical in defining the molecular relationship between these conditions. We investigated the association between maternal obesity and BPD using newborn screen metabolites as an explanatory variable. We hypothesized that elevated pre-pregnancy BMI compared to a normal BMI referent group, is associated with increased circulating short and long-chain acylcarnitines and subsequent development of BPD. This was a retrospective study with linkage of maternal pre-pregnancy BMI, with newborn screen metabolites obtained from the California Newborn Screening Program and further linked with neonatal outcomes. Results demonstrated elevated levels of phenylalanine and proline associated with an increased risk for BPD (OR 5.3, 95% CI 1.2-23.8 and OR 5.4, 95% CI 1.3-22.3) in the obesity group compared to the referent group. Short- and long-chain acylcarnitines demonstrated a mildly increased risk for BPD in neonates of mothers with severe obesity compared to controls. The findings suggest that specific metabolites may influence the molecular conditioning that increases susceptibility to BPD.

  View details for DOI 10.1038/s41598-025-85252-3

  View details for PubMedID 39774255

  View details for PubMedCentralID 9523142

• LIPIDOMIC ALTERATIONS AT BIRTH AND PREDISPOSITION TO BRONCHOPULMONARY DYSPLASIA Reiss, J., Phongpreecha, T., Trowbridge, C., Michael, B., Lancaster, S., Kasowski, M., Snyder, M., Maric, I., Stevenson, D. K., Aghaeepour, N., Sylvester, K., Shaw, G. M. SAGE PUBLICATIONS LTD. 2025: 75-76

  View details for Web of Science ID 001430200800057

• Multiomics Profiling and Predictive Modeling of Necrotizing Enterocolitis Ektare, S., Espinosa, C., Chang, A., Reiss, J., Wong, R. J., Gaudilliere, B., Shaw, G. M., Sylvester, K., Angst, M. S., Stevenson, D., Aghaeepour, N. SAGE PUBLICATIONS LTD. 2025: 240-241

  View details for Web of Science ID 001430200800197

• Prediction of late-onset preeclampsia using plasma proteomics: a longitudinal multi-cohort study. Scientific reports Andresen, I. J., Zucknick, M., Degnes, M. L., Angst, M. S., Aghaeepour, N., Romero, R., Roland, M. C., Tarca, A. L., Westerberg, A. C., Michelsen, T. M. 2024; 14 (1): 30813

  Abstract

  Preeclampsia is a pregnancy disorder with substantial perinatal and maternal morbidity and mortality. Pregnant women at risk of preeclampsia would benefit from early detection for follow-up, timely interventions and delivery. Several attempts have been made to identify protein biomarkers of preeclampsia, but findings vary with demographics, clinical characteristics, and time of sampling. In the current study, we combined three independent longitudinal pregnancy cohorts (Detroit, Stanford and Oslo) resulting in 124 late-onset preeclampsia (LOPE) cases and 178 gestational age matched controls, and analyzed > 1000 proteins in maternal plasma sampled between 12 and 34 weeks of gestation. Differential abundance analysis of combined protein data revealed increased deviation in protein abundance trajectories throughout gestation in women destined to develop LOPE compared to controls. There were no differentially abundant proteins at time interval T1 (12-19 weeks), yet 31 differentially abundant proteins were found at time interval T2 (19-27 weeks), and 48 proteins at time interval T3 (27- 34 weeks). Multi-protein random forest models assessed via cross-validation predicted LOPE with an area under the ROC curve of 0.72 (0.65-0.78), 0.76 (0.71-0.81) and 0.80 (0.75-0.85) at time interval T1, T2 and T3, respectively. The results at T3 were confirmed using a leave-one-cohort-out analysis suggesting cross-cohort consistency, and at T1 and T2 when the largest two cohorts were used as training sets.

  View details for DOI 10.1038/s41598-024-81277-2

  View details for PubMedID 39730472

  View details for PubMedCentralID PMC11681054

• Basic Science and Pathogenesis. Alzheimer's & dementia : the journal of the Alzheimer's Association Berson, E., Frye, B. M., Perna, A., Phongpreecha, T., Shome, S., Clarke, G., Negrey, J. D., Aghaeepour, N., Montine, T. J., Craft, S., Register, T. C., Shively, C. A. 2024; 20 Suppl 1: e089274

  Abstract

  Western and Mediterranean diets differentially affect cerebral cortical gene expression, brain structure, and socioemotional behavior in middle-aged female nonhuman primates (NHP) (Macaca fascicularis). In this study, we investigate the effect of diet on brain molecular composition.Using a machine learning approach, we quantified the impact of these diets on the presynaptic proteome in the lateral temporal cortex determined by synaptometry by time of flight (SynTOF) mass spectrometry and examined associations between the proteome, transcriptome, and an array of multisystem phenotypes. For this, we consider NHP fed with Mediterranean (n = 17) or Western (n = 19) diet for 31 months before brain retrieval (see study overview in Figure 1).Diet has a significant effect on presynaptic proteins (AUC = 0.86, Pvalue = 0.0002) (Figure 2A). We identified six presynaptic proteins (DAT, Aβ42, calreticulin, LC3B, K48-Ubiquitin, SLC6A8) elevated in the presynaptic proteome bythe Mediterranean compared to the Western diet (p<0.05) (Figure 2B). Interestingly, we demonstrated that transcriptomic data from adjacent cortex predict all the SynTOF markers (pvalue <0.05) (Figure 2C). We found that the SPATA22 transcript was positively correlated with five SynTOF markers (LRRK2, TMEM230, GAMT and 3NT, Aβ40) (all p<0.05). Transcription Factor AP-2 Gamma transcript (TFAP2C) was positively correlated with SynTOF markers pTau, CD47, and GAD65. Together, the multi-system phenotypes significantly predicted 26 SynTOF markers, the strongest relationships were between GFAP and brain volumetrics (Figure 2D). Numerous SynTOF markers correlated with hepatosteatosis, suggesting the relationships between liver health and presynapses composition. SynTOF markers were also associated with behavioral and physiological measures of social-environmental stress.Together these observations demonstrate that diet composition drives temporal presynaptic protein composition, that transcription profiles strongly predict the presynaptic proteomic profile, and that presynaptic proteins are closely associated with peripheral metabolism, stress responsivity, and socioemotional behavior. These data demonstrate the impact of diet composition on brain molecular composition.

  View details for DOI 10.1002/alz.089274

  View details for PubMedID 39751106

• Basic Science and Pathogenesis. Alzheimer's & dementia : the journal of the Alzheimer's Association Berson, E., Perna, A., Phongpreecha, T., Aghaeepour, N., Montine, T. J. 2024; 20 Suppl 1: e087057

  Abstract

  Single nucleus RNA sequencing (snRNA-seq) has revolutionized our ability to dissect transcriptional profiles in specific cell types. While nuclear sequencing enhances analysis robustness, it captures only 20-50% of the cellular transcriptional information, limiting our comprehensive understanding of the cellular transcriptional ensemble. Therefore, we propose a computational approach to extract the cellular signal from bulk transcriptomic data from brain tissue, allowing us to investigate cell type-specific transcriptomic programs underlying neurodegeneration.We adapted Cellformer - a deep learning deconvolution model for ATAC-seq data - to RNA-seq data. We leverage an excitotoxicity mouse model to detect cell-type specific transcriptomic responses to injury.Cellformer accurately deconvoluted mouse brain bulk RNA into 9 major cell types (mean Pearson correlation of 0.97) (Figure 1A). Validation with single nucleus datasets reveals a significantly higher correlation (0.85) within the same cell type compared to different cell types (0.20) (P-value < 1e-6) (Figure 1B). We applied Cellformer to bulk RNAseq data obtained from both tissue and nuclei isolated from the hippocampus of the same mouse. We compared these cell-type-specific transcriptomic signatures between healthy mice and those exposed to a low dose of Kainic Acid (KA), a potent toxin for excitatory neurons (Figure 1C). More shared information was found between snRNA and deconvoluted RNA from nuclei compared to deconvoluted tissue (Figure 1D). Interestingly, differential expression analysis revealed a greater effect of low-dose KA exposure on deconvoluted bulk tissue compared to nuclei, pinpointing synaptic and lysosomal signaling to excitatory neuronal cells (Figure 1E-F).In this study, we introduce a computational approach utilizing the Cellformer algorithm to deconvolute bulk RNA sequencing data into cell-type-specific profiles, enabling in-depth analysis of bulk RNAseq datasets. We demonstrate Cellformer's proficiency in recovering cell-type-specific RNA expression patterns. By comparing deconvoluted profiles between healthy and injured hippocampi, we unveil insights previously masked by the limitations of snRNA-seq, revealing intricate synaptic signaling dynamics. Cellformer offers the unprecedented ability to investigate extranuclear signaling in neurodegeneration.

  View details for DOI 10.1002/alz.087057

  View details for PubMedID 39750950

• Postpartum sleep quality and physical activity profiles following elective cesarean delivery: a longitudinal prospective cohort pilot study utilizing a wearable actigraphy device. International journal of obstetric anesthesia Pandal, P., Carvalho, B., Shu, C. H., Ciechanowicz, S., O'Carroll, J., Aghaeepour, N., Fowler, C., Simons, L. E., Druzin, M. L., Panelli, D. M., Sultan, P. 2024; 62: 104305

  Abstract

  While sleep and activity levels are impacted by childbirth, these changes before and after cesarean delivery are under explored. Few studies have characterized sleep and physical activity before and after cesarean delivery using objective measures. The aim of this study was to characterize sleep and activity before and after cesarean delivery using wrist-worn Actigraphy. Secondary aims were to explore associations between physical activity and sleep following scheduled cesarean delivery.Following IRB approval, ASA 2 and 3 patients aged 18-50 years, term gestation, singleton pregnancy, undergoing scheduled cesarean delivery under neuraxial anesthesia were invited to participate. Consented patients continuously wore an Actigraph GT9X device on their non-dominant wrist from 7 days prior to scheduled cesarean delivery until 28 days post-delivery. Sleep metrics included quality, duration, disruption and efficiency. Physical activity metrics included average daily moderate to vigorous physical activity bouts, metabolic equivalents (METs) and caloric expenditure. Granular data regarding sleep and activity were recorded and analyzed based on established algorithms and trend analysis using methodology previously described.Among the 38 recruited patients, analyzable actigraphy data were available in 21 patients. Trend analysis from day -7 (pre-delivery) to 28 (post-delivery) demonstrated that most variables did not differ significantly, indicating that at month 1, most activity and sleeping variables returned to third trimester levels. Some metrics of sleep improved in the first week postpartum compared to third trimester, however, total sleep time worsened and did not recover by day 28 compared to the third trimester durations. Physical activity levels dropped significantly immediately after delivery, then improved from day 0 to 28 post-surgery.Most sleep and physical activity metrics return to third trimester levels by 1 month postpartum. Several sleep metrics such as sleep efficiency and awakening after sleep were better in the first postpartum week than in the third trimester of pregnancy, but total sleep continues to be significantly impacted at day 28 postpartum. Physical activity returns to third trimester levels by one month postpartum. Future studies are needed to identify risk factors for worse physical recovery and sleep following cesarean delivery and to compare metrics following different peripartum complications.

  View details for DOI 10.1016/j.ijoa.2024.104305

  View details for PubMedID 40023061

• Impact of air pollution exposure on cytokines and histone modification profiles at single-cell levels during pregnancy. Science advances Jung, Y. S., Aguilera, J., Kaushik, A., Ha, J. W., Cansdale, S., Yang, E., Ahmed, R., Lurmann, F., Lutzker, L., Hammond, S. K., Balmes, J., Noth, E., Burt, T. D., Aghaeepour, N., Waldrop, A. R., Khatri, P., Utz, P. J., Rosenburg-Hasson, Y., DeKruyff, R., Maecker, H. T., Johnson, M. M., Nadeau, K. C. 2024; 10 (48): eadp5227

  Abstract

  Fine particulate matter (PM2.5) exposure can induce immune system pathology via epigenetic modification, affecting pregnancy outcomes. Our study investigated the association between PM2.5 exposure and immune response, as well as epigenetic changes using high-dimensional epigenetic landscape profiling using cytometry by time-of-flight (EpiTOF) at the single cell. We found statistically significant associations between PM2.5 exposure and levels of certain cytokines [interleukin-1RA (IL-1RA), IL-8/CXCL8, IL-18, and IL-27)] and histone posttranslational modifications (HPTMs) in immune cells (HPTMs: H3K9ac, H3K23ac, H3K27ac, H2BK120ub, H4K20me1/3, and H3K9me1/2) among pregnant and nonpregnant women. The cord blood of neonates with high maternal PM2.5 exposure showed lower IL-27 than those with low exposure. Furthermore, PM2.5 exposure affects the co-modification profiles of cytokines between pregnant women and their neonates, along with HPTMs in each immune cell type between pregnant and nonpregnant women. These modifications in specific histones and cytokines could indicate the toxicological mechanism of PM2.5 exposure in inflammation, inflammasome pathway, and pregnancy complications.

  View details for DOI 10.1126/sciadv.adp5227

  View details for PubMedID 39612334

• Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling. BMC pregnancy and childbirth Zhang, Y., Sylvester, K. G., Wong, R. J., Blumenfeld, Y. J., Hwa, K. Y., Chou, C. J., Thyparambil, S., Liao, W., Han, Z., Schilling, J., Jin, B., Marić, I., Aghaeepour, N., Angst, M. S., Gaudilliere, B., Winn, V. D., Shaw, G. M., Tian, L., Luo, R. Y., Darmstadt, G. L., Cohen, H. J., Stevenson, D. K., McElhinney, D. B., Ling, X. B. 2024; 24 (1): 783

  Abstract

  Preterm birth (PTB) is a serious health problem. PTB complications is the main cause of death in infants under five years of age worldwide. The ability to accurately predict risk for PTB during early pregnancy would allow early monitoring and interventions to provide personalized care, and hence improve outcomes for the mother and infant.This study aims to predict the risks of early preterm (< 35 weeks of gestation) or very early preterm (≤ 26 weeks of gestation) deliveries by using high-resolution maternal urinary metabolomic profiling in early pregnancy.A retrospective cohort study was conducted by two independent preterm and term cohorts using high-density weekly urine sampling. Maternal urine was collected serially at gestational weeks 8 to 24. Global metabolomics approaches were used to profile urine samples with high-resolution mass spectrometry. The significant features associated with preterm outcomes were selected by Gini Importance. Metabolite biomarker identification was performed by liquid chromatography tandem mass spectrometry (LCMS-MS). XGBoost models were developed to predict early or very early preterm delivery risk.The urine samples included 329 samples from 30 subjects at Stanford University, CA for model development, and 156 samples from 24 subjects at the University of Alabama, Birmingham, AL for validation.12 metabolites associated with PTB were selected and identified for modelling among 7,913 metabolic features in serial-collected urine samples of pregnant women. The model to predict early PTB was developed using a set of 12 metabolites that resulted in the area under the receiver operating characteristic (AUROCs) of 0.995 (95% CI: [0.992, 0.995]) and 0.964 (95% CI: [0.937, 0.964]), and sensitivities of 100% and 97.4% during development and validation testing, respectively. Using the same metabolites, the very early PTB prediction model achieved AUROCs of 0.950 (95% CI: [0.878, 0.950]) and 0.830 (95% CI: [0.687, 0.826]), and sensitivities of 95.0% and 60.0% during development and validation, respectively.Models for predicting risk of early or very early preterm deliveries were developed and tested using metabolic profiling during the 1st and 2nd trimesters of pregnancy. With patient validation studies, risk prediction models may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights of preterm birth.

  View details for DOI 10.1186/s12884-024-06974-2

  View details for PubMedID 39587571

  View details for PubMedCentralID PMC11587579

• Towards a new taxonomy of preterm birth. Journal of perinatology : official journal of the California Perinatal Association Stevenson, D. K., Chang, A. L., Wong, R. J., Reiss, J. D., Gaudillière, B., Sylvester, K. G., Ling, X. B., Angst, M. S., Shaw, G. M., Katz, M., Aghaeepour, N., Marić, I. 2024

  Abstract

  Disease categories traditionally reflect a historical clustering of clinical phenotypes based on biologic and nonbiologic features. Multiomics approaches have striven to identify signatures to develop individualized categorizations through tests and/or therapies for 'personalized' medicine. Precision health classifies clinical syndromes into endotype clusters based on novel technological advancements, which can reveal insights into the etiologies of phenotypical syndromes. A new taxonomy of preterm birth should be considered in this context, as not all preterm infants of similar gestational ages are the same because most have different biologic vulnerabilities and hence different health trajectories. Even the choice of interventions may affect observed clinical conditions. Thus, a new taxonomy of prematurity would help to advance the field of neonatology, but also obstetrics and perinatology by adopting anticipatory and more targeted approaches to the care of preterm infants with the intent of preventing and treating some of the most common newborn pathologic conditions.

  View details for DOI 10.1038/s41372-024-02183-z

  View details for PubMedID 39567650

  View details for PubMedCentralID 10028490

• Towards a new taxonomy of preterm birth Journal of Perinatology Stevenson, D. K., Chang, A. L., Wong, R. J., Reiss, J. D., Gaudillière, B., Sylvester, K. G., Ling, X. B., Angst, M. S., Shaw, G. M., Katz, M., Aghaeepour, N., Marić , I. 2024

  Abstract

  Disease categories traditionally reflect a historical clustering of clinical phenotypes based on biologic and nonbiologic features. Multiomics approaches have striven to identify signatures to develop individualized categorizations through tests and/or therapies for 'personalized' medicine. Precision health classifies clinical syndromes into endotype clusters based on novel technological advancements, which can reveal insights into the etiologies of phenotypical syndromes. A new taxonomy of preterm birth should be considered in this context, as not all preterm infants of similar gestational ages are the same because most have different biologic vulnerabilities and hence different health trajectories. Even the choice of interventions may affect observed clinical conditions. Thus, a new taxonomy of prematurity would help to advance the field of neonatology, but also obstetrics and perinatology by adopting anticipatory and more targeted approaches to the care of preterm infants with the intent of preventing and treating some of the most common newborn pathologic conditions.

  View details for DOI 10.1038/s41372-024-02183-z

  View details for PubMedCentralID 10028490

• Corrigendum to "Mode of delivery predicts postpartum maternal leukocyte telomere length" [Eur. J. Obstetr. Gynecol. Reprod. Biol. 300 (2024) 224-229]. European journal of obstetrics, gynecology, and reproductive biology Panelli, D. M., Mayo, J. A., Wong, R. J., Becker, M., Feyaerts, D., Marić, I., Wu, E., Gotlib, I. H., Gaudillière, B., Aghaeepour, N., Druzin, M. L., Stevenson, D. K., Shaw, G. M., Bianco, K. 2024; 304: 35

  View details for DOI 10.1016/j.ejogrb.2024.11.017

  View details for PubMedID 39561615

• An immune signature of postoperative cognitive decline: A prospective cohort study. International journal of surgery (London, England) Verdonk, F., Cambriel, A., Hedou, J., Ganio, E., Bellan, G., Gaudilliere, D., Einhaus, J., Sabayev, M., Stelzer, I. A., Feyaerts, D., Bonham, A. T., Ando, K., Choisy, B., Drover, D., Heifets, B., Chretien, F., Aghaeepour, N., Angst, M. S., Molliex, S., Sharshar, T., Gaillard, R., Gaudilliere, B. 2024

  Abstract

  Postoperative cognitive decline (POCD) is the predominant complication affecting patients over 60 years old following major surgery, yet its prediction and prevention remain challenging. Understanding the biological processes underlying the pathogenesis of POCD is essential for identifying mechanistic biomarkers to advance diagnostics and therapeutics. This study aimed to provide a comprehensive analysis of immune cell trajectories differentiating patients with and without POCD and to derive a predictive score enabling the identification of high-risk patients during the preoperative period.Twenty-six patients aged 60 years old and older undergoing elective major orthopedic surgery were enrolled in a prospective longitudinal study, and the occurrence of POCD was assessed seven days after surgery. Serial samples collected before surgery, and one, seven, and 90 days after surgery were analyzed using a combined single-cell mass cytometry and plasma proteomic approach. Unsupervised clustering of the high-dimensional mass cytometry data was employed to characterize time-dependent trajectories of all major innate and adaptive immune cell frequencies and signaling responses. Sparse machine learning coupled with data-driven feature selection was applied to the pre-surgery immunological dataset to classify patients at risk for POCD.The analysis identified cell-type and signaling-specific immune trajectories differentiating patients with and without POCD. The most prominent trajectory features revealed early exacerbation of JAK/STAT and dampening of inhibitory κB and nuclear factor-κB immune signaling responses in patients with POCD. Further analyses integrating immunological and clinical data collected before surgery identified a preoperative predictive model comprising one plasma protein and ten immune cell features that classified patients at risk for POCD with excellent accuracy (AUC=0.80, P=2.21e-02 U-test).Immune system-wide monitoring of patients over 60 years old undergoing surgery unveiled a peripheral immune signature of POCD. A predictive model built on immunological data collected before surgery demonstrated greater accuracy in predicting POCD compared to known clinical preoperative risk factors, offering a concise list of biomarker candidates to personalize perioperative management.

  View details for DOI 10.1097/JS9.0000000000002118

  View details for PubMedID 39411891

• Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning. Pediatric research Shu, C. H., Zebda, R., Espinosa, C., Reiss, J., Debuyserie, A., Reber, K., Aghaeepour, N., Pammi, M. 2024

  Abstract

  Predicting mortality and specific morbidities before they occur may allow for interventions that may improve health trajectories.Integrating key maternal and postnatal infant variables in the first 2 weeks of age into machine learning (ML) algorithms will reliably predict survival and specific morbidities in VLBW preterm infants.ML algorithms were developed to integrate 47 features for predicting mortality, bronchopulmonary dysplasia (BPD), neonatal sepsis, necrotizing enterocolitis (NEC), intraventricular hemorrhage (IVH), cystic periventricular leukomalacia (PVL), and retinopathy of prematurity (ROP). A retrospective cohort (n = 3341) was used to train and validate the models with a repeated 10-fold cross-validation strategy. These models were then tested on a separate cohort (n = 447) to evaluate the final model performance.Among the seven ML algorithms employed, tree-based ensemble models, specifically Random Forest (RF) and XGBoost, had the best performance metrics. The area under the receiver operating characteristic curve (AUROC) of sepsis with or without meningitis (0.73), NEC (0.73), BPD (0.71), and mortality (0.74) exceeded 0.7, while the area under Precision-Recall curve (AUPRC) for all outcomes was greater than the prevalence, demonstrating effective risk stratification in VLBW preterm infants.Our study demonstrates the potential of predictive analytics leveraging ML techniques in advancing precision medicine.Reliable prediction of adverse outcomes before they occur has the potential to institute interventions and possibly improve health trajectories in VLBW preterm infants. We used machine learning to develop and test predictive models for mortality and five major morbidities in VLBW preterm infants. Individualized prediction of outcomes and individualized interventions will advance Precision Medicine in Neonatology.

  View details for DOI 10.1038/s41390-024-03604-7

  View details for PubMedID 39379627

  View details for PubMedCentralID 5139808

• VMAP: Vaginal Microbiome Atlas during Pregnancy. JAMIA open Parraga-Leo, A., Oskotsky, T. T., Oskotsky, B., Wibrand, C., Roldan, A., Tang, A. S., Ha, C. W., Wong, R. J., Minot, S. S., Andreoletti, G., Kosti, I., Theis, K. R., Ng, S., Lee, Y. S., Diaz-Gimeno, P., Bennett, P. R., MacIntyre, D. A., Lynch, S. V., Romero, R., Tarca, A. L., Stevenson, D. K., Aghaeepour, N., Golob, J. L., Sirota, M. 2024; 7 (3): ooae099

  Abstract

  To enable interactive visualization of the vaginal microbiome across the pregnancy and facilitate discovery of novel insights and generation of new hypotheses.Vaginal Microbiome Atlas during Pregnancy (VMAP) was created with R shiny to generate visualizations of structured vaginal microbiome data from multiple studies.VMAP (http://vmapapp.org) visualizes 3880 vaginal microbiome samples of 1402 pregnant individuals from 11 studies, aggregated via open-source tool MaLiAmPi. Visualized features include diversity measures, VALENCIA community state types, and composition (phylotypes, taxonomy) that can be filtered by various categories.This work represents one of the largest and most geographically diverse aggregations of the vaginal microbiome in pregnancy to date and serves as a user-friendly resource to further analyze vaginal microbiome data and better understand pregnancies and associated outcomes.VMAP can be obtained from https://github.com/msirota/vmap.git and is currently deployed as an online app for non-R users.

  View details for DOI 10.1093/jamiaopen/ooae099

  View details for PubMedID 39345789

  View details for PubMedCentralID PMC11430916

• Parkinson's disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction. Research square Wilson, E., Umans, J., Swarovski, M., Minhas, P., Midttun, Ø., Ulvik, A. A., Shahid-Besanti, M., Linortner, P., Mhatre, S., Wang, Q., Channappa, D., Corso, N., Tian, L., Fredericks, C., Kerchner, G., Plowey, E., Cholerton, B., Ueland, P., Zabetian, C., Gray, N., Quinn, J., Montine, T., Sha, S., Longo, F., Wolk, D., Chen-Plotkin, A., Henderson, V., Wyss-Coray, T., Wagner, A., Mormino, E., Aghaeepour, N., Poston, K., Andreasson, K. 2024

  Abstract

  Parkinson's disease (PD) is a complex multisystem disorder clinically characterized by motor, non-motor, and premotor manifestations. Pathologically, PD involves neuronal loss in the substantia nigra, striatal dopamine deficiency, and accumulation of intracellular inclusions containing aggregates of α-synuclein. Recent studies demonstrate that PD is associated with dysregulated metabolic flux through the kynurenine pathway (KP), in which tryptophan is converted to kynurenine (KYN), and KYN is subsequently metabolized to neuroactive compounds quinolinic acid (QA) and kynurenic acid (KA). This multicenter study used highly sensitive liquid chromatography-tandem mass-spectrometry to compare blood and cerebral spinal fluid (CSF) KP metabolites between 158 unimpaired older adults and 177 participants with PD. Results indicate that increased neuroexcitatory QA/KA ratio in both plasma and CSF of PD participants associated with peripheral and cerebral inflammation and vitamin B6 deficiency. Furthermore, increased QA tracked with CSF tau and severity of both motor and non-motor PD clinical dysfunction. Importantly, plasma and CSF kynurenine metabolites classified PD participants with a high degree of accuracy (AUC = 0.897). Finally, analysis of metabolite data revealed subgroups with distinct KP profiles, and these were subsequently found to display distinct PD clinical features. Together, these data further support the hypothesis that the KP serves as a site of brain and periphery crosstalk, integrating B-vitamin status, inflammation and metabolism to ultimately influence PD clinical manifestation.

  View details for DOI 10.21203/rs.3.rs-4980210/v1

  View details for PubMedID 39399688

  View details for PubMedCentralID PMC11469709

• Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models. Briefings in bioinformatics Seong, D., Mataraso, S., Espinosa, C., Berson, E., Reincke, S. M., Xue, L., Kashiwagi, C., Kim, Y., Shu, C. H., Chung, P., Ghanem, M., Xie, F., Wong, R. J., Angst, M. S., Gaudilliere, B., Shaw, G. M., Stevenson, D. K., Aghaeepour, N. 2024; 25 (6)

  Abstract

  Translational biology posits a strong bi-directional link between clinical phenotypes and a patient's biological profile. By leveraging this bi-directional link, we can efficiently deconvolute pre-existing clinical information into biological profiles. However, traditional computational tools are limited in their ability to resolve this link because of the relatively small sizes of paired clinical-biological datasets for training and the high dimensionality/sparsity of tabular clinical data. Here, we use state-of-the-art foundation models (FMs) for electronic health record (EHR) data to generate proteomics profiles of pregnant patients, thereby deconvoluting pre-existing clinical information into biological profiles without the cost and effort of running large-scale traditional omics studies. We show that FM-derived representations of a patient's EHR data coupled with a fully connected neural network prediction head can generate 206 blood protein expression levels. Interestingly, these proteins were enriched for developmental pathways, while proteins not able to be generated from EHR data were enriched for metabolic pathways. Finally, we show a proteomic signature of gestational diabetes that includes proteins with established and novel links to gestational diabetes. These results showcase the power of FM-derived EHR representations in efficiently generating biological states of pregnant patients. This capability can revolutionize disease understanding and therapeutic development, offering a cost-effective, time-efficient, and less invasive alternative to traditional methods of generating proteomics.

  View details for DOI 10.1093/bib/bbae574

  View details for PubMedID 39545787

  View details for PubMedCentralID PMC11565587

• Factor Eight Inhibitor Bypass Activity use in cardiac surgery: A propensity matched analysis of safety outcomes. Anesthesiology Nicholas, J. A., Harrison, N., Chakraborty, D., Chang, A. L., Aghaeepour, N., Wirtz, K., Nielson, E., Parsons, C., Jackson, E., Panigrahi, A. K. 2024

  Abstract

  Bleeding during cardiac surgery may be refractory to standard interventions. Off-label use of Factor Eight Inhibitor Bypass Activity (FEIBA) has been described to treat such bleeding. However, reports of safety, particularly thromboembolic outcomes, show mixed results and reported cohorts have been small.Adult patients undergoing cardiac surgery on cardiopulmonary bypass between July 1, 2018 and June 30, 2023 at Stanford Hospital were reviewed (n=3335). Patients who received FEIBA to treat post-cardiopulmonary bypass bleeding were matched with those who did not by propensity scores in a 1:1 ratio using nearest neighbor matching (n= 352 per group). The primary outcome was a composite outcome of thromboembolic complications including any one of deep vein thrombosis (DVT), pulmonary embolism (PE), unplanned coronary artery intervention, ischemic stroke, and acute limb ischemia, in the postoperative period. Secondary outcomes included renal failure, reoperation, postoperative transfusion, ICU length of stay (LOS), and 30-day mortality.704 encounters were included in our propensity matched analysis. The mean dose of FEIBA administered was 7.3 ±5.5 units/kg. In propensity matched multivariate logistic regression models there was no statistically significant difference in odds ratios for thromboembolic outcomes, ICU LOS, or mortality. Patients who received >750 units of FEIBA had an increased odds ratio for acute renal failure (OR 4.14; 95% CI 1.61 to 10.36, p <0.001). In multivariate linear regression, patients receiving FEIBA were transfused more plasma and cryoprecipitate postoperatively. However, only the dose range of 501-750 units was associated with an increase in transfusion of RBCs (β 2.73; 95% CI 0.68 to 4.78; p=0.009), and platelets (β 1.74; 95% CI 0.85 to 2.63; p <0.001).Low dose FEIBA administration during cardiac surgery does not increase risk of thromboembolic events, ICU LOS, or mortality in a propensity matched cohort. Higher doses were associated with increased acute renal failure and postoperative transfusion. Further studies are required to establish the efficacy of activated factor concentrates to treat refractory bleeding during cardiac surgery.

  View details for DOI 10.1097/ALN.0000000000005208

  View details for PubMedID 39186670

• Single-cell peripheral immunoprofiling of lewy body and Parkinson's disease in a multi-site cohort. Molecular neurodegeneration Phongpreecha, T., Mathi, K., Cholerton, B., Fox, E. J., Sigal, N., Espinosa, C., Reincke, M., Chung, P., Hwang, L. J., Gajera, C. R., Berson, E., Perna, A., Xie, F., Shu, C. H., Hazra, D., Channappa, D., Dunn, J. E., Kipp, L. B., Poston, K. L., Montine, K. S., Maecker, H. T., Aghaeepour, N., Montine, T. J. 2024; 19 (1): 59

  Abstract

  Multiple lines of evidence support peripheral organs in the initiation or progression of Lewy body disease (LBD), a spectrum of neurodegenerative diagnoses that include Parkinson's Disease (PD) without or with dementia (PDD) and dementia with Lewy bodies (DLB). However, the potential contribution of the peripheral immune response to LBD remains unclear. This study aims to characterize peripheral immune responses unique to participants with LBD at single-cell resolution to highlight potential biomarkers and increase mechanistic understanding of LBD pathogenesis in humans.In a case-control study, peripheral mononuclear cell (PBMC) samples from research participants were randomly sampled from multiple sites across the United States. The diagnosis groups comprise healthy controls (HC, n = 159), LBD (n = 110), Alzheimer's disease dementia (ADD, n = 97), other neurodegenerative disease controls (NDC, n = 19), and immune disease controls (IDC, n = 14). PBMCs were activated with three stimulants (LPS, IL-6, and IFNa) or remained at basal state, stained by 13 surface markers and 7 intracellular signal markers, and analyzed by flow cytometry, which generated 1,184 immune features after gating.The model classified LBD from HC with an AUROC of 0.87 ± 0.06 and AUPRC of 0.80 ± 0.06. Without retraining, the same model was able to distinguish LBD from ADD, NDC, and IDC. Model predictions were driven by pPLCγ2, p38, and pSTAT5 signals from specific cell populations under specific activation. The immune responses characteristic for LBD were not associated with other common medical conditions related to the risk of LBD or dementia, such as sleep disorders, hypertension, or diabetes.Quantification of PBMC immune response from multisite research participants yielded a unique pattern for LBD compared to HC, multiple related neurodegenerative diseases, and autoimmune diseases thereby highlighting potential biomarkers and mechanisms of disease.

  View details for DOI 10.1186/s13024-024-00748-2

  View details for PubMedID 39090623

  View details for PubMedCentralID 9739123

• Unlocking human immune system complexity through AI. Nature methods Berson, E., Chung, P., Espinosa, C., Montine, T. J., Aghaeepour, N. 2024; 21 (8): 1400-1402

  View details for DOI 10.1038/s41592-024-02351-1

  View details for PubMedID 39122943

  View details for PubMedCentralID 9586871

• Artificial Intelligence in Perioperative Care: Opportunities and Challenges. Anesthesiology Han, L., Char, D. S., Aghaeepour, N. 2024; 141 (2): 379-387

  View details for DOI 10.1097/ALN.0000000000005013

  View details for PubMedID 38980160

• Association of pregnancy complications and postpartum maternal leukocyte telomeres in two diverse cohorts: a nested case-control study. BMC pregnancy and childbirth Panelli, D. M., Wang, X., Mayo, J., Wong, R. J., Hong, X., Becker, M., Aghaeepour, N., Druzin, M. L., Zuckerman, B. S., Stevenson, D. K., Shaw DrPH, G. M., Bianco, K. 2024; 24 (1): 490

  Abstract

  Biologic strain such as oxidative stress has been associated with short leukocyte telomere length (LTL), as well as with preeclampsia and spontaneous preterm birth, yet little is known about their relationships with each other. We investigated associations of postpartum maternal LTL with preeclampsia and spontaneous preterm birth.This pilot nested case control study included independent cohorts of pregnant people with singleton gestations from two academic institutions: Cohort 1 (hereafter referred to as Suburban) were enrolled prior to 20 weeks' gestation between 2012 and 2018; and Cohort 2 (hereafter referred to as Urban) were enrolled at delivery between 2000 and 2012. Spontaneous preterm birth or preeclampsia were the selected pregnancy complications and served as cases. Cases were compared with controls from each study cohort of uncomplicated term births. Blood was collected between postpartum day 1 and up to 6 months postpartum and samples were frozen, then simultaneously thawed for analysis. Postpartum LTL was the primary outcome, measured using quantitative polymerase chain reaction (PCR) and compared using linear multivariable regression models adjusting for maternal age. Secondary analyses were done stratified by mode of delivery and self-reported level of stress during pregnancy.156 people were included; 66 from the Suburban Cohort and 90 from the Urban Cohort. The Suburban Cohort was predominantly White, Hispanic, higher income and the Urban Cohort was predominantly Black, Haitian, and lower income. We found a trend towards shorter LTLs among people with preeclampsia in the Urban Cohort (6517 versus 6913 bp, p = 0.07), but not in the Suburban Cohort. There were no significant differences in LTLs among people with spontaneous preterm birth compared to term controls in the Suburban Cohort (6044 versus 6144 bp, p = 0.64) or in the Urban Cohort (6717 versus 6913, p = 0.37). No differences were noted by mode of delivery. When stratifying by stress levels in the Urban Cohort, preeclampsia was associated with shorter postpartum LTLs in people with moderate stress levels (p = 0.02).Our exploratory results compare postpartum maternal LTLs between cases with preeclampsia or spontaneous preterm birth and controls in two distinct cohorts. These pilot data contribute to emerging literature on LTLs in pregnancy.

  View details for DOI 10.1186/s12884-024-06688-5

  View details for PubMedID 39033276

  View details for PubMedCentralID 5967638

• Mode of delivery predicts postpartum maternal leukocyte telomere length. European journal of obstetrics, gynecology, and reproductive biology Panelli, D. M., Mayo, J. A., Wong, R. J., Becker, M., Feyaerts, D., Marić, I., Wu, E., Gotlib, I. H., Gaudillière, B., Aghaeepour, N., Druzin, M. L., Stevenson, D. K., Shaw, G. M., Bianco, K. 2024; 300: 224-229

  Abstract

  Recent studies have suggested that pregnancy accelerates biologic aging, yet little is known about how biomarkers of aging are affected by events during the peripartum period. Given that immune shifts are known to occur following surgery, we explored the relation between mode of delivery and postpartum maternal leukocyte telomere length (LTL), a marker of biologic aging.Postpartum maternal blood samples were obtained from a prospective cohort of term, singleton livebirths without hypertensive disorders or peripartum infections between 2012 and 2018. The primary outcome was postpartum LTLs from one blood sample drawn between postpartum week 1 and up to 6 months postpartum, measured from thawed frozen peripheral blood mononuclear cells using quantitative PCR in basepairs (bp). Multivariable linear regression models compared LTLs between vaginal versus cesarean births, adjusting for age, body mass index, and nulliparity as potential confounders. Analyses were conducted in two mutually exclusive groups: those with LTL measured postpartum week 1 and those measured up to 6 months postpartum. Secondarily, we compared multiomics by mode of delivery using machine-learning methods to evaluate whether other biologic changes occurred following cesarean. These included transcriptomics, metabolomics, microbiomics, immunomics, and proteomics (serum and plasma).Of 67 included people, 50 (74.6 %) had vaginal and 17 (25.4 %) had cesarean births. LTLs were significantly shorter after cesarean in postpartum week 1 (5755.2 bp cesarean versus 6267.8 bp vaginal, p = 0.01) as well as in the later draws (5586.6 versus 5945.6 bp, p = 0.04). After adjusting for confounders, these differences persisted in both week 1 (adjusted beta -496.1, 95 % confidence interval [CI] -891.1, -101.1, p = 0.01) and beyond (adjusted beta -396.8; 95 % CI -727.2, -66.4. p = 0.02). Among the 15 participants who also had complete postpartum multiomics data available, there were predictive signatures of vaginal versus cesarean births in transcriptomics (cell-free [cf]RNA), metabolomics, microbiomics, and proteomics that did not persist after false discovery correction.Maternal LTLs in postpartum week 1 were nearly 500 bp shorter following cesarean. This difference persisted several weeks postpartum, even though other markers of inflammation had normalized. Mode of delivery should be considered in any analyses of postpartum LTLs and further investigation into this phenomenon is warranted.

  View details for DOI 10.1016/j.ejogrb.2024.07.026

  View details for PubMedID 39032311

• Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication. JAMA surgery Chung, P., Fong, C. T., Walters, A. M., Aghaeepour, N., Yetisgen, M., O'Reilly-Shah, V. N. 2024

  Abstract

  General-domain large language models may be able to perform risk stratification and predict postoperative outcome measures using a description of the procedure and a patient's electronic health record notes.To examine predictive performance on 8 different tasks: prediction of American Society of Anesthesiologists Physical Status (ASA-PS), hospital admission, intensive care unit (ICU) admission, unplanned admission, hospital mortality, postanesthesia care unit (PACU) phase 1 duration, hospital duration, and ICU duration.This prognostic study included task-specific datasets constructed from 2 years of retrospective electronic health records data collected during routine clinical care. Case and note data were formatted into prompts and given to the large language model GPT-4 Turbo (OpenAI) to generate a prediction and explanation. The setting included a quaternary care center comprising 3 academic hospitals and affiliated clinics in a single metropolitan area. Patients who had a surgery or procedure with anesthesia and at least 1 clinician-written note filed in the electronic health record before surgery were included in the study. Data were analyzed from November to December 2023.Compared original notes, note summaries, few-shot prompting, and chain-of-thought prompting strategies.F1 score for binary and categorical outcomes. Mean absolute error for numerical duration outcomes.Study results were measured on task-specific datasets, each with 1000 cases with the exception of unplanned admission, which had 949 cases, and hospital mortality, which had 576 cases. The best results for each task included an F1 score of 0.50 (95% CI, 0.47-0.53) for ASA-PS, 0.64 (95% CI, 0.61-0.67) for hospital admission, 0.81 (95% CI, 0.78-0.83) for ICU admission, 0.61 (95% CI, 0.58-0.64) for unplanned admission, and 0.86 (95% CI, 0.83-0.89) for hospital mortality prediction. Performance on duration prediction tasks was universally poor across all prompt strategies for which the large language model achieved a mean absolute error of 49 minutes (95% CI, 46-51 minutes) for PACU phase 1 duration, 4.5 days (95% CI, 4.2-5.0 days) for hospital duration, and 1.1 days (95% CI, 0.9-1.3 days) for ICU duration prediction.Current general-domain large language models may assist clinicians in perioperative risk stratification on classification tasks but are inadequate for numerical duration predictions. Their ability to produce high-quality natural language explanations for the predictions may make them useful tools in clinical workflows and may be complementary to traditional risk prediction models.

  View details for DOI 10.1001/jamasurg.2024.1621

  View details for PubMedID 38837145

• Predicting Preterm Birth Using Proteomics. Clinics in perinatology Marić, I., Stevenson, D. K., Aghaeepour, N., Gaudillière, B., Wong, R. J., Angst, M. S. 2024; 51 (2): 391-409

  Abstract

  The complexity of preterm birth (PTB), both spontaneous and medically indicated, and its various etiologies and associated risk factors pose a significant challenge for developing tools to accurately predict risk. This review focuses on the discovery of proteomics signatures that might be useful for predicting spontaneous PTB or preeclampsia, which often results in PTB. We describe methods for proteomics analyses, proteomics biomarker candidates that have so far been identified, obstacles for discovering biomarkers that are sufficiently accurate for clinical use, and the derivation of composite signatures including clinical parameters to increase predictive power.

  View details for DOI 10.1016/j.clp.2024.02.011

  View details for PubMedID 38705648

• Computational Approaches for Predicting Preterm Birth and Newborn Outcomes. Clinics in perinatology Seong, D., Espinosa, C., Aghaeepour, N. 2024; 51 (2): 461-473

  Abstract

  Preterm birth (PTB) and its associated morbidities are a leading cause of infant mortality and morbidity. Accurate predictive models and a better biological understanding of PTB-associated morbidities are critical in reducing their adverse effects. Increasing availability of multimodal high-dimensional data sets with concurrent advances in artificial intelligence (AI) have created a rich opportunity to gain novel insights into PTB, a clinically complex and multifactorial disease. Here, the authors review the use of AI to analyze 3 modes of data: electronic health records, biological omics, and social determinants of health metrics.

  View details for DOI 10.1016/j.clp.2024.02.005

  View details for PubMedID 38705652

  View details for PubMedCentralID PMC11070639

• Associations between anxiety, sleep, and blood pressure parameters in pregnancy: a prospective pilot cohort study. BMC pregnancy and childbirth Miller, H. E., Simpson, S. L., Hurtado, J., Boncompagni, A., Chueh, J., Shu, C. H., Barwick, F., Leonard, S. A., Carvalho, B., Sultan, P., Aghaeepour, N., Druzin, M., Panelli, D. M. 2024; 24 (1): 366

  Abstract

  The potential effect modification of sleep on the relationship between anxiety and elevated blood pressure (BP) in pregnancy is understudied. We evaluated the relationship between anxiety, insomnia, and short sleep duration, as well as any interaction effects between these variables, on BP during pregnancy.This was a prospective pilot cohort of pregnant people between 23 to 36 weeks' gestation at a single institution between 2021 and 2022. Standardized questionnaires were used to measure clinical insomnia and anxiety. Objective sleep duration was measured using a wrist-worn actigraphy device. Primary outcomes were systolic (SBP), diastolic (DBP), and mean (MAP) non-invasive BP measurements. Separate sequential multivariable linear regression models fit with generalized estimating equations (GEE) were used to separately assess associations between anxiety (independent variable) and each BP parameter (dependent variables), after adjusting for potential confounders (Model 1). Additional analyses were conducted adding insomnia and the interaction between anxiety and insomnia as independent variables (Model 2), and adding short sleep duration and the interaction between anxiety and short sleep duration as independent variables (Model 3), to evaluate any moderating effects on BP parameters.Among the 60 participants who completed the study, 15 (25%) screened positive for anxiety, 11 (18%) had subjective insomnia, and 34 (59%) had objective short sleep duration. In Model 1, increased anxiety was not associated with increases in any BP parameters. When subjective insomnia was included in Model 2, increased DBP and MAP was significantly associated with anxiety (DBP: β 6.1, p = 0.01, MAP: β 6.2 p < 0.01). When short sleep was included in Model 3, all BP parameters were significantly associated with anxiety (SBP: β 9.6, p = 0.01, DBP: β 8.1, p < 0.001, and MAP: β 8.8, p < 0.001). No moderating effects were detected between insomnia and anxiety (p interactions: SBP 0.80, DBP 0.60, MAP 0.32) or between short sleep duration and anxiety (p interactions: SBP 0.12, DBP 0.24, MAP 0.13) on BP.When including either subjective insomnia or objective short sleep duration, pregnant people with anxiety had 5.1-9.6 mmHg higher SBP, 6.1-8.1 mmHg higher DBP, and 6.2-8.8 mmHg higher MAP than people without anxiety.

  View details for DOI 10.1186/s12884-024-06540-w

  View details for PubMedID 38750438

  View details for PubMedCentralID 2941423

• Evaluation of Sleep in Pregnant Inpatients Compared With Outpatients. Obstetrics and gynecology Panelli, D. M., Miller, H. E., Simpson, S. L., Hurtado, J., Shu, C. H., Boncompagni, A. C., Chueh, J., Barwick, F., Carvalho, B., Sultan, P., Aghaeepour, N., Druzin, M. L. 2024

  Abstract

  To evaluate whether antepartum hospitalization was associated with differences in sleep duration or disrupted sleep patterns.This was a prospective cohort study with enrollment of pregnant people aged 18-55 years with singleton gestations at 16 weeks of gestation or more between 2021 and 2022. Each enrolled antepartum patient was matched by gestational age to outpatients recruited from obstetric clinics at the same institution. Participants responded to the ISI (Insomnia Severity Index) and wore actigraph accelerometer watches for up to 7 days. The primary outcome was total sleep duration per 24 hours. Secondary outcomes included sleep efficiency (time asleep/time in bed), ISI score, clinical insomnia (ISI score higher than 15), short sleep duration (less than 300 minutes/24 hours), wakefulness after sleep onset, number of awakenings, and sleep fragmentation index. Outcomes were evaluated with multivariable generalized estimating equations adjusted for body mass index (BMI), sleep aid use, and insurance type, accounting for gestational age correlations. An interaction term assessed the joint effects of time and inpatient status.Overall 58 participants were included: 18 inpatients and 40 outpatients. Inpatients had significantly lower total sleep duration than outpatients (mean 4.4 hours [SD 1.6 hours] inpatient vs 5.2 hours [SD 1.5 hours] outpatient, adjusted β=-1.1, 95% CI, -1.8 to -0.3, P=.01). Awakenings (10.1 inpatient vs 13.8, P=.01) and wakefulness after sleep onset (28.3 inpatient vs 35.5 outpatient, P=.03) were lower among inpatients. There were no differences in the other sleep outcomes, and no interaction was detected for time in the study and inpatient status. Inpatients were more likely to use sleep aids (39.9% vs 12.5%, P=.03).Hospitalized pregnant patients slept about 1 hour/day less than outpatients. Fewer awakenings and reduced wakefulness after sleep onset among inpatients may reflect increased use of sleep aids in hospitalized patients.

  View details for DOI 10.1097/AOG.0000000000005591

  View details for PubMedID 38663016

• Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends. Heliyon Ghanem, M., Espinosa, C., Chung, P., Reincke, M., Harrison, N., Phongpreecha, T., Shome, S., Saarunya, G., Berson, E., James, T., Xie, F., Shu, C. H., Hazra, D., Mataraso, S., Kim, Y., Seong, D., Chakraborty, D., Studer, M., Xue, L., Marić, I., Chang, A. L., Tjoa, E., Gaudillière, B., Tawfik, V. L., Mackey, S., Aghaeepour, N. 2024; 10 (7): e29050

  Abstract

  Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field.The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test.The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning".Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

  View details for DOI 10.1016/j.heliyon.2024.e29050

  View details for PubMedID 38623206

  View details for PubMedCentralID PMC11016610

• Intra- and post-pandemic impact of the COVID-19 outbreak on Stanford Health Care. Academic pathology Phongpreecha, T., Berson, E., Xue, L., Shome, S., Saarunya, G., Fralick, J., Ruiz-Tagle, B. G., Foody, A., Chin, A. L., Lim, M., Arthofer, R., Albini, C., Montine, K., Folkins, A. K., Kong, C. S., Aghaeepour, N., Montine, T., Kerr, A. 2024; 11 (2): 100113

  Abstract

  Stanford Health Care, which provides about 7% of overall healthcare to approximately 9 million people in the San Francisco Bay Area, has undergone significant changes due to the opening of a second hospital in late 2019 and, more importantly, the COVID-19 pandemic. We examine the impact of these events on anatomic pathology (AP) cases, aiming to enhance operational efficiency in response to evolving healthcare demands. We extracted historical census, admission, lab tests, operation, and APdata since 2015. An approximately 45% increase in the volume of laboratory tests (P<0.0001) and a 17% increase in AP cases (P<0.0001) occurred post-pandemic. These increases were associated with progressively increasing (P<0.0001) hospital census. Census increase stemmed from higher admission through the emergency department (ED), and longer lengths of stay mostly for transfer patients, likely due to the greater capability of the new ED and changes in regional and local practice patterns post-pandemic. Higher census led to overcapacity, which has an inverted U relationship that peaked at 103% capacity for AP cases and 114% capacity for laboratory tests. Overcapacity led to a lower capability to perform clinical activities, particularly those related to surgical procedures. We conclude by suggesting parameters for optimal operations in the post-pandemic era.

  View details for DOI 10.1016/j.acpath.2024.100113

  View details for PubMedID 38562568

• Physical activity among pregnant inpatients and outpatients and associations with anxiety. European journal of obstetrics, gynecology, and reproductive biology Panelli, D. M., Miller, H. E., Simpson, S. L., Hurtado, J., Shu, C. H., Boncompagni, A. C., Chueh, J., Carvalho, B., Sultan, P., Aghaeepour, N., Druzin, M. L. 2024; 297: 8-14

  Abstract

  Physical activity is linked to lower anxiety, but little is known about the association during pregnancy. This is especially important for antepartum inpatients, who are known to have increased anxiety yet may not be able to achieve target levels of physical activity during hospitalization. We compared physical activity metrics between pregnant inpatients and outpatients and explored correlations with anxiety.This was a prospective cohort between 2021 and 2022 of pregnant people aged 18-55 years carrying singleton gestations ≥ 16 weeks. Three exposure groups were matched for gestational age: 1) outpatients from general obstetric clinics; 2) outpatients from high-risk Maternal-Fetal Medicine obstetric clinics; and 3) antepartum inpatients. Participants wore Actigraph GT9X Link accelerometer watches for up to 7 days to measure physical activity. The primary outcome was mean daily step count. Secondary outcomes were metabolic equivalent tasks (METs), hourly kilocalories (kcals), moderate to vigorous physical activity (MVPA) bursts, and anxiety (State-Trait Anxiety Inventory [STAI]). Step counts were compared using multivariable generalized estimating equations adjusting for maternal age, body-mass index, and insurance type as a socioeconomic construct, accounting for within-group clustering by gestational age. Spearman correlations were used to correlate anxiety scores with step counts.58 participants were analyzed. Compared to outpatients, inpatients had significantly lower mean daily steps (primary outcome, adjusted beta -2185, 95 % confidence interval [CI] -3146, -1224, p < 0.01), METs (adjusted beta -0.18, 95 % CI -0.23, -0.13, p < 0.01), MVPAs (adjusted beta -38.2, 95 % CI -52.3, -24.1, p < 0.01), and kcals (adjusted beta -222.9, 95 % CI -438.0, -7.8, p = 0.04). Over the course of the week, steps progressively decreased for inpatients (p-interaction 0.01) but not for either of the outpatient groups. Among the entire cohort, lower step counts correlated with higher anxiety scores (r = 0.30, p = 0.02).We present antenatal population norms and variance for step counts, metabolic equivalent tasks, moderate to vigorous physical activity bursts, and kcals, as well as correlations with anxiety. Antepartum inpatients had significantly lower physical activity than outpatients, and lower step counts correlated with higher anxiety levels. These results highlight the need for physical activity interventions, particularly for hospitalized pregnant people.

  View details for DOI 10.1016/j.ejogrb.2024.03.033

  View details for PubMedID 38554481

• Incidence of Coexisting Diseases in Adult Moyamoya Vasculopathy Patients by Racial Group at a Large American Referral Center. Journal of neurosurgical anesthesiology Wheaton, N., Harrison, N., Doufas, A., Chakraborty, D., Chang, A. L., Aghaeepour, N., Burbridge, M. A. 2024

  View details for DOI 10.1097/ANA.0000000000000962

  View details for PubMedID 38533743

• Proteins in scalp hair of preschool children. Psych Rovnaghi, C. R., Singhal, K., Leib, R. D., Xenochristou, M., Aghaeepour, N., Chien, A. S., Ruiz, M. O., Dinakarpandian, D., Anand, K. J. 2024; 6 (1): 143-162

  Abstract

  (1)Early childhood experiences have long-lasting effects on subsequent mental and physical health, education, and employment. Measurement of these effects relies on insensitive behavioral signs, subjective assessments by adult observers, neuroimaging or neurophysiological studies, or retrospective epidemiologic outcomes. Despite intensive search, the underlying mechanisms for these long-term changes in development and health status remain unknown.(2)We analyzed scalp hair from healthy children and their mothers using an unbiased proteomics platform using tandem mass spectrometry, ultra-performance liquid chromatography, and collision induced dissociation to reveal commonly observed hair proteins with spectral count of 3 or higher.(3)We observed 1368 non-structural hair proteins in children, 1438 non-structural hair proteins in mothers, with 1288 proteins showing individual variability. Mothers showed higher numbers of peptide spectral matches and hair proteins compared to children, with important age-related differences between mothers and children. Age-related differences were also observed in children, with differential protein expression patterns between younger (2 years and below) and older children (3-5 years). We observed greater similarity in hair protein patterns between mothers and their biological children as compared to mothers and unrelated children. The top 5% proteins driving population variability represent biological pathways associated with brain development, immune signaling, and stress response regulation.(4)Non-structural proteins observed in scalp hair include promising biomarkers to investigate the long-term developmental changes and health status associated with early childhood experiences.

  View details for DOI 10.3390/psych6010009

  View details for PubMedID 39534431

  View details for PubMedCentralID PMC11556458

• A Proteomic Signature Persists Years Following a Hypertensive Disorder of Pregnancy. Winn, V. D., Shu, C., Sedan, O., Aghaeepour, N., Hlatky, M. A. SPRINGER HEIDELBERG. 2024: 236A-237A

  View details for Web of Science ID 001287525000527

• Reassessing acquired neonatal intestinal diseases using unsupervised machine learning. Pediatric research Gipson, D. R., Chang, A. L., Lure, A. C., Mehta, S. A., Gowen, T., Shumans, E., Stevenson, D., de la Cruz, D., Aghaeepour, N., Neu, J. 2024

  Abstract

  Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning.Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis.Five clusters of intestinal injury were identified: Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster.Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases.Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.

  View details for DOI 10.1038/s41390-024-03074-x

  View details for PubMedID 38413766

  View details for PubMedCentralID 8096612

• Corrigendum: Advances and potential of omics studies for understanding the development of food allergy. Frontiers in allergy Sindher, S. B., Chin, A. R., Aghaeepour, N., Prince, L., Maecker, H., Shaw, G. M., Stevenson, D., Nadeau, K. C., Snyder, M., Khatri, P., Boyd, S. D., Winn, V. D., Angst, M. S., Chinthrajah, R. S. 2024; 5: 1373485

  Abstract

  [This corrects the article DOI: 10.3389/falgy.2023.1149008.].

  View details for DOI 10.3389/falgy.2024.1373485

  View details for PubMedID 38464397

  View details for PubMedCentralID PMC10921899

• Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights. Nature aging Tang, A. S., Rankin, K. P., Cerono, G., Miramontes, S., Mills, H., Roger, J., Zeng, B., Nelson, C., Soman, K., Woldemariam, S., Li, Y., Lee, A., Bove, R., Glymour, M., Aghaeepour, N., Oskotsky, T. T., Miller, Z., Allen, I. E., Sanders, S. J., Baranzini, S., Sirota, M. 2024

  Abstract

  Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.

  View details for DOI 10.1038/s43587-024-00573-8

  View details for PubMedID 38383858

• Multimodal machine learning for modeling infant head circumference, mothers' milk composition, and their shared environment. Scientific reports Becker, M., Fehr, K., Goguen, S., Miliku, K., Field, C., Robertson, B., Yonemitsu, C., Bode, L., Simons, E., Marshall, J., Dawod, B., Mandhane, P., Turvey, S. E., Moraes, T. J., Subbarao, P., Rodriguez, N., Aghaeepour, N., Azad, M. B. 2024; 14 (1): 2977

  Abstract

  Links between human milk (HM) and infant development are poorly understood and often focus on individual HM components. Here we apply multi-modal predictive machine learning to study HM and head circumference (a proxy for brain development) among 1022 mother-infant dyads of the CHILD Cohort. We integrated HM data (19 oligosaccharides, 28 fatty acids, 3 hormones, 28 chemokines) with maternal and infant demographic, health, dietary and home environment data. Head circumference was significantly predictable at 3 and 12 months. Two of the most associated features were HM n3-polyunsaturated fatty acid C22:6n3 (docosahexaenoic acid, DHA; p = 9.6e-05) and maternal intake of fish (p = 4.1e-03), a key dietary source of DHA with established relationships to brain function. Thus, using a systems biology approach, we identified meaningful relationships between HM and brain development, which validates our statistical approach, gives credence to the novel associations we observed, and sets the foundation for further research with additional cohorts and HM analytes.

  View details for DOI 10.1038/s41598-024-52323-w

  View details for PubMedID 38316895

• Air Pollution and Pregnancy: Insights into Immune Response, Histone Modifications, and Cytokine Signatures Jung, Y., Ha, J., Aguilera, J., Kaushik, A., Cansdale, S., Yang, E., Dermadi, D., Lurmann, F., Lutzker, L., Hammond, K., Balmes, J., Noth, E., Eisen, E., Aghaeepour, N., Shaw, G., Waldrop, A., Khatri, P., Utz, P., Rosenburg-Hasson, Y., Maecker, H., Burt, T., Johnson, M., Nadeau, K. MOSBY-ELSEVIER. 2024: AB370

  View details for Web of Science ID 001267526000850

• Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study. Journal of child psychology and psychiatry, and allied disciplines Buthmann, J. L., Miller, J. G., Aghaeepour, N., King, L. S., Stevenson, D. K., Shaw, G. M., Wong, R. J., Gotlib, I. H. 2024

  Abstract

  Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties.Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their 3- to 5-year-old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist).We found that machine learning models of prenatal proteomics predict 5%-10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes.Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell-signaling pathways were enriched in relation to child temperament and psychopathology measures.

  View details for DOI 10.1111/jcpp.13948

  View details for PubMedID 38287782

• Correction: Digital health application integrating wearable data and behavioral patterns improves metabolic health. NPJ digital medicine Zahedani, A. D., McLaughlin, T., Veluvali, A., Aghaeepour, N., Hosseinian, A., Agarwal, S., Ruan, J., Tripathi, S., Woodward, M., Hashemi, N., Snyder, M. 2024; 7 (1): 9

  View details for DOI 10.1038/s41746-024-00996-y

  View details for PubMedID 38216626

• Discovery of sparse, reliable omic biomarkers with Stabl. Nature biotechnology Hédou, J., Marić, I., Bellan, G., Einhaus, J., Gaudillière, D. K., Ladant, F. X., Verdonk, F., Stelzer, I. A., Feyaerts, D., Tsai, A. S., Ganio, E. A., Sabayev, M., Gillard, J., Amar, J., Cambriel, A., Oskotsky, T. T., Roldan, A., Golob, J. L., Sirota, M., Bonham, T. A., Sato, M., Diop, M., Durand, X., Angst, M. S., Stevenson, D. K., Aghaeepour, N., Montanari, A., Gaudillière, B. 2024

  Abstract

  Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

  View details for DOI 10.1038/s41587-023-02033-x

  View details for PubMedID 38168992

  View details for PubMedCentralID 7003173

• RETRACTED: 60 Predicting chorioamnionitis using AI-based methods: a retrospective cohort study. American journal of obstetrics and gynecology Waldrop, A. R., James, T. K., Suharwardy, S., Studer, M., Chang, A., Bernal, C. E., Xie, F., Shome, S., Hazra, D., Kim, Y., Clarke, G., Chakraborty, D., Mataraso, S., Berson, E., Xue, L., Payrovnaziri, S., Mohammadi, N., Haberkorn, W., Maric, I., El-Sayed, Y. Y., Carvalho, B., Aghaeepour, N. 2024; 230 (1S): S46

  Abstract

  This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). This meeting abstract has been retracted at the request of the authors. The team determined further analysis is warranted before the formal presentation of the results.

  View details for DOI 10.1016/j.ajog.2023.11.081

  View details for PubMedID 38355237

• Longitudinal Triglyceride Profiling in Preterm Infants Suggests Dynamic and Age-Specific Trajectories Reiss, J., Ding, Chang, A., Phongpreecha, T., Lyon, A., Long, J. Z., Jordan, B. K., Scottoline, B., Snyder, M., Sylvester, K., Stevenson, D. K., Aghaeepour, N., Shaw, G. M. SAGE PUBLICATIONS LTD. 2024: 600-602

  View details for Web of Science ID 001307337600598

• MEDALIGN: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., Patel, B., Chiang, C., Callahan, A., Huo, Z., Gatidis, S., Adams, S., Fayanju, O., Shah, S. J., Savage, T., Goh, E., Chaudhari, A. S., Aghaeepour, N., Sharp, C., Pfeffer, M. A., Liang, P., Chen, J. H., Morse, K. E., Brunskill, E. P., Fries, J. A., Shah, N. H. edited by Wooldridge, M., Dy, J., Natarajan, S. ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2024: 22021-22030

  View details for Web of Science ID 001239985800017

• Association of antenatal anxiety, short sleep duration, and blood pressure parameters: a pilot study Miller, H. E., Simpson, S. L., Hurtado, J., Shu, C., Chueh, J., Barwick, F., Leonard, S. A., Carvalho, B., Sultan, P., Aghaeepour, N., Druzin, M. L., Panelli, D. M. MOSBY-ELSEVIER. 2024: S287

  View details for Web of Science ID 001282913902153

• Insomnia in pregnancy: are hospitalized inpatients sleeping less than outpatients? Panelli, D. M., Miller, H. E., Simpson, S. L., Hurtado, J., Shu, C., Boncompagni, A. C., Chueh, J., Barwick, F., Carvalho, B., Sultan, P., Aghaeepour, N., Druzin, M. L. MOSBY-ELSEVIER. 2024: S378

  View details for Web of Science ID 001282913903132

• Physical activity among pregnant inpatients and outpatients and associations with anxiety Panelli, D. M., Miller, H. E., Simpson, S. L., Hurtado, J., Shu, C., Boncompagni, A. C., Chueh, J., Carvalho, B., Sultan, P., Aghaeepour, N., Druzin, M. L. MOSBY-ELSEVIER. 2024: S578

  View details for Web of Science ID 001282913905134

• Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research. Cell reports. Medicine Golob, J. L., Oskotsky, T. T., Tang, A. S., Roldan, A., Chung, V., Ha, C. W., Wong, R. J., Flynn, K. J., Parraga-Leo, A., Wibrand, C., Minot, S. S., Oskotsky, B., Andreoletti, G., Kosti, I., Bletz, J., Nelson, A., Gao, J., Wei, Z., Chen, G., Tang, Z. Z., Novielli, P., Romano, D., Pantaleo, E., Amoroso, N., Monaco, A., Vacca, M., De Angelis, M., Bellotti, R., Tangaro, S., Kuntzleman, A., Bigcraft, I., Techtmann, S., Bae, D., Kim, E., Jeon, J., Joe, S., Theis, K. R., Ng, S., Lee, Y. S., Diaz-Gimeno, P., Bennett, P. R., MacIntyre, D. A., Stolovitzky, G., Lynch, S. V., Albrecht, J., Gomez-Lopez, N., Romero, R., Stevenson, D. K., Aghaeepour, N., Tarca, A. L., Costello, J. C., Sirota, M. 2023: 101350

  Abstract

  Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

  View details for DOI 10.1016/j.xcrm.2023.101350

  View details for PubMedID 38134931

• Understanding the molecular basis of resilience to Alzheimer's disease FRONTIERS IN NEUROSCIENCE Montine, K. S., Berson, E., Phongpreecha, T., Huang, Z., Aghaeepour, N., Zou, J. Y., Maccoss, M. J., Montine, T. J. 2023; 17

  View details for DOI 10.3389/fnins.2023.1311157

  View details for Web of Science ID 001133997000001

• Understanding the molecular basis of resilience to Alzheimer's disease. Frontiers in neuroscience Montine, K. S., Berson, E., Phongpreecha, T., Huang, Z., Aghaeepour, N., Zou, J. Y., MacCoss, M. J., Montine, T. J. 2023; 17: 1311157

  Abstract

  The cellular and molecular distinction between brain aging and neurodegenerative disease begins to blur in the oldest old. Approximately 15-25% of observations in humans do not fit predicted clinical manifestations, likely the result of suppressed damage despite usually adequate stressors and of resilience, the suppression of neurological dysfunction despite usually adequate degeneration. Factors during life may predict the clinico-pathologic state of resilience: cardiovascular health and mental health, more so than educational attainment, are predictive of a continuous measure of resilience to Alzheimer's disease (AD) and AD-related dementias (ADRDs). In resilience to AD alone (RAD), core features include synaptic and axonal processes, especially in the hippocampus. Future focus on larger and more diverse cohorts and additional regions offer emerging opportunities to understand this counterforce to neurodegeneration. The focus of this review is the molecular basis of resilience to AD.

  View details for DOI 10.3389/fnins.2023.1311157

  View details for PubMedID 38192507

  View details for PubMedCentralID PMC10773681

• Digital health application integrating wearable data and behavioral patterns improves metabolic health. NPJ digital medicine Zahedani, A. D., Veluvali, A., McLaughlin, T., Aghaeepour, N., Hosseinian, A., Agarwal, S., Ruan, J., Tripathi, S., Woodward, M., Hashemi, N., Snyder, M. 2023; 6 (1): 216

  Abstract

  The effectiveness of lifestyle interventions in reducing caloric intake and increasing physical activity for preventing Type 2 Diabetes (T2D) has been previously demonstrated. The use of modern technologies can potentially further improve the success of these interventions, promote metabolic health, and prevent T2D at scale. To test this concept, we built a remote program that uses continuous glucose monitoring (CGM) and wearables to make lifestyle recommendations that improve health. We enrolled 2,217 participants with varying degrees of glucose levels (normal range, and prediabetes and T2D ranges), using continuous glucose monitoring (CGM) over 28 days to capture glucose patterns. Participants logged food intake, physical activity, and body weight via a smartphone app that integrated wearables data and provided daily insights, including overlaying glucose patterns with activity and food intake, macronutrient breakdown, glycemic index (GI), glycemic load (GL), and activity measures. The app furthermore provided personalized recommendations based on users' preferences, goals, and observed glycemic patterns. Users could interact with the app for an additional 2 months without CGM. Here we report significant improvements in hyperglycemia, glucose variability, and hypoglycemia, particularly in those who were not diabetic at baseline. Body weight decreased in all groups, especially those who were overweight or obese. Healthy eating habits improved significantly, with reduced daily caloric intake and carbohydrate-to-calorie ratio and increased intake of protein, fiber, and healthy fats relative to calories. These findings suggest that lifestyle recommendations, in addition to behavior logging and CGM data integration within a mobile app, can enhance the metabolic health of both nondiabetic and T2D individuals, leading to healthier lifestyle choices. This technology can be a valuable tool for T2D prevention and treatment.

  View details for DOI 10.1038/s41746-023-00956-y

  View details for PubMedID 38001287

  View details for PubMedCentralID 3891203

• Quantitative estimate of cognitive resilience and its medical and genetic associations. Alzheimer's research & therapy Phongpreecha, T., Godrich, D., Berson, E., Espinosa, C., Kim, Y., Cholerton, B., Chang, A. L., Mataraso, S., Bukhari, S. A., Perna, A., Yakabi, K., Montine, K. S., Poston, K. L., Mormino, E., White, L., Beecham, G., Aghaeepour, N., Montine, T. J. 2023; 15 (1): 192

  Abstract

  We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined.This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR's relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer's Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR.CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele (P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms.Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals.

  View details for DOI 10.1186/s13195-023-01329-z

  View details for PubMedID 37926851

  View details for PubMedCentralID 6410486

• Prior Knowledge Integration Improves Relapse Prediction and Identifies Relapse Associated Mechanisms in Childhood B Cell Acute Lymphoblastic Leukemia Koladiya, A., Jager, A., Culos, A., Merchant, M., Liu, Y., Stuani, L., Sarno, J., Domizi, P., Mullighan, C. G., Aghaeepour, N., Bendall, S., Davis, K. L. AMER SOC HEMATOLOGY. 2023

  View details for DOI 10.1182/blood-2023-187264

  View details for Web of Science ID 001159306706074

• Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo. Cell Wolf, J., Rasmussen, D. K., Sun, Y. J., Vu, J. T., Wang, E., Espinosa, C., Bigini, F., Chang, R. T., Montague, A. A., Tang, P. H., Mruthyunjaya, P., Aghaeepour, N., Dufour, A., Bassuk, A. G., Mahajan, V. B. 2023

  Abstract

  Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.

  View details for DOI 10.1016/j.cell.2023.09.012

  View details for PubMedID 37863056

• LEVERAGING ELECTRONIC MEDICAL RECORDS REVEALS COMORBIDITIES SIGNIFICANTLY ASSOCIATED WITH MALE INFERTILITY Woldemariam, S., Xie, F., Roldan, A., Roger, J., Tang, A., Oskotsky, T., Aghaeepour, N., Eisenberg, M., Sirota, M. ELSEVIER SCIENCE INC. 2023: E53-E54

  View details for Web of Science ID 001084670200128

• Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity. NPJ digital medicine Ravindra, N. G., Espinosa, C., Berson, E., Phongpreecha, T., Zhao, P., Becker, M., Chang, A. L., Shome, S., Marić, I., De Francesco, D., Mataraso, S., Saarunya, G., Thuraiappah, M., Xue, L., Gaudillière, B., Angst, M. S., Shaw, G. M., Herzog, E. D., Stevenson, D. K., England, S. K., Aghaeepour, N. 2023; 6 (1): 171

  Abstract

  Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

  View details for DOI 10.1038/s41746-023-00911-x

  View details for PubMedID 37770643

  View details for PubMedCentralID 3796350

• Cross-species comparative analysis of single presynapses. Scientific reports Berson, E., Gajera, C. R., Phongpreecha, T., Perna, A., Bukhari, S. A., Becker, M., Chang, A. L., De Francesco, D., Espinosa, C., Ravindra, N. G., Postupna, N., Latimer, C. S., Shively, C. A., Register, T. C., Craft, S., Montine, K. S., Fox, E. J., Keene, C. D., Bendall, S. C., Aghaeepour, N., Montine, T. J. 2023; 13 (1): 13849

  Abstract

  Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.

  View details for DOI 10.1038/s41598-023-40683-8

  View details for PubMedID 37620363

  View details for PubMedCentralID 3365257

• Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature. Nature communications Berson, E., Sreenivas, A., Phongpreecha, T., Perna, A., Grandi, F. C., Xue, L., Ravindra, N. G., Payrovnaziri, N., Mataraso, S., Kim, Y., Espinosa, C., Chang, A. L., Becker, M., Montine, K. S., Fox, E. J., Chang, H. Y., Corces, M. R., Aghaeepour, N., Montine, T. J. 2023; 14 (1): 4947

  Abstract

  Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.

  View details for DOI 10.1038/s41467-023-40611-4

  View details for PubMedID 37587197

  View details for PubMedCentralID 6071637

• Variability and relative contribution of surgeon and anesthesia specific time components to total procedural time in cardiac surgery. The Journal of thoracic and cardiovascular surgery Vanneman, M. W., Thuraiappah, M., Feinstein, I., Fielding-Singh, V., Peterson, A., Kronenberg, S., Angst, M. S., Aghaeepour, N. 2023

  Abstract

  OBJECTIVES: Decreasing variability in time intensive tasks during cardiac surgery may reduce total procedural time, lower costs, reduce clinician burnout, and improve patient access. The relative contribution and variability of surgeon and anesthesia control times to total procedural time is unknown.METHODS: 669 patients undergoing coronary artery bypass graft surgery were enrolled. Using linear regression, we estimated adjusted surgeon and anesthesia control times controlling for patient and procedural covariates. The primary end point compared overall surgeon and anesthesia control times. The secondary end point compared the variability in adjusted surgeon and anesthesiologist control times. Sensitivity analyses quantified the relative importance of the specific surgeon and anesthesiologist in the adjusted linear models.RESULTS: The median surgeon control time was 4.1 hours (interquartile range: 3.4 to 4.9 hours) compared to a median anesthesia control time of 1.0 hours (interquartile range: 0.8 to 1.2 hours, p < 0.001). Using linear regression, the variability in adjusted surgeon control time amongst surgeons (range: 1.8 hours) was 3.5-fold greater than the variability in adjusted anesthesia control time amongst anesthesiologists (range: 0.5 hours, p < 0.001). The specific surgeon and anesthesiologist accounted for 50% of the explanatory power of the predictive model (p < 0.001).CONCLUSIONS: Surgeon control time variability is significantly greater than anesthesia control time variability and strongly associated with the surgeon performing the procedure. While these results suggest surgeon control time variability is an attractive operational target, further studies are needed to determine practitioner specific and modifiable attributes to reduce variability and improve efficiency.

  View details for DOI 10.1016/j.jtcvs.2023.08.011

  View details for PubMedID 37574007

• Machine learning models of plasma proteomic data predict mood in chronic stroke and tie it to aberrant peripheral immune responses. Brain, behavior, and immunity Bidoki, N. H., Zera, K. A., Nassar, H., Drag, L. L., Mlynash, M., Osborn, E., Musabbir, M., Eun K Kim, D., Paula Mendez, M., Lansberg, M. G., Aghaeepour, N., Buckwalter, M. S. 2023

  Abstract

  Post-stroke depression is common, long-lasting and associated with severe morbidity and death, but mechanisms are not well-understood. We used a broad proteomics panel and developed a machine learning algorithm to determine whether plasma protein data can predict mood in people with chronic stroke, and to identify proteins and pathways associated with mood. We used Olink to measure 1,196 plasma proteins in 85 participants aged 25 and older who were between 5 months and 9 years after ischemic stroke. Mood was assessed with the Stroke Impact Scale mood questionnaire (SIS3). Machine learning multivariable regression models were constructed to estimate SIS3 using proteomics data, age, and time since stroke. We also dichotomized participants into better mood (SIS3 > 63) or worse mood (SIS3 ≤ 63) and analyzed candidate proteins. Machine learning models verified that there is indeed a relationship between plasma proteomic data and mood in chronic stroke, with the most accurate prediction of mood occurring when we add age and time since stroke. At the individual protein level, no single protein or set of proteins predicts mood. But by using univariate analyses of the proteins most highly associated with mood we produced a model of chronic post-stroke depression. We utilized the fact that this list contained many proteins that are also implicated in major depression. Also, over 80% of immune proteins that correlate with mood were higher with worse mood, implicating a broadly overactive immune system in chronic post-stroke depression. Finally, we used a comprehensive literature review of major depression and acute post-stroke depression. We propose that in chronic post-stroke depression there is over-activation of the immune response that then triggers changes in serotonin activity and neuronal plasticity leading to depressed mood.

  View details for DOI 10.1016/j.bbi.2023.08.002

  View details for PubMedID 37557961

• Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine. AJOG global reports Espinosa, C., Ali, S. M., Khan, W., Khanam, R., Pervin, J., Price, J. T., Rahman, S., Hasan, T., Ahmed, S., Raqib, R., Rahman, M., Aktar, S., Nisar, M. I., Khalid, J., Dhingra, U., Dutta, A., Deb, S., Stringer, J. S., Wong, R. J., Shaw, G. M., Stevenson, D. K., Darmstadt, G. L., Gaudilliere, B., Baqui, A. H., Jehan, F., Rahman, A., Sazawal, S., Vwalika, B., Aghaeepour, N., Angst, M. S. 2023; 3 (3): 100244

  Abstract

  Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures.This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome.Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins.A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins.Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

  View details for DOI 10.1016/j.xagr.2023.100244

  View details for PubMedID 37456144

  View details for PubMedCentralID PMC10339042

• Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries. Science advances Espinosa, C. A., Khan, W., Khanam, R., Das, S., Khalid, J., Pervin, J., Kasaro, M. P., Contrepois, K., Chang, A. L., Phongpreecha, T., Michael, B., Ellenberger, M., Mehmood, U., Hotwani, A., Nizar, A., Kabir, F., Wong, R. J., Becker, M., Berson, E., Culos, A., De Francesco, D., Mataraso, S., Ravindra, N., Thuraiappah, M., Xenochristou, M., Stelzer, I. A., Marić, I., Dutta, A., Raqib, R., Ahmed, S., Rahman, S., Hasan, A. S., Ali, S. M., Juma, M. H., Rahman, M., Aktar, S., Deb, S., Price, J. T., Wise, P. H., Winn, V. D., Druzin, M. L., Gibbs, R. S., Darmstadt, G. L., Murray, J. C., Stringer, J. S., Gaudilliere, B., Snyder, M. P., Angst, M. S., Rahman, A., Baqui, A. H., Jehan, F., Nisar, M. I., Vwalika, B., Sazawal, S., Shaw, G. M., Stevenson, D. K., Aghaeepour, N. 2023; 9 (21): eade7692

  Abstract

  Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

  View details for DOI 10.1126/sciadv.ade7692

  View details for PubMedID 37224249

• STABL Enables Reliable and Selective biomarker Discovery in Predictive Modeling of High Dimensional Omics Data Verdonk, F., Hedou, J., Maric, I., Bellan, G., Einhaus, J., Gaudilliere, D., Ladant, F., Stelzer, I., Feyaerts, D., Tsai, A., Bonham, A., Angst, M., Aghaeepour, N., Stevenson, D., Tibshirani, R., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2023: 814-821

  View details for Web of Science ID 001058985600289

• Large-scale correlation network construction for unraveling the coordination of complex biological systems NATURE COMPUTATIONAL SCIENCE Becker, M., Nassar, H., Espinosa, C., Stelzer, I. A., Feyaerts, D., Berson, E., Bidoki, N. H., Chang, A. L., Saarunya, G., Culos, A., De Francesco, D., Fallahzadeh, R., Liu, Q., Kim, Y., Maric, I., Mataraso, S. J., Payrovnaziri, S., Phongpreecha, T., Ravindra, N. G., Stanley, N., Shome, S., Tan, Y., Thuraiappah, M., Xenochristou, M., Xue, L., Shaw, G., Stevenson, D., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2023

  View details for DOI 10.1038/s43588-023-00429-y

  View details for Web of Science ID 000968297800002

• Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research. medRxiv : the preprint server for health sciences Golob, J. L., Oskotsky, T. T., Tang, A. S., Roldan, A., Chung, V., Ha, C. W., Wong, R. J., Flynn, K. J., Parraga-Leo, A., Wibrand, C., Minot, S. S., Andreoletti, G., Kosti, I., Bletz, J., Nelson, A., Gao, J., Wei, Z., Chen, G., Tang, Z. Z., Novielli, P., Romano, D., Pantaleo, E., Amoroso, N., Monaco, A., Vacca, M., De Angelis, M., Bellotti, R., Tangaro, S., Kuntzleman, A., Bigcraft, I., Techtmann, S., Bae, D., Kim, E., Jeon, J., Joe, S., Theis, K. R., Ng, S., Lee Li, Y. S., Diaz-Gimeno, P., Bennett, P. R., MacIntyre, D. A., Stolovitzky, G., Lynch, S. V., Albrecht, J., Gomez-Lopez, N., Romero, R., Stevenson, D. K., Aghaeepour, N., Tarca, A. L., Costello, J. C., Sirota, M. 2023

  Abstract

  Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

  View details for DOI 10.1101/2023.03.07.23286920

  View details for PubMedID 36945505

  View details for PubMedCentralID PMC10029035

• VMAP: Vaginal Microbiome Atlas During Pregnancy. medRxiv : the preprint server for health sciences Parraga-Leo, A., Oskotsky, T. T., Oskotsky, B., Wibrand, C., Roldan, A., Tang, A., Ha, C. W., Wong, R. J., Minot, S. S., Andreoletti, G., Kosti, I., Theis, K. R., Ng, S., Lee, Y. S., Diaz-Gimeno, P., Bennett, P. R., MacIntyre, D. A., Lynch, S. V., Romero, R., Tarca, A. L., Stevenson, D. K., Aghaeepour, N., Golob, J., Sirota, M. 2023

  Abstract

  The vaginal microbiome has been shown to be associated with pregnancy outcomes including preterm birth (PTB) risk. Here we present VMAP: Vaginal Microbiome Atlas during Pregnancy (http://vmapapp.org), an application to visualize features of 3,909 vaginal microbiome samples of 1,416 pregnant individuals from 11 studies, aggregated from raw public and newly generated sequences via an open-source tool, MaLiAmPi. Our visualization tool (http://vmapapp.org) includes microbial features such as various measures of diversity, VALENCIA community state types (CST), and composition (via phylotypes and taxonomy). This work serves as a resource for the research community to further analyze and visualize vaginal microbiome data in order to better understand both healthy term pregnancies and those associated with adverse outcomes.

  View details for DOI 10.1101/2023.03.21.23286947

  View details for PubMedID 36993193

  View details for PubMedCentralID PMC10055588

• A longitudinal examination of parent diagnostic uncertainty in pediatric chronic pain Neville, A., Biggs, E., Tremblay-McGaw, A., Miner, A., Coghill, R., King, C., Lopez-Sola, M., Moayedi, M., Gaudilliere, B., Aghaeepour, N., Angst, M., Stinson, J., Simons, L. E. OXFORD UNIV PRESS INC. 2023: 144-145

  View details for Web of Science ID 001061853800310

• Large-scale correlation network construction for unraveling the coordination of complex biological systems. Nature computational science Becker, M., Nassar, H., Espinosa, C., Stelzer, I. A., Feyaerts, D., Berson, E., Bidoki, N. H., Chang, A. L., Saarunya, G., Culos, A., De Francesco, D., Fallahzadeh, R., Liu, Q., Kim, Y., Marić, I., Mataraso, S. J., Payrovnaziri, S. N., Phongpreecha, T., Ravindra, N. G., Stanley, N., Shome, S., Tan, Y., Thuraiappah, M., Xenochristou, M., Xue, L., Shaw, G., Stevenson, D., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2023; 3 (4): 346-359

  Abstract

  Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

  View details for DOI 10.1038/s43588-023-00429-y

  View details for PubMedID 38116462

  View details for PubMedCentralID PMC10727505

• Evidence for human milk as a biological system and recommendations for study design-a report from "Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)" Working Group 4. The American journal of clinical nutrition Donovan, S. M., Aghaeepour, N., Andres, A., Azad, M. B., Becker, M., Carlson, S. E., Järvinen, K. M., Lin, W., Lönnerdal, B., Slupsky, C. M., Steiber, A. L., Raiten, D. J. 2023; 117 Suppl 1: S61-S86

  Abstract

  Human milk contains all of the essential nutrients required by the infant within a complex matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is a source of bioactive components, living cells and microbes that facilitate the transition to life outside the womb. Our ability to fully appreciate the importance of this matrix relies on the recognition of short- and long-term health benefits and, as highlighted in previous sections of this supplement, its ecology (i.e., interactions among the lactating parent and breastfed infant as well as within the context of the human milk matrix itself). Designing and interpreting studies to address this complexity depends on the availability of new tools and technologies that account for such complexity. Past efforts have often compared human milk to infant formula, which has provided some insight into the bioactivity of human milk, as a whole, or of individual milk components supplemented with formula. However, this experimental approach cannot capture the contributions of the individual components to the human milk ecology, the interaction between these components within the human milk matrix, or the significance of the matrix itself to enhance human milk bioactivity on outcomes of interest. This paper presents approaches to explore human milk as a biological system and the functional implications of that system and its components. Specifically, we discuss study design and data collection considerations and how emerging analytical technologies, bioinformatics, and systems biology approaches could be applied to advance our understanding of this critical aspect of human biology.

  View details for DOI 10.1016/j.ajcnut.2022.12.021

  View details for PubMedID 37173061

• Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study. Research square Roger, J., Xie, F., Costello, J., Tang, A., Liu, J., Oskotsky, T., Woldemariam, S., Kosti, I., Le, B., Snyder, M. P., Giudice, L. C., Torgerson, D., Shaw, G. M., Stevenson, D. K., Rajkovic, A., Glymour, M. M., Aghaeepour, N., Cakmak, H., Lathi, R. B., Sirota, M. 2023

  Abstract

  Recurrent pregnancy loss (RPL), defined as 2 or more pregnancy losses, affects 5-6% of ever-pregnant individuals. Approximately half of these cases have no identifiable explanation. To generate hypotheses about RPL etiologies, we implemented a case-control study comparing the history of over 1,600 diagnoses between RPL and live-birth patients, leveraging the University of California San Francisco (UCSF) and Stanford University electronic health record databases. In total, our study included 8,496 RPL (UCSF: 3,840, Stanford: 4,656) and 53,278 Control (UCSF: 17,259, Stanford: 36,019) patients. Menstrual abnormalities and infertility-associated diagnoses were significantly positively associated with RPL in both medical centers. Age-stratified analysis revealed that the majority of RPL-associated diagnoses had higher odds ratios for patients <35 compared with 35+ patients. While Stanford results were sensitive to control for healthcare utilization, UCSF results were stable across analyses with and without utilization. Intersecting significant results between medical centers was an effective filter to identify associations that are robust across center-specific utilization patterns.

  View details for DOI 10.21203/rs.3.rs-2631220/v1

  View details for PubMedID 36993325

  View details for PubMedCentralID PMC10055527

• Author Correction: Early response evaluation by single cell signaling profiling in acute myeloid leukemia. Nature communications Tislevoll, B. S., Hellesøy, M., Fagerholt, O. H., Gullaksen, S. E., Srivastava, A., Birkeland, E., Kleftogiannis, D., Ayuda-Durán, P., Piechaczyk, L., Tadele, D. S., Skavland, J., Baliakas, P., Hovland, R., Andresen, V., Seternes, O. M., Tvedt, T. H., Aghaeepour, N., Gavasso, S., Porkka, K., Jonassen, I., Fløisand, Y., Enserink, J., Blaser, N., Gjertsen, B. T. 2023; 14 (1): 1767

  View details for DOI 10.1038/s41467-023-37488-8

  View details for PubMedID 36997540

  View details for PubMedCentralID PMC10063685

• Advances and potential of omics studies for understanding the development of food allergy. Frontiers in allergy Sindher, S. B., Chin, A. R., Aghaeepour, N., Prince, L., Maecker, H., Shaw, G. M., Stevenson, D. K., Nadeau, K. C., Snyder, M., Khatri, P., Boyd, S. D., Winn, V. D., Angst, M. S., Chinthrajah, R. S. 2023; 4: 1149008

  Abstract

  The prevalence of food allergy continues to rise globally, carrying with it substantial safety, economic, and emotional burdens. Although preventative strategies do exist, the heterogeneity of allergy trajectories and clinical phenotypes has made it difficult to identify patients who would benefit from these strategies. Therefore, further studies investigating the molecular mechanisms that differentiate these trajectories are needed. Large-scale omics studies have identified key insights into the molecular mechanisms for many different diseases, however the application of these technologies to uncover the drivers of food allergy development is in its infancy. Here we review the use of omics approaches in food allergy and highlight key gaps in knowledge for applying these technologies for the characterization of food allergy development.

  View details for DOI 10.3389/falgy.2023.1149008

  View details for PubMedID 37034151

  View details for PubMedCentralID PMC10080041

• Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data. Research square Hédou, J., Marić, I., Bellan, G., Einhaus, J., Gaudillière, D. K., Ladant, F. X., Verdonk, F., Stelzer, I. A., Feyaerts, D., Tsai, A. S., Ganio, E. A., Sabayev, M., Gillard, J., Bonham, T. A., Sato, M., Diop, M., Angst, M. S., Stevenson, D., Aghaeepour, N., Montanari, A., Gaudillière, B. 2023

  Abstract

  High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at https://github.com/gregbellan/Stabl.

  View details for DOI 10.21203/rs.3.rs-2609859/v1

  View details for PubMedID 36909508

  View details for PubMedCentralID PMC10002850

• Changes in preterm birth and stillbirth during COVID-19 lockdowns in 26 countries. Nature human behaviour Calvert, C., Brockway, M. M., Zoega, H., Miller, J. E., Been, J. V., Amegah, A. K., Racine-Poon, A., Oskoui, S. E., Abok, I. I., Aghaeepour, N., Akwaowo, C. D., Alshaikh, B. N., Ayede, A. I., Bacchini, F., Barekatain, B., Barnes, R., Bebak, K., Berard, A., Bhutta, Z. A., Brook, J. R., Bryan, L. R., Cajachagua-Torres, K. N., Campbell-Yeo, M., Chu, D. T., Connor, K. L., Cornette, L., Cortés, S., Daly, M., Debauche, C., Dedeke, I. O., Einarsdóttir, K., Engjom, H., Estrada-Gutierrez, G., Fantasia, I., Fiorentino, N. M., Franklin, M., Fraser, A., Gachuno, O. W., Gallo, L. A., Gissler, M., Håberg, S. E., Habibelahi, A., Häggström, J., Hookham, L., Hui, L., Huicho, L., Hunter, K. J., Huq, S., Kc, A., Kadambari, S., Kelishadi, R., Khalili, N., Kippen, J., Le Doare, K., Llorca, J., Magee, L. A., Magnus, M. C., Man, K. K., Mburugu, P. M., Mediratta, R. P., Morris, A. D., Muhajarine, N., Mulholland, R. H., Bonnard, L. N., Nakibuuka, V., Nassar, N., Nyadanu, S. D., Oakley, L., Oladokun, A., Olayemi, O. O., Olutekunbi, O. A., Oluwafemi, R. O., Ogunkunle, T. O., Orton, C., Örtqvist, A. K., Ouma, J., Oyapero, O., Palmer, K. R., Pedersen, L. H., Pereira, G., Pereyra, I., Philip, R. K., Pruski, D., Przybylski, M., Quezada-Pinedo, H. G., Regan, A. K., Rhoda, N. R., Rihs, T. A., Riley, T., Rocha, T. A., Rolnik, D. L., Saner, C., Schneuer, F. J., Souter, V. L., Stephansson, O., Sun, S., Swift, E. M., Szabó, M., Temmerman, M., Tooke, L., Urquia, M. L., von Dadelszen, P., Wellenius, G. A., Whitehead, C., Wong, I. C., Wood, R., Wróblewska-Seniuk, K., Yeboah-Antwi, K., Yilgwan, C. S., Zawiejska, A., Sheikh, A., Rodriguez, N., Burgner, D., Stock, S. J., Azad, M. B. 2023

  Abstract

  Preterm birth (PTB) is the leading cause of infant mortality worldwide. Changes in PTB rates, ranging from -90% to +30%, were reported in many countries following early COVID-19 pandemic response measures ('lockdowns'). It is unclear whether this variation reflects real differences in lockdown impacts, or perhaps differences in stillbirth rates and/or study designs. Here we present interrupted time series and meta-analyses using harmonized data from 52 million births in 26 countries, 18 of which had representative population-based data, with overall PTB rates ranging from 6% to 12% and stillbirth ranging from 2.5 to 10.5 per 1,000 births. We show small reductions in PTB in the first (odds ratio 0.96, 95% confidence interval 0.95-0.98, P value <0.0001), second (0.96, 0.92-0.99, 0.03) and third (0.97, 0.94-1.00, 0.09) months of lockdown, but not in the fourth month of lockdown (0.99, 0.96-1.01, 0.34), although there were some between-country differences after the first month. For high-income countries in this study, we did not observe an association between lockdown and stillbirths in the second (1.00, 0.88-1.14, 0.98), third (0.99, 0.88-1.12, 0.89) and fourth (1.01, 0.87-1.18, 0.86) months of lockdown, although we have imprecise estimates due to stillbirths being a relatively rare event. We did, however, find evidence of increased risk of stillbirth in the first month of lockdown in high-income countries (1.14, 1.02-1.29, 0.02) and, in Brazil, we found evidence for an association between lockdown and stillbirth in the second (1.09, 1.03-1.15, 0.002), third (1.10, 1.03-1.17, 0.003) and fourth (1.12, 1.05-1.19, <0.001) months of lockdown. With an estimated 14.8 million PTB annually worldwide, the modest reductions observed during early pandemic lockdowns translate into large numbers of PTB averted globally and warrant further research into causal pathways.

  View details for DOI 10.1038/s41562-023-01522-y

  View details for PubMedID 36849590

  View details for PubMedCentralID 8417352

• Data-driven longitudinal characterization of neonatal health and morbidity. Science translational medicine De Francesco, D., Reiss, J. D., Roger, J., Tang, A. S., Chang, A. L., Becker, M., Phongpreecha, T., Espinosa, C., Morin, S., Berson, E., Thuraiappah, M., Le, B. L., Ravindra, N. G., Payrovnaziri, S. N., Mataraso, S., Kim, Y., Xue, L., Rosenstein, M. G., Oskotsky, T., Marić, I., Gaudilliere, B., Carvalho, B., Bateman, B. T., Angst, M. S., Prince, L. S., Blumenfeld, Y. J., Benitz, W. E., Fuerch, J. H., Shaw, G. M., Sylvester, K. G., Stevenson, D. K., Sirota, M., Aghaeepour, N. 2023; 15 (683): eadc9854

  Abstract

  Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

  View details for DOI 10.1126/scitranslmed.adc9854

  View details for PubMedID 36791208

• Towards multiomic analysis of oral mucosal pathologies. Seminars in immunopathology Einhaus, J., Han, X., Feyaerts, D., Sunwoo, J., Gaudilliere, B., Ahmad, S. H., Aghaeepour, N., Bruckman, K., Ojcius, D., Schurch, C. M., Gaudilliere, D. K. 2023

  Abstract

  Oral mucosal pathologies comprise an array of diseases with worldwide prevalence and medical relevance. Affecting a confined space with crucial physiological and social functions, oral pathologies can be mutilating and drastically reduce quality of life. Despite their relevance, treatment for these diseases is often far from curative and remains vastly understudied. While multiple factors are involved in the pathogenesis of oral mucosal pathologies, the host's immune system plays a major role in the development, maintenance, and resolution of these diseases. Consequently, a precise understanding of immunological mechanisms implicated in oral mucosal pathologies is critical (1) to identify accurate, mechanistic biomarkers of clinical outcomes; (2) to develop targeted immunotherapeutic strategies; and (3) to individualize prevention and treatment approaches. Here, we review key elements of the immune system's role in oral mucosal pathologies that hold promise to overcome limitations in current diagnostic and therapeutic approaches. We emphasize recent and ongoing multiomic and single-cell approaches that enable an integrative view of these pathophysiological processes and thereby provide unifying and clinically relevant biological signatures.

  View details for DOI 10.1007/s00281-022-00982-0

  View details for PubMedID 36790488

• Sp3 is essential for normal lung morphogenesis and cell cycle progression during mouse embryonic development. Development (Cambridge, England) McCoy, A. M., Lakhdari, O., Shome, S., Caoili, K., Hernandez, G. E., Aghaeepour, N., Butcher, L. D., Fisch, K., Prince, L. S. 2023

  Abstract

  Members of the Sp family of transcription factors regulate gene expression via binding GC boxes within promoter regions. Unlike Sp1, which stimulates transcription, the closely related Sp3 can either repress or activate gene expression and is required for perinatal survival in mice. Here we use RNAseq and cellular phenotyping to show how Sp3 regulates murine fetal cell differentiation and proliferation. Homozygous Sp3-/- mice were smaller than WT and Sp+/- littermates, died soon after birth, and had abnormal lung morphogenesis. RNAseq of Sp3-/- fetal lung mesenchymal cells identified alterations in extracellular matrix production, developmental signaling pathways, and myofibroblast/lipofibroblast differentiation. The lungs of Sp3-/- mice contained multiple structural defects, with abnormal endothelial cell morphology, lack of elastic fiber formation, and accumulation of lipid droplets within mesenchymal lipofibroblasts. Sp3-/- cells and mice also displayed cell cycle arrest, with accumulation in G0/G1 and reduced expression of numerous cell cycle regulators including Ccne1. These data detail the global impact of Sp3 on in vivo mouse gene expression and development.

  View details for DOI 10.1242/dev.200839

  View details for PubMedID 36762637

• The impacts of ambient air pollution exposure during pregnancy on maternal and neonatal inflammatory biomarkers Ha, J., Aguilera, J., Jung, Y., Cansdale, S., Lurmann, F., Lutzker, L., Hammond, K., Balmes, J., Noth, E., Eisen, E., Aghaeepour, N., Shaw, G., Waldrop, A., Khatri, P., Utz, P. J., Rosenburg-Hasson, Y., Maecker, H., Burt, T., Nadeau, K., Prunicki, M. MOSBY-ELSEVIER. 2023: AB119

  View details for Web of Science ID 000991651900367

• Prediction of neuropathologic lesions from clinical data. Alzheimer's & dementia : the journal of the Alzheimer's Association Phongpreecha, T., Cholerton, B., Bhukari, S., Chang, A. L., De Francesco, D., Thuraiappah, M., Godrich, D., Perna, A., Becker, M. G., Ravindra, N. G., Espinosa, C., Kim, Y., Berson, E., Mataraso, S., Sha, S. J., Fox, E. J., Montine, K. S., Baker, L. D., Craft, S., White, L., Poston, K. L., Beecham, G., Aghaeepour, N., Montine, T. J. 2023

  Abstract

  Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life.This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities.Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased.Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions.

  View details for DOI 10.1002/alz.12921

  View details for PubMedID 36681388

• Early response evaluation by single cell signaling profiling in acute myeloid leukemia. Nature communications Tislevoll, B. S., Hellesoy, M., Fagerholt, O. H., Gullaksen, S., Srivastava, A., Birkeland, E., Kleftogiannis, D., Ayuda-Duran, P., Piechaczyk, L., Tadele, D. S., Skavland, J., Panagiotis, B., Hovland, R., Andresen, V., Seternes, O. M., Tvedt, T. H., Aghaeepour, N., Gavasso, S., Porkka, K., Jonassen, I., Floisand, Y., Enserink, J., Blaser, N., Gjertsen, B. T. 2023; 14 (1): 115

  Abstract

  Aberrant pro-survival signaling is a hallmark of cancer cells, but the response to chemotherapy is poorly understood. In this study, we investigate the initial signaling response to standard induction chemotherapy in a cohort of 32 acute myeloid leukemia (AML) patients, using 36-dimensional mass cytometry. Through supervised and unsupervised machine learning approaches, we find that reduction of extracellular-signal-regulated kinase (ERK) 1/2 and p38 mitogen-activated protein kinase (MAPK) phosphorylation in the myeloid cell compartment 24h post-chemotherapy is a significant predictor of patient 5-year overall survival in this cohort. Validation by RNA sequencing shows induction of MAPK target gene expression in patients with high phospho-ERK1/2 24h post-chemotherapy, while proteomics confirm an increase of the p38 prime target MAPK activated protein kinase 2 (MAPKAPK2). In this study, we demonstrate that mass cytometry can be a valuable tool for early response evaluation in AML and elucidate the potential of functional signaling analyses in precision oncology diagnostics.

  View details for DOI 10.1038/s41467-022-35624-4

  View details for PubMedID 36611026

• Feature-weighted elastic net: using "features of features" for better prediction. Statistica Sinica Tay, J. K., Aghaeepour, N., Hastie, T., Tibshirani, R. 2023; 33 (1): 259-279

  Abstract

  In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additional information for better prediction. The method, which we call the feature-weighted elastic net ("fwelnet"), uses these "features of features" to adapt the relative penalties on the feature coefficients in the elastic net penalty. In our simulations, fwelnet outperforms the lasso in terms of test mean squared error and usually gives an improvement in true positive rate or false positive rate for feature selection. We also apply this method to early prediction of preeclampsia, where fwelnet outperforms the lasso in terms of 10-fold cross-validated area under the curve (0.86 vs. 0.80). We also provide a connection between fwelnet and the group lasso and suggest how fwelnet might be used for multi-task learning.

  View details for DOI 10.5705/ss.202020.0226

  View details for PubMedID 37102071

• Shorter maternal leukocyte telomere length following cesarean birth: Implications for future research Panelli, D. M., Mayo, J. A., Wong, R. J., Becker, M., Maric, I., Wu, E., Gotlib, I. H., Aghaeepour, N., Druzin, M. L., Stevenson, D. K., Shaw, G. M., Bianco, K. MOSBY-ELSEVIER. 2023: S456-S457

  View details for Web of Science ID 000909337401260

• In-Silico Generation of High-Dimensional Immune Response Data in Patients using a Deep Neural Network. Cytometry. Part A : the journal of the International Society for Analytical Cytology Fallahzadeh, R., Bidoki, N. H., Stelzer, I. A., Becker, M., Marić, I., Chang, A. L., Culos, A., Phongpreecha, T., Xenochristou, M., De Francesco, D., Espinosa, C., Berson, E., Verdonk, F., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2022

  Abstract

  Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1002/cyto.a.24709

  View details for PubMedID 36507780

• Early prediction and longitudinal modeling of preeclampsia from multiomics. Patterns (New York, N.Y.) Maric, I., Contrepois, K., Moufarrej, M. N., Stelzer, I. A., Feyaerts, D., Han, X., Tang, A., Stanley, N., Wong, R. J., Traber, G. M., Ellenberger, M., Chang, A. L., Fallahzadeh, R., Nassar, H., Becker, M., Xenochristou, M., Espinosa, C., De Francesco, D., Ghaemi, M. S., Costello, E. K., Culos, A., Ling, X. B., Sylvester, K. G., Darmstadt, G. L., Winn, V. D., Shaw, G. M., Relman, D. A., Quake, S. R., Angst, M. S., Snyder, M. P., Stevenson, D. K., Gaudilliere, B., Aghaeepour, N. 2022; 3 (12): 100655

  Abstract

  Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC]= 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC= 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC= 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

  View details for DOI 10.1016/j.patter.2022.100655

  View details for PubMedID 36569558

• Author Correction: Prediction of gestational age using urinary metabolites in term and preterm pregnancies. Scientific reports Contrepois, K., Chen, S., Ghaemi, M. S., Wong, R. J., Jehan, F., Sazawal, S., Baqui, A. H., Stringer, J. S., Rahman, A., Nisar, M. I., Dhingra, U., Khanam, R., Ilyas, M., Dutta, A., Mehmood, U., Deb, S., Hotwani, A., Ali, S. M., Rahman, S., Nizar, A., Ame, S. M., Muhammad, S., Chauhan, A., Khan, W., Raqib, R., Das, S., Ahmed, S., Hasan, T., Khalid, J., Juma, M. H., Chowdhury, N. H., Kabir, F., Aftab, F., Quaiyum, A., Manu, A., Yoshida, S., Bahl, R., Pervin, J., Price, J. T., Rahman, M., Kasaro, M. P., Litch, J. A., Musonda, P., Vwalika, B., Shaw, G., Stevenson, D. K., Aghaeepour, N., Snyder, M. P. 2022; 12 (1): 19753

  View details for DOI 10.1038/s41598-022-23715-7

  View details for PubMedID 36396676

• The Childhood Acute Illness and Nutrition (CHAIN) network nested case-cohort study protocol: a multi-omics approach to understanding mortality among children in sub-Saharan Africa and South Asia. Gates open research Njunge, J. M., Tickell, K., Diallo, A. H., Sayeem Bin Shahid, A. S., Gazi, M. A., Saleem, A., Kazi, Z., Ali, S., Tigoi, C., Mupere, E., Lancioni, C. L., Yoshioka, E., Chisti, M. J., Mburu, M., Ngari, M., Ngao, N., Gichuki, B., Omer, E., Gumbi, W., Singa, B., Bandsma, R., Ahmed, T., Voskuijl, W., Williams, T. N., Macharia, A., Makale, J., Mitchel, A., Williams, J., Gogain, J., Janjic, N., Mandal, R., Wishart, D. S., Wu, H., Xia, L., Routledge, M., Gong, Y. Y., Espinosa, C., Aghaeepour, N., Liu, J., Houpt, E., Lawley, T. D., Browne, H., Shao, Y., Rwigi, D., Kariuki, K., Kaburu, T., Uhlig, H. H., Gartner, L., Jones, K., Koulman, A., Walson, J., Berkley, J. 2022; 6: 77

  Abstract

  Introduction: Many acutely ill children in low- and middle-income settings have a high risk of mortality both during and after hospitalisation despite guideline-based care. Understanding the biological mechanisms underpinning mortality may suggest optimal pathways to target for interventions to further reduce mortality. The Childhood Acute Illness and Nutrition (CHAIN) Network ( www.chainnnetwork.org) Nested Case-Cohort Study (CNCC) aims to investigate biological mechanisms leading to inpatient and post-discharge mortality through an integrated multi-omic approach. Methods and analysis; The CNCC comprises a subset of participants from the CHAIN cohort (1278/3101 hospitalised participants, including 350 children who died and 658 survivors, and 270/1140 well community children of similar age and household location) from nine sites in six countries across sub-Saharan Africa and South Asia. Systemic proteome, metabolome, lipidome, lipopolysaccharides, haemoglobin variants, toxins, pathogens, intestinal microbiome and biomarkers of enteropathy will be determined. Computational systems biology analysis will include machine learning and multivariate predictive modelling with stacked generalization approaches accounting for the different characteristics of each biological modality. This systems approach is anticipated to yield mechanistic insights, show interactions and behaviours of the components of biological entities, and help develop interventions to reduce mortality among acutely ill children. Ethics and dissemination. The CHAIN Network cohort and CNCC was approved by institutional review boards of all partner sites. Results will be published in open access, peer reviewed scientific journals and presented to academic and policy stakeholders. Data will be made publicly available, including uploading to recognised omics databases. Trial registration NCT03208725.

  View details for DOI 10.12688/gatesopenres.13635.2

  View details for PubMedID 36415883

  View details for PubMedCentralID PMC9646488

• Omics approaches: interactions at the maternal-fetal interface and origins of child health and disease. Pediatric research Ozen, M., Aghaeepour, N., Maric, I., Wong, R. J., Stevenson, D. K., Jantzie, L. L. 2022

  Abstract

  Immunoperinatology is an emerging field. Transdisciplinary efforts by physicians, physician-scientists, basic science researchers, and computational biologists have made substantial advancements by identifying unique immunologic signatures of specific diseases, discovering innovative preventative or treatment strategies, and establishing foundations for individualized neonatal intensive care of the most vulnerable neonates. In this review, we summarize the immunobiology and immunopathology of pregnancy, highlight omics approaches to study the maternal-fetal interface, and their contributions to pregnancy health. We examined the importance of transdisciplinary, multiomic (such as genomics, transcriptomics, proteomics, metabolomics, and immunomics) and machine-learning strategies in unraveling the mechanisms of adverse pregnancy, neonatal, and childhood outcomes and how they can guide the development of novel therapies to improve maternal and neonatal health. IMPACT: Discuss immunoperinatology research from the lens of omics and machine-learning approaches. Identify opportunities for omics-based approaches to delineate infection/inflammation-associated maternal, neonatal, and later life adverse outcomes (e.g., histologic chorioamnionitis [HCA]).

  View details for DOI 10.1038/s41390-022-02335-x

  View details for PubMedID 36216868

• LEVERAGING ELECTRONIC HEALTH RECORD DATA TO IDENTIFY PHENOTYPES ASSOCIATED WITH PREGNANCY LOSS MAY LEAD TO IMPROVED UNDERSTANDING OF RECURRENT PREGNANCY LOSS Roger, J., Tang, A., Woldemariam, S., Oskotsky, T., Wen, T., Liu, J., Kosti, I., Le, B., Cakmak, H., Snyder, M., Aghaeepour, N., Shaw, G., Stevenson, D., Giudice, L. C., Glymour, M., Rajkovic, A., Lathi, R., Sirota, M. ELSEVIER SCIENCE INC. 2022: E107

  View details for Web of Science ID 000891804600262

• Multiomics, artificial intelligence, and precision medicine in perinatology. Pediatric research Pammi, M., Aghaeepour, N., Neu, J. 2022

  Abstract

  Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

  View details for DOI 10.1038/s41390-022-02181-x

  View details for PubMedID 35804156

• Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19. Cell reports. Medicine Feyaerts, D., Hédou, J., Gillard, J., Chen, H., Tsai, E. S., Peterson, L. S., Ando, K., Manohar, M., Do, E., Dhondalay, G. K., Fitzpatrick, J., Artandi, M., Chang, I., Snow, T. T., Chinthrajah, R. S., Warren, C. M., Wittman, R., Meyerowitz, J. G., Ganio, E. A., Stelzer, I. A., Han, X., Verdonk, F., Gaudillière, D. K., Mukherjee, N., Tsai, A. S., Rumer, K. K., Jacobsen, D. R., Bjornson-Hooper, Z. B., Jiang, S., Saavedra, S. F., Valdés Ferrer, S. I., Kelly, J. D., Furman, D., Aghaeepour, N., Angst, M. S., Boyd, S. D., Pinsky, B. A., Nolan, G. P., Nadeau, K. C., Gaudillière, B., McIlwain, D. R. 2022: 100680

  Abstract

  The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

  View details for DOI 10.1016/j.xcrm.2022.100680

  View details for PubMedID 35839768

• HMOX1 Genetic Polymorphisms Display Ancestral Diversity and May Be Linked to Hypertensive Disorders in Pregnancy. Reproductive sciences (Thousand Oaks, Calif.) Sun, T., Cruz, G. I., Mousavi, N., Maric, I., Brewer, A., Wong, R. J., Aghaeepour, N., Sayed, N., Wu, J. C., Stevenson, D. K., Leonard, S. A., Gymrek, M., Winn, V. D. 2022

  Abstract

  Racial disparity exists for hypertensive disorders in pregnancy (HDP), which leads to disparate morbidity and mortality worldwide. The enzyme heme oxygenase-1 (HO-1) is encoded by HMOX1, which has genetic polymorphisms in its regulatory region that impact its expression and activity and have been associated with various diseases. However, studies of these genetic variants in HDP have been limited. The objective of this study was to examine HMOX1 as a potential genetic contributor of ancestral disparity seen in HDP. First, the 1000 Genomes Project (1KG) phase 3 was utilized to compare the frequencies of alleles, genotypes, and estimated haplotypes of guanidine thymidine repeats (GTn; containing rs3074372) and A/T SNP (rs2071746) among females from five ancestral populations (Africa, theAmericas, Europe, East Asia, and South Asia, N=1271). Then, using genomic DNA from women with a history of HDP, we explored the possibility of HMOX1 variants predisposing women to HDP (N=178) compared with an equivalent ancestral group from 1KG (N=263). Both HMOX1 variants were distributed differently across ancestries, with African women having a distinct distribution and an overall higher prevalence of the variants previously associated with lower HO-1 expression. The two HMOX1 variants display linkage disequilibrium in all but the African group, and within EUR cohort, LL and AA individuals have a higher prevalence in HDP. HMOX1 variants demonstrate ancestral differences that may contribute to racial disparity in HDP. Understanding maternal genetic contribution to HDP will help improve prediction and facilitate personalized approaches to care for HDP.

  View details for DOI 10.1007/s43032-022-01001-1

  View details for PubMedID 35697922

• Signature for Pain Recovery IN Teens (SPRINT): protocol for a multisite prospective signature study in chronic musculoskeletal pain. BMJ open Simons, L., Moayedi, M., Coghill, R. C., Stinson, J., Angst, M. S., Aghaeepour, N., Gaudilliere, B., King, C. D., López-Solà, M., Hoeppli, M. E., Biggs, E., Ganio, E., Williams, S. E., Goldschneider, K. R., Campbell, F., Ruskin, D., Krane, E. J., Walker, S., Rush, G., Heirich, M. 2022; 12 (6): e061548

  Abstract

  Current treatments for chronic musculoskeletal (MSK) pain are suboptimal. Discovery of robust prognostic markers separating patients who recover from patients with persistent pain and disability is critical for developing patient-specific treatment strategies and conceiving novel approaches that benefit all patients. Given that chronic pain is a biopsychosocial process, this study aims to discover and validate a robust prognostic signature that measures across multiple dimensions in the same adolescent patient cohort with a computational analysis pipeline. This will facilitate risk stratification in adolescent patients with chronic MSK pain and more resourceful allocation of patients to costly and potentially burdensome multidisciplinary pain treatment approaches.Here we describe a multi-institutional effort to collect, curate and analyse a high dimensional data set including epidemiological, psychometric, quantitative sensory, brain imaging and biological information collected over the course of 12 months. The aim of this effort is to derive a multivariate model with strong prognostic power regarding the clinical course of adolescent MSK pain and function.The study complies with the National Institutes of Health policy on the use of a single internal review board (sIRB) for multisite research, with Cincinnati Children's Hospital Medical Center Review Board as the reviewing IRB. Stanford's IRB is a relying IRB within the sIRB. As foreign institutions, the University of Toronto and The Hospital for Sick Children (SickKids) are overseen by their respective ethics boards. All participants provide signed informed consent. We are committed to open-access publication, so that patients, clinicians and scientists have access to the study data and the signature(s) derived. After findings are published, we will upload a limited data set for sharing with other investigators on applicable repositories.NCT04285112.

  View details for DOI 10.1136/bmjopen-2022-061548

  View details for PubMedID 35676017

• Prediction of gestational age using urinary metabolites in term and preterm pregnancies. Scientific reports Contrepois, K., Chen, S., Ghaemi, M. S., Wong, R. J., Alliance for Maternal and Newborn Health Improvement (AMANHI), Global Alliance to Prevent Prematurity and Stillbirth (GAPPS), Shaw, G., Stevenson, D. K., Aghaeepour, N., Snyder, M. P., Jehan, F., Sazawal, S., Baqui, A. H., Nisar, M. I., Dhingra, U., Khanam, R., Ilyas, M., Dutta, A., Mehmood, U., Deb, S., Hotwani, A., Ali, S. M., Rahman, S., Nizar, A., Ame, S. M., Muhammad, S., Chauhan, A., Khan, W., Raqib, R., Das, S., Ahmed, S., Hasan, T., Khalid, J., Juma, M. H., Chowdhury, N. H., Kabir, F., Aftab, F., Quaiyum, M. A., Manu, A., Yoshida, S., Bahl, R., Rahman, A., Pervin, J., Price, J. T., Rahman, M., Kasaro, M. P., Litch, J. A., Musonda, P., Vwalika, B., Stringer, J. S. 2022; 12 (1): 8033

  Abstract

  Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n=99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC-MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho=0.87, RMSE=1.58weeks) that was validated in an independent cohort (n=20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.

  View details for DOI 10.1038/s41598-022-11866-6

  View details for PubMedID 35577875

• Leukocyte telomere dynamics across gestation in uncomplicated pregnancies and associations with stress. BMC pregnancy and childbirth Panelli, D. M., Leonard, S. A., Wong, R. J., Becker, M., Mayo, J. A., Wu, E., Girsen, A. I., Gotlib, I. H., Aghaeepour, N., Druzin, M. L., Shaw, G. M., Stevenson, D. K., Bianco, K. 2022; 22 (1): 381

  Abstract

  Short leukocyte telomere length is a biomarker associated with stress and morbidity in non-pregnant adults. Little is known, however, about maternal telomere dynamics in pregnancy. To address this, we examined changes in maternal leukocyte telomere length (LTL) during uncomplicated pregnancies and explored correlations with perceived stress.In this pilot study, maternal LTL was measured in blood collected from nulliparas who delivered live, term, singleton infants between 2012 and 2018 at a single institution. Participants were excluded if they had diabetes or hypertensive disease. Samples were collected over the course of pregnancy and divided into three time periods: < 200/7 weeks (Timepoint 1); 201/7 to 366/7 weeks (Timepoint 2); and 370/7 to 9-weeks postpartum (Timepoint 3). All participants also completed a survey assessing a multivariate profile of perceived stress at the time of enrollment in the first trimester. LTL was measured using quantitative polymerase chain reaction (PCR). Wilcoxon signed-rank tests were used to compare LTL differences within participants across all timepoint intervals. To determine whether mode of delivery affected LTL, we compared postpartum Timepoint 3 LTLs between participants who had vaginal versus cesarean birth. Secondarily, we evaluated the association of the assessed multivariate stress profile and LTL using machine learning analysis.A total of 115 samples from 46 patients were analyzed. LTL (mean ± SD), expressed as telomere to single copy gene (T/S) ratios, were: 1.15 ± 0.26, 1.13 ± 0.23, and 1.07 ± 0.21 for Timepoints 1, 2, and 3, respectively. There were no significant differences in LTL between Timepoints 1 and 2 (LTL T/S change - 0.03 ± 0.26, p = 0.39); 2 and 3 (- 0.07 ± 0.29, p = 0.38) or Timepoints 1 and 3 (- 0.07 ± 0.21, p = 0.06). Participants who underwent cesareans had significantly shorter postpartum LTLs than those who delivered vaginally (T/S ratio: 0.94 ± 0.12 cesarean versus 1.12 ± 0.21 vaginal, p = 0.01). In secondary analysis, poor sleep quality was the main stress construct associated with shorter Timepoint 1 LTLs (p = 0.02) and shorter mean LTLs (p = 0.03).In this cohort of healthy pregnancies, maternal LTLs did not significantly change across gestation and postpartum LTLs were shorter after cesarean than after vaginal birth. Significant associations between sleep quality and short LTLs warrant further investigation.

  View details for DOI 10.1186/s12884-022-04693-0

  View details for PubMedID 35501726

• Integrated single-cell and plasma proteomic modeling to predict surgical site complications, a prospective cohort study Tsai, A. S., Hedou, J., Einhaus, J., Rumer, K., Verdonk, F., Stanley, N., Choisy, B., Ganio, E. A., Bonham, A., Jacobsen, D., Warrington, B., Gao, X., Tingle, M., McAllister, T., Fallahzadeh, R., Feyaerts, D., Stelzer, I., Gaudilliere, D., Ando, K., Shelton, A., Morris, A., Kebebew, E., Aghaeepour, N., Kin, C., Angst, M. S., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2022: 1204-1205

  View details for Web of Science ID 000840283001162

• An immune signature of postoperative cognitive dysfunction (POCD) Verdonk, F., Tsai, A. S., Hedou, J., Heifets, B. D., Gaudilliere, D., Bellan, G., Sharshar, T., Gaillard, R., Molliex, S., Feyaerts, D., Stelzer, I., Ganio, E. A., Sato, M., Bonham, A., Ando, K., Aghaeepour, N., Angst, M. S., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2022: 577-578

  View details for Web of Science ID 000840283000229

• A data-driven health index for neonatal morbidities. iScience De Francesco, D., Blumenfeld, Y. J., Maric, I., Mayo, J. A., Chang, A. L., Fallahzadeh, R., Phongpreecha, T., Butwick, A. J., Xenochristou, M., Phibbs, C. S., Bidoki, N. H., Becker, M., Culos, A., Espinosa, C., Liu, Q., Sylvester, K. G., Gaudilliere, B., Angst, M. S., Stevenson, D. K., Shaw, G. M., Aghaeepour, N. 2022; 25 (4): 104143

  Abstract

  Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.

  View details for DOI 10.1016/j.isci.2022.104143

  View details for PubMedID 35402862

• Increases in ambient air pollutants during pregnancy are linked to increases in methylation of IL4, IL10, and IFNgamma. Clinical epigenetics Aguilera, J., Han, X., Cao, S., Balmes, J., Lurmann, F., Tyner, T., Lutzker, L., Noth, E., Hammond, S. K., Sampath, V., Burt, T., Utz, P. J., Khatri, P., Aghaeepour, N., Maecker, H., Prunicki, M., Nadeau, K. 2022; 14 (1): 40

  Abstract

  BACKGROUND: Ambient air pollutant (AAP) exposure is associated with adverse pregnancy outcomes, such as preeclampsia, preterm labor, and low birth weight. Previous studies have shown methylation of immune genes associate with exposure to air pollutants in pregnant women, but the cell-mediated response in the context of typical pregnancy cell alterations has not been investigated. Pregnancy causes attenuation in cell-mediated immunity with alterations in the Th1/Th2/Th17/Treg environment, contributing to maternal susceptibility. We recruited women (n=186) who were 20weeks pregnant from Fresno, CA, an area with chronically elevated AAP levels. Associations of average pollution concentration estimates for 1week, 1month, 3months, and 6months prior to blood draw were associated with Th cell subset (Th1, Th2, Th17, and Treg) percentages and methylation of CpG sites (IL4, IL10, IFNgamma, and FoxP3). Linear regression models were adjusted for weight, age, season, race, and asthma, using a Q value as the false-discovery-rate-adjusted p-value across all genes.RESULTS: Short-term and mid-term AAP exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2) carbon monoxide (CO), and polycyclic aromatic hydrocarbons (PAH456) were associated with percentages of immune cells. A decrease in Th1 cell percentage was negatively associated with PM2.5 (1 mo/3 mo: Q<0.05), NO2 (1 mo/3 mo/6 mo: Q<0.05), and PAH456 (1week/1 mo/3 mo: Q<0.05). Th2 cell percentages were negatively associated with PM2.5 (1week/1 mo/3 mo/6 mo: Q<0.06), and NO2 (1week/1 mo/3 mo/6 mo: Q<0.06). Th17 cell percentage was negatively associated with NO2 (3 mo/6 mo: Q<0.01), CO (1week/1 mo: Q<0.1), PM2.5 (3 mo/6 mo: Q<0.05), and PAH456 (1 mo/3 mo/6 mo: Q<0.08). Methylation of the IL10 gene was positively associated with CO (1week/1 mo/3 mo: Q<0.01), NO2 (1 mo/3 mo/6 mo: Q<0.08), PAH456 (1week/1 mo/3 mo: Q<0.01), and PM2.5 (3 mo: Q=0.06) while IL4 gene methylation was positively associated with concentrations of CO (1week/1 mo/3 mo/6 mo: Q<0.09). Also, IFNgamma gene methylation was positively associated with CO (1week/1 mo/3 mo: Q<0.05) and PAH456 (1week/1 mo/3 mo: Q<0.06).CONCLUSION: Exposure to several AAPs was negatively associated with T-helper subsets involved in pro-inflammatory and anti-inflammatory responses during pregnancy. Methylation of IL4, IL10, and IFNgamma genes with pollution exposure confirms previous research. These results offer insights into the detrimental effects of air pollution during pregnancy, the demand for more epigenetic studies, and mitigation strategies to decrease pollution exposure during pregnancy.

  View details for DOI 10.1186/s13148-022-01254-2

  View details for PubMedID 35287715

• Multiomics Modeling of Preterm Birth in Low- and Middle-Income Countries Espinosa Bernal, C. A., Shaw, G. M., Stevenson, D. K., Aghaeepour, N., MOMI Consortium SPRINGER HEIDELBERG. 2022: 49-50

  View details for Web of Science ID 000762765300020

• Depression And Not Cognitive Ability Is Most Strongly Associated With Long-term Functional Outcomes Following Stroke. Drag, L. L., Musabbir, M., Mlynash, M., Mendez, M. P., Kim, D. K., Aghaeepour, N., Lansberg, M. G., Buckwalter, M. S. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for DOI 10.1161/str.53.suppl_1.TP9

  View details for Web of Science ID 000788100600297

• MULTIOMICS LONGITUDINAL MODELING OF PREECLAMPTIC PREGNANCIES Espinosa, C., Maric, I., Contrepois, K., Moufarrej, M., Stelzer, I. S., Feyaerts, D., Han, X., Tang, A., Wong, R. J., Darmstadt, G. L., Winn, V. D., Shaw, G. M., Relman, D. A., Quake, S. R., Angst, M. S., Snyder, M., Stevenson, D. K., Gaudilliere, B., Aghaeepour, N. BMJ PUBLISHING GROUP. 2022: 309

  View details for DOI 10.1136/jim-2022-WRMC.400

  View details for Web of Science ID 000737295900421

• Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning. Frontiers in pediatrics Becker, M., Dai, J., Chang, A. L., Feyaerts, D., Stelzer, I. A., Zhang, M., Berson, E., Saarunya, G., De Francesco, D., Espinosa, C., Kim, Y., Maric, I., Mataraso, S., Payrovnaziri, S. N., Phongpreecha, T., Ravindra, N. G., Shome, S., Tan, Y., Thuraiappah, M., Xue, L., Mayo, J. A., Quaintance, C. C., Laborde, A., King, L. S., Dhabhar, F. S., Gotlib, I. H., Wong, R. J., Angst, M. S., Shaw, G. M., Stevenson, D. K., Gaudilliere, B., Aghaeepour, N. 2022; 10: 933266

  Abstract

  Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.Objectives: The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and Methods: In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).Results: Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.Conclusions: Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.

  View details for DOI 10.3389/fped.2022.933266

  View details for PubMedID 36582513

• Maternal stress and its consequences - biological strain. American journal of perinatology Stevenson, D. K., Gotlib, I. H., Buthmann, J. L., Maric, I., Aghaeepour, N., Gaudilliere, B., Angst, M. S., Darmstadt, G. L., Druzin, M. L., Wong, R. J., Shaw, G. M., Katz, M. 2022

  Abstract

  Understanding the role of stress in pregnancy and its consequences is important, particularly given documented associations between maternal stress and preterm birth and other pathologic outcomes. Physical and psychological stressors can elicit the same biological responses, known as biological strain. Chronic stressors, like poverty and racism (race-based discriminatory treatment), may create a legacy or trajectory of biological strain that no amount of coping can relieve in the absence of larger-scale socio-behavioral or societal changes. An integrative approach that takes into consideration simultaneously social and biological determinants of stress may provide the best insights into risk for preterm birth. The most successful computational approaches and the most predictive machine-learning models are likely to be those that combine information about the stressors and the biological strain (for example, as measured by different omics) experienced during pregnancy.

  View details for DOI 10.1055/a-1798-1602

  View details for PubMedID 35292943

• Perinatal infection, inflammation, preterm birth, and brain injury: A review with proposals for future investigations. Experimental neurology Reiss, J. D., Peterson, L. S., Nesamoney, S. N., Chang, A. L., Pasca, A. M., Marić, I., Shaw, G. M., Gaudilliere, B., Wong, R. J., Sylvester, K. G., Bonifacio, S. L., Aghaeepour, N., Gibbs, R. S., Stevenson, D. K. 2022: 113988

  Abstract

  Preterm newborns are exposed to several risk factors for developing brain injury. Clinical studies have suggested that the presence of intrauterine infection is a consistent risk factor for preterm birth and white matter injury. Animal models have confirmed these associations by identifying inflammatory cascades originating at the maternofetal interface that penetrate the fetal blood-brain barrier and result in brain injury. Acquired diseases of prematurity further potentiate the risk for cerebral injury. Systems biology approaches incorporating ante- and post-natal risk factors and analyzing omic and multiomic data using machine learning are promising methodologies for further elucidating biologic mechanisms of fetal and neonatal brain injury.

  View details for DOI 10.1016/j.expneurol.2022.113988

  View details for PubMedID 35081400

• Cellular aging and pregnancy complications: Examining maternal leukocyte telomere length in two diverse cohorts. Panelli, D. M., Wang, X., Wong, R. J., Cruz, G., Hong, X., Aghaeepour, N., Druzin, M. L., Shaw, G. M., Zuckerman, B. S., Stevenson, D. K., Bianco, K. MOSBY-ELSEVIER. 2022: S646

  View details for Web of Science ID 000737459401371

• MULTIOMICS MODELING OF PRETERM BIRTH IN LOW- AND MIDDLE-INCOME COUNTRIES Bernal, C., Maric, I., Stevenson, D. K., Aghaeepour, N. BMJ PUBLISHING GROUP. 2022: 310-311

  View details for DOI 10.1136/jim-2022-WRMC.403

  View details for Web of Science ID 000737295900424

• Integrated Single-Cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study. Annals of surgery Rumer, K. K., Hedou, J., Tsai, A., Einhaus, J., Verdonk, F., Stanley, N., Choisy, B., Ganio, E., Bonham, A., Jacobsen, D., Warrington, B., Gao, X., Tingle, M., McAllister, T. N., Fallahzadeh, R., Feyaerts, D., Stelzer, I., Gaudilliere, D., Ando, K., Shelton, A., Morris, A., Kebebew, E., Aghaeepour, N., Kin, C., Angst, M. S., Gaudilliere, B. 1800

  Abstract

  OBJECTIVE: The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery.SUMMARY BACKGROUND DATA: SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs.METHODS: Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery.RESULTS: A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82).CONCLUSIONS: The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.

  View details for DOI 10.1097/SLA.0000000000005348

  View details for PubMedID 34954754

• Single-synapse analyses of Alzheimer's disease implicate pathologic tau, DJ1, CD47, and ApoE. Science advances Phongpreecha, T., Gajera, C. R., Liu, C. C., Vijayaragavan, K., Chang, A. L., Becker, M., Fallahzadeh, R., Fernandez, R., Postupna, N., Sherfield, E., Tebaykin, D., Latimer, C., Shively, C. A., Register, T. C., Craft, S., Montine, K. S., Fox, E. J., Poston, K. L., Keene, C. D., Angelo, M., Bendall, S. C., Aghaeepour, N., Montine, T. J. 1800; 7 (51): eabk0473

  Abstract

  [Figure: see text].

  View details for DOI 10.1126/sciadv.abk0473

  View details for PubMedID 34910503

• Harnessing the Potential of Multiomics Studies for Precision Medicine in Infectious Disease. Open forum infectious diseases Ward, R. A., Aghaeepour, N., Bhattacharyya, R. P., Clish, C. B., Gaudilliere, B., Hacohen, N., Mansour, M. K., Mudd, P. A., Pasupneti, S., Presti, R. M., Rhee, E. P., Sen, P., Spec, A., Tam, J. M., Villani, A., Woolley, A. E., Hsu, J. L., Vyas, J. M. 2021; 8 (11): ofab483

  Abstract

  The field of infectious diseases currently takes a reactive approach and treats infections as they present in patients. Although certain populations are known to be at greater risk of developing infection (eg, immunocompromised), we lack a systems approach to define the true risk of future infection for a patient. Guided by impressive gains in "omics" technologies, future strategies to infectious diseases should take a precision approach to infection through identification of patients at intermediate and high-risk of infection and deploy targeted preventative measures (ie, prophylaxis). The advances of high-throughput immune profiling by multiomics approaches (ie, transcriptomics, epigenomics, metabolomics, proteomics) hold the promise to identify patients at increased risk of infection and enable risk-stratifying approaches to be applied in the clinic. Integration of patient-specific data using machine learning improves the effectiveness of prediction, providing the necessary technologies needed to propel the field of infectious diseases medicine into the era of personalized medicine.

  View details for DOI 10.1093/ofid/ofab483

  View details for PubMedID 34805429

• Newborn screen metabolic panels reflect the impact of common disorders of pregnancy. Pediatric research Reiss, J. D., Chang, A. L., Mayo, J. A., Bianco, K., Lee, H. C., Stevenson, D. K., Shaw, G. M., Aghaeepour, N., Sylvester, K. G. 2021

  Abstract

  BACKGROUND: Hypertensive disorders of pregnancy and maternal diabetes profoundly affect fetal and newborn growth, yet disturbances in intermediate metabolism and relevant mediators of fetal growth alterations remain poorly defined. We sought to determine whether there are distinct newborn screen metabolic patterns among newborns affected by maternal hypertensive disorders or diabetes in utero.METHODS: A retrospective observational study investigating distinct newborn screen metabolites in conjunction with data linked to birth and hospitalization records in the state of California between 2005 and 2010.RESULTS: A total of 41,333 maternal-infant dyads were included. Infants of diabetic mothers demonstrated associations with short-chain acylcarnitines and free carnitine. Infants born to mothers with preeclampsia with severe features and chronic hypertension with superimposed preeclampsia had alterations in acetylcarnitine, free carnitine, and ornithine levels. These results were further accentuated by size for gestational age designations.CONCLUSIONS: Infants of diabetic mothers demonstrate metabolic signs of incomplete beta oxidation and altered lipid metabolism. Infants of mothers with hypertensive disorders of pregnancy carry analyte signals that may reflect oxidative stress via altered nitric oxide signaling. The newborn screen analyte composition is influenced by the presence of these maternal conditions and is further associated with the newborn size designation at birth.IMPACT: Substantial differences in newborn screen analyte profiles were present based on the presence or absence of maternal diabetes or hypertensive disorder of pregnancy and this finding was further influenced by the newborn size designation at birth. The metabolic health of the newborn can be examined using the newborn screen and is heavily impacted by the condition of the mother during pregnancy. Utilizing the newborn screen to identify newborns affected by common conditions of pregnancy may help relate an infant's underlying biological disposition with their clinical phenotype allowing for greater risk stratification and intervention.

  View details for DOI 10.1038/s41390-021-01753-7

  View details for PubMedID 34671094

• Black swans and ambitious overgeneralization in newborn intensive care. Pediatric research Stevenson, D. K., Wong, R. J., Shaw, G. M., Aghaeepour, N., Maric, I., Prince, L. S., Reiss, J. D., Katz, M. 2021

  View details for DOI 10.1038/s41390-021-01771-5

  View details for PubMedID 34601493

• Multi-Omic, Longitudinal Profile of Third-Trimester Pregnancies Identifies a Molecular Switch That Predicts the Onset of Labor. Stelzer, I., Ghaemi, M., Han, X., Ando, K., Hedou, J., Feyaerts, D., Peterson, L., Ganio, E., Tsai, A., Tsai, E., Rumer, K., Stanley, N., Fallazadeh, R., Becker, M., Culos, A., Gaudilliere, D., Wong, R., Winn, V., Shaw, G., Snyder, M., Stevenson, D., Contrepois, K., Angst, M., Aghaeepour, N., Gaudilliere, B. SPRINGER HEIDELBERG. 2021: 233A-234A

  View details for Web of Science ID 000675441000486

• Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth. Cell reports. Medicine Tarca, A. L., Pataki, B. A., Romero, R., Sirota, M., Guan, Y., Kutum, R., Gomez-Lopez, N., Done, B., Bhatti, G., Yu, T., Andreoletti, G., Chaiworapongsa, T., DREAM Preterm Birth Prediction Challenge Consortium, Hassan, S. S., Hsu, C., Aghaeepour, N., Stolovitzky, G., Csabai, I., Costello, J. C. 2021; 2 (6): 100323

  Abstract

  Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r= 0.83) and, using data collected before 37weeks of gestation, also predicts the delivery date in both normal pregnancies (r=0.86) and those with spontaneous preterm birth (r= 0.75). Based on samples collected before 33weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC= 0.76 versus AUROC= 0.6 at 27-33weeks of gestation).

  View details for DOI 10.1016/j.xcrm.2021.100323

  View details for PubMedID 34195686

• Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset. Science translational medicine Stelzer, I. A., Ghaemi, M. S., Han, X., Ando, K., Hedou, J. J., Feyaerts, D., Peterson, L. S., Rumer, K. K., Tsai, E. S., Ganio, E. A., Gaudilliere, D. K., Tsai, A. S., Choisy, B., Gaigne, L. P., Verdonk, F., Jacobsen, D., Gavasso, S., Traber, G. M., Ellenberger, M., Stanley, N., Becker, M., Culos, A., Fallahzadeh, R., Wong, R. J., Darmstadt, G. L., Druzin, M. L., Winn, V. D., Gibbs, R. S., Ling, X. B., Sylvester, K., Carvalho, B., Snyder, M. P., Shaw, G. M., Stevenson, D. K., Contrepois, K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. 2021; 13 (592)

  Abstract

  Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 * 10-40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 * 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

  View details for DOI 10.1126/scitranslmed.abd9898

  View details for PubMedID 33952678

• High-dimensional profiling clusters asthma severity by lymphoid and non-lymphoid status. Cell reports Camiolo, M. J., Zhou, X., Oriss, T. B., Yan, Q., Gorry, M., Horne, W., Trudeau, J. B., Scholl, K., Chen, W., Kolls, J. K., Ray, P., Weisel, F. J., Weisel, N. M., Aghaeepour, N., Nadeau, K., Wenzel, S. E., Ray, A. 2021; 35 (2): 108974

  Abstract

  Clinical definitions of asthma fail to capture the heterogeneity of immune dysfunction in severe, treatment-refractory disease. Applying mass cytometry and machine learning to bronchoalveolar lavage (BAL) cells, we find that corticosteroid-resistant asthma patients cluster largely into two groups: one enriched in interleukin (IL)-4+ innate immune cells and another dominated by interferon (IFN)-gamma+ Tcells, including tissue-resident memory cells. In contrast, BAL cells of a healthier population are enriched in IL-10+ macrophages. To better understand cellular mediators of severe asthma, we developed the Immune Cell Linkage through Exploratory Matrices (ICLite) algorithm to perform deconvolution of bulk RNA sequencing of mixed-cell populations. Signatures of mitosis and IL-7 signaling in CD206-FcepsilonRI+CD127+IL-4+ innate cells in one patient group, contrasting with adaptive immune response in Tcells in the other, are preserved across technologies. Transcriptional signatures uncovered by ICLite identify T-cell-high and T-cell-poor severe asthma patients in an independent cohort, suggesting broad applicability of our findings.

  View details for DOI 10.1016/j.celrep.2021.108974

  View details for PubMedID 33852838

• Understanding how biologic and social determinants affect disparities in preterm birth and outcomes of preterm infants in the NICU. Seminars in perinatology Stevenson, D. K., Aghaeepour, N., Maric, I., Angst, M. S., Darmstadt, G. L., Druzin, M. L., Gaudilliere, B., Ling, X. B., Moufarrej, M. N., Peterson, L. S., Quake, S. R., Relman, D. A., Snyder, M. P., Sylvester, K. G., Shaw, G. M., Wong, R. J. 2021: 151408

  Abstract

  To understand the disparities in spontaneous preterm birth (sPTB) and/or its outcomes, biologic and social determinants as well as healthcare practice (such as those in neonatal intensive care units) should be considered. They have been largely intractable and remain obscure in most cases, despite a myriad of identified risk factors for and causes of sPTB. We still do not know how they might actually affect and lead to the different outcomes at different gestational ages and if they are independent of NICU practices. Here we describe an integrated approach to study the interplay between the genome and exposome, which may drive biochemistry and physiology, with health disparities.

  View details for DOI 10.1016/j.semperi.2021.151408

  View details for PubMedID 33875265

• Predicting Post-Liver Transplant Outcomes-Rise of the Machines or a Foggy Crystal Ball? Journal of cardiothoracic and vascular anesthesia Vanneman, M. W., Fielding-Singh, V., Aghaeepour, N. 2021

  View details for DOI 10.1053/j.jvca.2021.03.012

  View details for PubMedID 33846080

• Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19. bioRxiv : the preprint server for biology Feyaerts, D., Hédou, J., Gillard, J., Chen, H., Tsai, E. S., Peterson, L. S., Ando, K., Manohar, M., Do, E., Dhondalay, G. K., Fitzpatrick, J., Artandi, M., Chang, I., Snow, T. T., Chinthrajah, R. S., Warren, C. M., Wittman, R., Meyerowitz, J. G., Ganio, E. A., Stelzer, I. A., Han, X., Verdonk, F., Gaudillière, D. K., Mukherjee, N., Tsai, A. S., Rumer, K. K., Jiang, S., Valdés Ferrer, S. I., Kelly, J. D., Furman, D., Aghaeepour, N., Angst, M. S., Boyd, S. D., Pinsky, B. A., Nolan, G. P., Nadeau, K. C., Gaudillière, B., McIlwain, D. R. 2021

  Abstract

  The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression.

  View details for DOI 10.1101/2021.02.09.430269

  View details for PubMedID 33594362

  View details for PubMedCentralID PMC7885914

• Risk assessment analysis for maternal autoantibody-related autism (MAR-ASD): a subtype of autism. Molecular psychiatry Ramirez-Celis, A. n., Becker, M. n., Nuño, M. n., Schauer, J. n., Aghaeepour, N. n., Van de Water, J. n. 2021

  Abstract

  The incidence of autism spectrum disorder (ASD) has been rising, however ASD-risk biomarkers remain lacking. We previously identified the presence of maternal autoantibodies to fetal brain proteins specific to ASD, now termed maternal autoantibody-related (MAR) ASD. The current study aimed to create and validate a serological assay to identify ASD-specific maternal autoantibody patterns of reactivity against eight previously identified proteins (CRMP1, CRMP2, GDA, NSE, LDHA, LDHB, STIP1, and YBOX) that are highly expressed in developing brain, and determine the relationship of these reactivity patterns with ASD outcome severity. We used plasma from mothers of children diagnosed with ASD (n = 450) and from typically developing children (TD, n = 342) to develop an ELISA test for each of the protein antigens. We then determined patterns of reactivity a highly significant association with ASD, and discovered several patterns that were ASD-specific (18% in the training set and 10% in the validation set vs. 0% TD). The three main patterns associated with MAR ASD are CRMP1 + GDA (ASD% = 4.2 vs. TD% = 0, OR 31.04, p = <0.0001), CRMP1 + CRMP2 (ASD% = 3.6 vs. TD% = 0, OR 26.08, p = 0.0005) and NSE + STIP1 (ASD% = 3.1 vs. TD% = 0, OR 22.82, p = 0.0001). Additionally, we found that maternal autoantibody reactivity to CRMP1 significantly increases the odds of a child having a higher Autism Diagnostic Observation Schedule (ADOS) severity score (OR 2.3; 95% CI: 1.358-3.987, p = 0.0021). This is the first report that uses machine learning subgroup discovery to identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of risk for a subset of up to 18% of ASD cases in this study population.

  View details for DOI 10.1038/s41380-020-00998-8

  View details for PubMedID 33483694

• Deleterious and Protective Psychosocial and Stress-Related Factors Predict Risk of Spontaneous Preterm Birth. American journal of perinatology Becker, M. n., Mayo, J. A., Phogat, N. K., Quaintance, C. C., Laborde, A. n., King, L. n., Gotlib, I. H., Gaudilliere, B. n., Angst, M. S., Shaw, G. M., Stevenson, D. K., Aghaeepour, N. n., Dhabhar, F. S. 2021

  Abstract

   The aim of the study was to: (1) Identify (early in pregnancy) psychosocial and stress-related factors that predict risk of spontaneous preterm birth (PTB, gestational age <37 weeks); (2) Investigate whether "protective" factors (e.g., happiness/social support) decrease risk; (3) Use the Dhabhar Quick-Assessment Questionnaire for Stress and Psychosocial Factors™ (DQAQ-SPF™) to rapidly quantify harmful or protective factors that predict increased or decreased risk respectively, of PTB. This is a prospective cohort study. Relative risk (RR) analyses investigated association between individual factors and PTB. Machine learning-based interdependency analysis (IDPA) identified factor clusters, strength, and direction of association with PTB. A nonlinear model based on support vector machines was built for predicting PTB and identifying factors that most strongly predicted PTB. Higher levels of deleterious factors were associated with increased RR for PTB: General anxiety (RR = 8.9; 95% confidence interval or CI = 2.0,39.6), pain (RR = 5.7; CI = 1.7,17.0); tiredness/fatigue (RR = 3.7; CI = 1.09,13.5); perceived risk of birth complications (RR = 4; CI = 1.6,10.01); self-rated health current (RR = 2.6; CI = 1.0,6.7) and previous 3 years (RR = 2.9; CI = 1.1,7.7); and divorce (RR = 2.9; CI = 1.1,7.8). Lower levels of protective factors were also associated with increased RR for PTB: low happiness (RR = 9.1; CI = 1.25,71.5); low support from parents/siblings (RR = 3.5; CI = 0.9,12.9), and father-of-baby (RR = 3; CI = 1.1,9.9). These factors were also components of the clusters identified by the IDPA: perceived risk of birth complications (p < 0.05 after FDR correction), and general anxiety, happiness, tiredness/fatigue, self-rated health, social support, pain, and sleep (p < 0.05 without FDR correction). Supervised analysis of all factors, subject to cross-validation, produced a model highly predictive of PTB (AUROC or area under the receiver operating characteristic = 0.73). Model reduction through forward selection revealed that even a small set of factors (including those identified by RR and IDPA) predicted PTB. These findings represent an important step toward identifying key factors, which can be assessed rapidly before/after conception, to predict risk of PTB, and perhaps other adverse pregnancy outcomes. Quantifying these factors, before, or early in pregnancy, could identify women at risk of delivering preterm, pinpoint mechanisms/targets for intervention, and facilitate the development of interventions to prevent PTB.· Newly designed questionnaire used for rapid quantification of stress and psychosocial factors early during pregnancy.. · Deleterious factors predict increased preterm birth (PTB) risk.. · Protective factors predict decreased PTB risk..

  View details for DOI 10.1055/s-0041-1729162

  View details for PubMedID 34015838

• Human influenza virus challenge identifies cellular correlates of protection for oral vaccination. Cell host & microbe McIlwain, D. R., Chen, H., Rahil, Z., Bidoki, N. H., Jiang, S., Bjornson, Z., Kolhatkar, N. S., Martinez, C. J., Gaudillière, B., Hedou, J., Mukherjee, N., Schürch, C. M., Trejo, A., Affrime, M., Bock, B., Kim, K., Liebowitz, D., Aghaeepour, N., Tucker, S. N., Nolan, G. P. 2021

  Abstract

  Developing new influenza vaccines with improved performance and easier administration routes hinges on defining correlates of protection. Vaccine-elicited cellular correlates of protection for influenza in humans have not yet been demonstrated. A phase-2 double-blind randomized placebo and active (inactivated influenza vaccine) controlled study provides evidence that a human-adenovirus-5-based oral influenza vaccine tablet (VXA-A1.1) can protect from H1N1 virus challenge in humans. Mass cytometry characterization of vaccine-elicited cellular immune responses identified shared and vaccine-type-specific responses across B and T cells. For VXA-A1.1, the abundance of hemagglutinin-specific plasmablasts and plasmablasts positive for integrin α4β7, phosphorylated STAT5, or lacking expression of CD62L at day 8 were significantly correlated with protection from developing viral shedding following virus challenge at day 90 and contributed to an effective machine learning model of protection. These findings reveal the characteristics of vaccine-elicited cellular correlates of protection for an oral influenza vaccine.

  View details for DOI 10.1016/j.chom.2021.10.009

  View details for PubMedID 34784508

• Mortality Risk Among Patients With COVID-19 Prescribed Selective Serotonin Reuptake Inhibitor Antidepressants. JAMA network open Oskotsky, T., Maric, I., Tang, A., Oskotsky, B., Wong, R. J., Aghaeepour, N., Sirota, M., Stevenson, D. K. 2021; 4 (11): e2133090

  Abstract

  Antidepressant use may be associated with reduced levels of several proinflammatory cytokines suggested to be involved with the development of severe COVID-19. An association between the use of selective serotonin reuptake inhibitors (SSRIs)-specifically fluoxetine hydrochloride and fluvoxamine maleate-with decreased mortality among patients with COVID-19 has been reported in recent studies; however, these studies had limited power due to their small size.To investigate the association of SSRIs with outcomes in patients with COVID-19 by analyzing electronic health records (EHRs).This retrospective cohort study used propensity score matching by demographic characteristics, comorbidities, and medication indication to compare SSRI-treated patients with matched control patients not treated with SSRIs within a large EHR database representing a diverse population of 83 584 patients diagnosed with COVID-19 from January to September 2020 and with a duration of follow-up of as long as 8 months in 87 health care centers across the US.Selective serotonin reuptake inhibitors and specifically (1) fluoxetine, (2) fluoxetine or fluvoxamine, and (3) other SSRIs (ie, not fluoxetine or fluvoxamine).Death.A total of 3401 adult patients with COVID-19 prescribed SSRIs (2033 women [59.8%]; mean [SD] age, 63.8 [18.1] years) were identified, with 470 receiving fluoxetine only (280 women [59.6%]; mean [SD] age, 58.5 [18.1] years), 481 receiving fluoxetine or fluvoxamine (285 women [59.3%]; mean [SD] age, 58.7 [18.0] years), and 2898 receiving other SSRIs (1733 women [59.8%]; mean [SD] age, 64.7 [18.0] years) within a defined time frame. When compared with matched untreated control patients, relative risk (RR) of mortality was reduced among patients prescribed any SSRI (497 of 3401 [14.6%] vs 1130 of 6802 [16.6%]; RR, 0.92 [95% CI, 0.85-0.99]; adjusted P = .03); fluoxetine (46 of 470 [9.8%] vs 937 of 7050 [13.3%]; RR, 0.72 [95% CI, 0.54-0.97]; adjusted P = .03); and fluoxetine or fluvoxamine (48 of 481 [10.0%] vs 956 of 7215 [13.3%]; RR, 0.74 [95% CI, 0.55-0.99]; adjusted P = .04). The association between receiving any SSRI that is not fluoxetine or fluvoxamine and risk of death was not statistically significant (447 of 2898 [15.4%] vs 1474 of 8694 [17.0%]; RR, 0.92 [95% CI, 0.84-1.00]; adjusted P = .06).These results support evidence that SSRIs may be associated with reduced severity of COVID-19 reflected in the reduced RR of mortality. Further research and randomized clinical trials are needed to elucidate the effect of SSRIs generally, or more specifically of fluoxetine and fluvoxamine, on the severity of COVID-19 outcomes.

  View details for DOI 10.1001/jamanetworkopen.2021.33090

  View details for PubMedID 34779847

• Use of Patient-Reported Outcome Measures to Assess Outpatient Postpartum Recovery: A Systematic Review. JAMA network open Sultan, P., Sharawi, N., Blake, L., Ando, K., Sultan, E., Aghaeepour, N., Carvalho, B., Sadana, N. 2021; 4 (5): e2111600

  Abstract

  Outpatient postpartum recovery is an underexplored area of obstetrics. There is currently no consensus regarding which patient-reported outcome measure (PROM) clinicians and researchers should use to evaluate postpartum recovery.To evaluate PROMs of outpatient postpartum recovery using Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines.An initial literature search performed in July 2019 identified postpartum recovery PROMs and validation studies. A secondary search in July 2020 identified additional validation studies. Both searches were performed using 4 databases (Web of Science, Embase, PubMed, and CINAHL), with no date limiters. Studies with PROMs evaluating more than 3 proposed outpatient postpartum recovery domains were considered. Studies were included if they assessed any psychometric measurement property of the included PROMs in the outpatient postpartum setting. The PROMs were assessed for the following 8 psychometric measurement properties, as defined by COSMIN: content validity, structural validity, internal consistency, cross-cultural validity and measurement invariance, reliability, measurement error, hypothesis testing, and responsiveness. Psychometric measurement properties were evaluated in each included study using the COSMIN criteria by assessing (1) the quality of the methods (very good, adequate, doubtful, inadequate, or not assessed); (2) overall rating of results (sufficient, insufficient, inconsistent, or indeterminate); (3) level of evidence assessed using the Grading of Recommendations, Assessment, Development and Evaluations assessment tool; and (4) level of recommendation, which included class A (recommended for use; showed adequate content validity with at least low-quality evidence for sufficient internal consistency), class B (not class A or class C), or class C (not recommended).In total, 15 PROMs (7 obstetric specific and 8 non-obstetric specific) were identified, evaluating outpatient postpartum recovery in 46 studies involving 19 165 women. The majority of psychometric measurement properties of the included PROMs were graded as having very-low-level or low-level evidence. The best-performing PROMs that received class A recommendations were the Maternal Concerns Questionnaire, the Postpartum Quality of Life tool, and the World Health Organization Quality of Life-BREF. The remainder of the evaluated PROMs had insufficient evidence to make recommendations regarding their use (and received class B recommendations).This review found that the best-performing PROMs currently available to evaluate outpatient postpartum recovery were the Maternal Concerns Questionnaire, the Postpartum Quality of Life tool, and the World Health Organization Quality of Life-BREF; however, these tools all had significant limitations. This study highlights the need to focus future efforts on robustly developing and validating a new PROM that may comprehensively evaluate outpatient postpartum recovery.

  View details for DOI 10.1001/jamanetworkopen.2021.11600

  View details for PubMedID 34042993

• Human immune system adaptations to simulated microgravity revealed by single-cell mass cytometry. Scientific reports Spatz, J. M., Fulford, M. H., Tsai, A., Gaudilliere, D., Hedou, J., Ganio, E., Angst, M., Aghaeepour, N., Gaudilliere, B. 2021; 11 (1): 11872

  Abstract

  Exposure to microgravity (µG) during space flights produces a state of immunosuppression, leading to increased viral shedding, which could interfere with long term missions. However, the cellular mechanisms that underlie the immunosuppressive effects of µG are ill-defined. A deep understanding of human immune adaptations to µG is a necessary first step to design data-driven interventions aimed at preserving astronauts' immune defense during short- and long-term spaceflights. We employed a high-dimensional mass cytometry approach to characterize over 250 cell-specific functional responses in 18 innate and adaptive immune cell subsets exposed to 1G or simulated (s)µG using the Rotating Wall Vessel. A statistically stringent elastic net method produced a multivariate model that accurately stratified immune responses observed in 1G and sµG (p value 2E-4, cross-validation). Aspects of our analysis resonated with prior knowledge of human immune adaptations to µG, including the dampening of Natural Killer, CD4+ and CD8+ T cell responses. Remarkably, we found that sµG enhanced STAT5 signaling responses of immunosuppressive Tregs. Our results suggest µG exerts a dual effect on the human immune system, simultaneously dampening cytotoxic responses while enhancing Treg function. Our study provides a single-cell readout of sµG-induced immune dysfunctions and an analytical framework for future studies of human immune adaptations to human long-term spaceflights.

  View details for DOI 10.1038/s41598-021-90458-2

  View details for PubMedID 34099760

• Objective Activity Parameters Track Patient-Specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States. Annals of surgery Fallahzadeh, R., Verdonk, F., Ganio, E., Culos, A., Stanley, N., Marić, I., Chang, A. L., Becker, M., Phongpreecha, T., Xenochristou, M., De Francesco, D., Espinosa, C., Gao, X., Tsai, A., Sultan, P., Tingle, M., Amanatullah, D. F., Huddleston, J. I., Goodman, S. B., Gaudilliere, B., Angst, M. S., Aghaeepour, N. 2021

  Abstract

  The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established.Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. While phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity.Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state.The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, six distinct domains of physical function and sleep are identified to represent the objective temporal patterns: "activity capacity" and "moderate and overall activity" (declined immediately after surgery); "sleep disruption and sedentary activity" (increased after surgery); "overall sleep", "sleep onset", and "light activity" (no clear changes were observed after surgery). These patterns can be linked to individual patients' preoperative immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in M-MDSCs predicted a slower recovery.Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.

  View details for DOI 10.1097/SLA.0000000000005250

  View details for PubMedID 35129529

• Single-Cell Analysis of the Neonatal Immune System Across the Gestational Age Continuum. Frontiers in immunology Peterson, L. S., Hedou, J., Ganio, E. A., Stelzer, I. A., Feyaerts, D., Harbert, E., Adusumelli, Y., Ando, K., Tsai, E. S., Tsai, A. S., Han, X., Ringle, M., Houghteling, P., Reiss, J. D., Lewis, D. B., Winn, V. D., Angst, M. S., Aghaeepour, N., Stevenson, D. K., Gaudilliere, B. 2021; 12: 714090

  Abstract

  Although most causes of death and morbidity in premature infants are related to immune maladaptation, the premature immune system remains poorly understood. We provide a comprehensive single-cell depiction of the neonatal immune system at birth across the spectrum of viable gestational age (GA), ranging from 25 weeks to term. A mass cytometry immunoassay interrogated all major immune cell subsets, including signaling activity and responsiveness to stimulation. An elastic net model described the relationship between GA and immunome (R=0.85, p=8.75e-14), and unsupervised clustering highlighted previously unrecognized GA-dependent immune dynamics, including decreasing basal MAP-kinase/NFκB signaling in antigen presenting cells; increasing responsiveness of cytotoxic lymphocytes to interferon-α; and decreasing frequency of regulatory and invariant T cells, including NKT-like cells and CD8+CD161+ T cells. Knowledge gained from the analysis of the neonatal immune landscape across GA provides a mechanistic framework to understand the unique susceptibility of preterm infants to both hyper-inflammatory diseases and infections.

  View details for DOI 10.3389/fimmu.2021.714090

  View details for PubMedID 34497610

  View details for PubMedCentralID PMC8420969

• A Peripheral Immune Signature of Labor Induction. Frontiers in immunology Ando, K., Hédou, J. J., Feyaerts, D., Han, X., Ganio, E. A., Tsai, E. S., Peterson, L. S., Verdonk, F., Tsai, A. S., Marić, I., Wong, R. J., Angst, M. S., Aghaeepour, N., Stevenson, D. K., Blumenfeld, Y. J., Sultan, P., Carvalho, B., Stelzer, I. A., Gaudillière, B. 2021; 12: 725989

  Abstract

  Approximately 1 in 4 pregnant women in the United States undergo labor induction. The onset and establishment of labor, particularly induced labor, is a complex and dynamic process influenced by multiple endocrine, inflammatory, and mechanical factors as well as obstetric and pharmacological interventions. The duration from labor induction to the onset of active labor remains unpredictable. Moreover, prolonged labor is associated with severe complications for the mother and her offspring, most importantly chorioamnionitis, uterine atony, and postpartum hemorrhage. While maternal immune system adaptations that are critical for the maintenance of a healthy pregnancy have been previously characterized, the role of the immune system during the establishment of labor is poorly understood. Understanding maternal immune adaptations during labor initiation can have important ramifications for predicting successful labor induction and labor complications in both induced and spontaneous types of labor. The aim of this study was to characterize labor-associated maternal immune system dynamics from labor induction to the start of active labor. Serial blood samples from fifteen participants were collected immediately prior to labor induction (baseline) and during the latent phase until the start of active labor. Using high-dimensional mass cytometry, a total of 1,059 single-cell immune features were extracted from each sample. A multivariate machine-learning method was employed to characterize the dynamic changes of the maternal immune system after labor induction until the establishment of active labor. A cross-validated linear sparse regression model (least absolute shrinkage and selection operator, LASSO) predicted the minutes since induction of labor with high accuracy (R = 0.86, p = 6.7e-15, RMSE = 277 min). Immune features most informative for the model included STAT5 signaling in central memory CD8+ T cells and pro-inflammatory STAT3 signaling responses across multiple adaptive and innate immune cell subsets. Our study reports a peripheral immune signature of labor induction, and provides important insights into biological mechanisms that may ultimately predict labor induction success as well as complications, thereby facilitating clinical decision-making to improve maternal and fetal well-being.

  View details for DOI 10.3389/fimmu.2021.725989

  View details for PubMedID 34566984

  View details for PubMedCentralID PMC8458888

• Measuring the human immune response to surgery: multiomics for the prediction of postoperative outcomes. Current opinion in critical care Verdonk, F., Einhaus, J., Tsai, A. S., Hedou, J., Choisy, B., Gaudilliere, D., Kin, C., Aghaeepour, N., Angst, M. S., Gaudilliere, B. 2021

  Abstract

  Postoperative complications including infections, cognitive impairment, and protracted recovery occur in one-third of the 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on our healthcare system. However, the accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain as major clinical challenges.Although multifactorial in origin, the dysregulation of immunological mechanisms that occur in response to surgical trauma is a key determinant of postoperative complications. Prior research, primarily focusing on inflammatory plasma markers, has provided important clues regarding their pathogenesis. However, the recent advent of high-content, single-cell transcriptomic, and proteomic technologies has considerably improved our ability to characterize the immune response to surgery, thereby providing new means to understand the immunological basis of postoperative complications and to identify prognostic biological signatures.The comprehensive and single-cell characterization of the human immune response to surgery has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers, ultimately providing patients and surgeons with actionable information to improve surgical outcomes. Although recent studies have generated a wealth of knowledge, laying the foundation for a single-cell atlas of the human immune response to surgery, larger-scale multiomic studies are required to derive robust, scalable, and sufficiently powerful models to accurately predict the risk of postoperative complications in individual patients.

  View details for DOI 10.1097/MCC.0000000000000883

  View details for PubMedID 34545029

• Data-Driven Modeling of Pregnancy-Related Complications. Trends in molecular medicine Espinosa, C. n., Becker, M. n., Marić, I. n., Wong, R. J., Shaw, G. M., Gaudilliere, B. n., Aghaeepour, N. n., Stevenson, D. K. 2021

  Abstract

  A healthy pregnancy depends on complex interrelated biological adaptations involving placentation, maternal immune responses, and hormonal homeostasis. Recent advances in high-throughput technologies have provided access to multiomics biological data that, combined with clinical and social data, can provide a deeper understanding of normal and abnormal pregnancies. Integration of these heterogeneous datasets using state-of-the-art machine-learning methods can enable the prediction of short- and long-term health trajectories for a mother and offspring and the development of treatments to prevent or minimize complications. We review advanced machine-learning methods that could: provide deeper biological insights into a pregnancy not yet unveiled by current methodologies; clarify the etiologies and heterogeneity of pathologies that affect a pregnancy; and suggest the best approaches to address disparities in outcomes affecting vulnerable populations.

  View details for DOI 10.1016/j.molmed.2021.01.007

  View details for PubMedID 33573911

• METABOLIC PATTERNS OF INFANTS BORN TO MOTHERS WITH PREECLAMPSIA AND DIABETES SHOW UNDERLYING METABOLIC VULNERABILITY Reiss, J., Chang, A., Mayo, J., Stevenson, D. K., Shaw, G. M., Aghaeepour, N., Sylvester, K. BMJ PUBLISHING GROUP. 2021: 196

  View details for DOI 10.1136/jim-2021-WRMC.206

  View details for Web of Science ID 000608729100224

• Proteomic signatures predict preeclampsia in individual cohorts but not across cohorts - implications for clinical biomarker studies. The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians Ghaemi, M. S., Tarca, A. L., Romero, R. n., Stanley, N. n., Fallahzadeh, R. n., Tanada, A. n., Culos, A. n., Ando, K. n., Han, X. n., Blumenfeld, Y. J., Druzin, M. L., El-Sayed, Y. Y., Gibbs, R. S., Winn, V. D., Contrepois, K. n., Ling, X. B., Wong, R. J., Shaw, G. M., Stevenson, D. K., Gaudilliere, B. n., Aghaeepour, N. n., Angst, M. S. 2021: 1–8

  Abstract

  Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care.The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes.Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE.The model derived in the Stanford cohort was highly significant (p = 3.9E-15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E-01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E-21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E-02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E-454, R = 0.92) and Detroit cohorts (p = 1.1.E-92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences.Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the "same" clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.

  View details for DOI 10.1080/14767058.2021.1888915

  View details for PubMedID 33653202

• Single-cell peripheral immunoprofiling of Alzheimer's and Parkinson's diseases. Science advances Phongpreecha, T., Fernandez, R., Mrdjen, D., Culos, A., Gajera, C. R., Wawro, A. M., Stanley, N., Gaudilliere, B., Poston, K. L., Aghaeepour, N., Montine, T. J. 2020; 6 (48)

  Abstract

  Peripheral blood mononuclear cells (PBMCs) may provide insight into the pathogenesis of Alzheimer's disease (AD) or Parkinson's disease (PD). We investigated PBMC samples from 132 well-characterized research participants using seven canonical immune stimulants, mass cytometric identification of 35 PBMC subsets, and single-cell quantification of 15 intracellular signaling markers, followed by machine learning model development to increase predictive power. From these, three main intracellular signaling pathways were identified specifically in PBMC subsets from people with AD versus controls: reduced activation of PLCgamma2 across many cell types and stimulations and selectively variable activation of STAT1 and STAT5, depending on stimulant and cell type. Our findings functionally buttress the now multiply-validated observation that a rare coding variant in PLCG2 is associated with a decreased risk of AD. Together, these data suggest enhanced PLCgamma2 activity as a potential new therapeutic target for AD with a readily accessible pharmacodynamic biomarker.

  View details for DOI 10.1126/sciadv.abd5575

  View details for PubMedID 33239300

• Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions NATURE MACHINE INTELLIGENCE Culos, A., Tsai, A. S., Stanley, N., Becker, M., Ghaemi, M. S., McIlwain, D. R., Fallahzadeh, R., Tanada, A., Nassar, H., Espinosa, C., Xenochristou, M., Ganio, E., Peterson, L., Han, X., Stelzer, I. A., Ando, K., Gaudilliere, D., Phongpreecha, T., Maric, I., Chang, A. L., Shaw, G. M., Stevenson, D. K., Bendall, S., Davis, K. L., Fantl, W., Nolan, G. P., Hastie, T., Tibshirani, R., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020

  View details for DOI 10.1038/s42256-020-00232-8

  View details for Web of Science ID 000579336000001

• Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. Nature machine intelligence Culos, A., Tsai, A. S., Stanley, N., Becker, M., Ghaemi, M. S., McIlwain, D. R., Fallahzadeh, R., Tanada, A., Nassar, H., Espinosa, C., Xenochristou, M., Ganio, E., Peterson, L., Han, X., Stelzer, I. A., Ando, K., Gaudilliere, D., Phongpreecha, T., Marić, I., Chang, A. L., Shaw, G. M., Stevenson, D. K., Bendall, S., Davis, K. L., Fantl, W., Nolan, G. P., Hastie, T., Tibshirani, R., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020; 2 (10): 619-628

  Abstract

  The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

  View details for DOI 10.1038/s42256-020-00232-8

  View details for PubMedID 33294774

  View details for PubMedCentralID PMC7720904

• A Deep Immune Profiling in Inflammatory Bowel Disease Reveals Disordered Immune Cell Frequencies Before and in Response to Major Abdominal Operations Rumer, K., Stanley, N., Ganio, E., Tsai, E., Kin, C., Shelton, A. A., Morris, A. M., Angst, M., Aghaeepour, N., Gaudilliere, B. ELSEVIER SCIENCE INC. 2020: S50

  View details for Web of Science ID 000582792300069

• IMMUNE PROFILING TO PREDICT RECOVERY OUTCOMES AFTER SURGERY Tsai, E. S., Rumer, K., Wong, K., Warrington, B., Shelton, E., Ganio, E., Verdonk, F., Ando, K., Tingle, M., Folk-Tolbert, M., Shankar, K., Shelton, A., Morris, A., Kirilcuk, N., Gurland, B., Kebebew, E., Kin, C., Angst, M. S., Aghaeepour, N., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2020: 66–67

  View details for Web of Science ID 000587668800152

• Towards personalized medicine in maternal and child health: integrating biologic and social determinants. Pediatric research Stevenson, D. K., Wong, R. J., Aghaeepour, N., Maric, I., Angst, M. S., Contrepois, K., Darmstadt, G. L., Druzin, M. L., Eisenberg, M. L., Gaudilliere, B., Gibbs, R. S., Gotlib, I. H., Gould, J. B., Lee, H. C., Ling, X. B., Mayo, J. A., Moufarrej, M. N., Quaintance, C. C., Quake, S. R., Relman, D. A., Sirota, M., Snyder, M. P., Sylvester, K. G., Hao, S., Wise, P. H., Shaw, G. M., Katz, M. 2020

  View details for DOI 10.1038/s41390-020-0981-8

  View details for PubMedID 32454518

• Effects of Selective Exclusion of Patients on Preterm Birth Test Performance OBSTETRICS AND GYNECOLOGY McElrath, T. F., Cantonwine, D., Stevenson, D. K., Shaw, G. M., Aghaeepour, N., Quake, S. 2020; 135 (5): 1228–29

  View details for Web of Science ID 000570209400048

• Early prediction of preeclampsia via machine learning. American journal of obstetrics & gynecology MFM Maric, I., Tsur, A., Aghaeepour, N., Montanari, A., Stevenson, D. K., Shaw, G. M., Winn, V. D. 2020; 2 (2): 100100

  Abstract

  BACKGROUND: Early prediction of preeclampsia is challenging because of poorly understood causes, various risk factors, and likely multiple pathogenic phenotypes of preeclampsia. Statistical learning methods are well-equipped to deal with a large number of variables, such as patients' clinical and laboratory data, and to select the most informative features automatically.OBJECTIVE: Our objective was to use statistical learning methods to analyze all available clinical and laboratory data that were obtained during routine prenatal visits in early pregnancy and to use them to develop a prediction model for preeclampsia.STUDY DESIGN: This was a retrospective cohort study that used data from 16,370 births at Lucile Packard Children Hospital at Stanford, CA, from April 2014 to January 2018. Two statistical learning algorithms were used to build a predictive model: (1) elastic net and (2) gradient boosting algorithm. Models for all preeclampsia and early-onset preeclampsia (<34 weeks gestation) were fitted with the use of patient data that were available at <16 weeks gestational age. The 67 variables that were considered in the models included maternal characteristics, medical history, routine prenatal laboratory results, and medication intake. The area under the receiver operator curve, true-positive rate, and false-positive rate were assessed via cross-validation.RESULTS: Using the elastic netalgorithm, we developed a prediction model that contained a subset of the most informative features from all variables. The obtained prediction model for preeclampsia yielded an area under the curve of 0.79 (95% confidence interval, 0.75-0.83), sensitivity of 45.2%, and false-positive rate of 8.1%. The prediction model for early-onset preeclampsia achieved an area under the curve of 0.89 (95% confidence interval, 0.84-0.95), true-positive rate of 72.3%, and false-positive rate of 8.8%.CONCLUSION: Statistical learning methods in a retrospective cohort study automatically identified a set of significant features for prediction and yielded high prediction performance for preeclampsia risk from routine early pregnancy information.

  View details for DOI 10.1016/j.ajogmf.2020.100100

  View details for PubMedID 33345966

• Multi-Omic, Longitudinal Profile of Third-Trimester Pregnancies Identifies a Molecular Switch That Predicts the Onset of Labor. Stelzer, I., Ghaemi, M., Han, X., Ando, K., Peterson, L., Contrepois, K., Ganio, E., Tsai, A., Tsai, E., Rumer, K., Stanley, N., Fallazadeh, R., Becker, M., Culos, A., Gaudilliere, D., Wong, R., Winn, V., Shaw, G., Stevenson, D., Snyder, M., Angst, M., Aghaeepour, N., Gaudilliere, B. SPRINGER HEIDELBERG. 2020: 89A

  View details for Web of Science ID 000525432600082

• Development of a Comprehensive Neuropsychological Battery to Assess Post-Stroke Cognitive Functioning Drag, L., Aghaeepour, N., Mlynash, M., Osborn, E., Rah, E., Buckwalter, M., Lansberg, M. LIPPINCOTT WILLIAMS & WILKINS. 2020

  View details for Web of Science ID 000590040201406

• Multiomic immune clockworks of pregnancy. Seminars in immunopathology Peterson, L. S., Stelzer, I. A., Tsai, A. S., Ghaemi, M. S., Han, X. n., Ando, K. n., Winn, V. D., Martinez, N. R., Contrepois, K. n., Moufarrej, M. N., Quake, S. n., Relman, D. A., Snyder, M. P., Shaw, G. M., Stevenson, D. K., Wong, R. J., Arck, P. n., Angst, M. S., Aghaeepour, N. n., Gaudilliere, B. n. 2020

  Abstract

  Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy "immune clock" is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth.

  View details for DOI 10.1007/s00281-019-00772-1

  View details for PubMedID 32020337

• Multivariate prediction of dementia in Parkinson's disease. NPJ Parkinson's disease Phongpreecha, T., Cholerton, B., Mata, I. F., Zabetian, C. P., Poston, K. L., Aghaeepour, N., Tian, L., Quinn, J. F., Chung, K. A., Hiller, A. L., Hu, S. C., Edwards, K. L., Montine, T. J. 2020; 6 (1): 20

  Abstract

  Cognitive impairment in Parkinson's disease (PD) is pervasive with potentially devastating effects. Identification of those at risk for cognitive decline is vital to identify and implement appropriate interventions. Robust multivariate approaches, including fixed-effect, mixed-effect, and multitask learning models, were used to study associations between biological, clinical, and cognitive factors and for predicting cognitive status longitudinally in a well-characterized prevalent PD cohort (n = 827). Age, disease duration, sex, and GBA status were the primary biological factors associated with cognitive status and progression to dementia. Specific cognitive tests were better predictors of subsequent cognitive status for cognitively unimpaired and dementia groups. However, these models could not accurately predict future mild cognitive impairment (PD-MCI). Data collected from a large PD cohort thus revealed the primary biological and cognitive factors associated with dementia, and provide clinicians with data to aid in the identification of risk for dementia. Sex differences and their potential relationship to genetic status are also discussed.

  View details for DOI 10.1038/s41531-020-00121-2

  View details for PubMedID 34429432

• Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities. Nature reviews. Neurology Davis, K. D., Aghaeepour, N. n., Ahn, A. H., Angst, M. S., Borsook, D. n., Brenton, A. n., Burczynski, M. E., Crean, C. n., Edwards, R. n., Gaudilliere, B. n., Hergenroeder, G. W., Iadarola, M. J., Iyengar, S. n., Jiang, Y. n., Kong, J. T., Mackey, S. n., Saab, C. Y., Sang, C. N., Scholz, J. n., Segerdahl, M. n., Tracey, I. n., Veasley, C. n., Wang, J. n., Wager, T. D., Wasan, A. D., Pelleymounter, M. A. 2020

  Abstract

  Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

  View details for DOI 10.1038/s41582-020-0362-2

  View details for PubMedID 32541893

• A longitudinal study of the post-stroke immune response and cognitive functioning: the StrokeCog study protocol. BMC neurology Drag, L. L., Mlynash, M. n., Nassar, H. n., Osborn, E. n., Kim, D. E., Angst, M. S., Aghaeepour, N. n., Buckwalter, M. n., Lansberg, M. G. 2020; 20 (1): 313

  Abstract

  Stroke increases the risk of cognitive impairment even several years after the stroke event. The exact mechanisms of post-stroke cognitive decline are unclear, but the immunological response to stroke might play a role. The aims of the StrokeCog study are to examine the associations between immunological responses and long-term post-stroke cognitive trajectories in individuals with ischemic stroke.StrokeCog is a single-center, prospective, observational, cohort study. Starting 6-12 months after stroke, comprehensive neuropsychological assessment, plasma and serum, and psychosocial variables will be collected at up to 4 annual visits. Single cell sequencing of peripheral blood monocytes and plasma proteomics will be conducted. The primary outcome will be the change in global and domain-specific neuropsychological performance across annual evaluations. To explain the differences in cognitive change amongst participants, we will examine the relationships between comprehensive immunological measures and these cognitive trajectories. It is anticipated that 210 participants will be enrolled during the first 3 years of this 4-year study. Accounting for attrition, an anticipated final sample size of 158 participants with an average of 3 annual study visits will be available at the completion of the study. Power analyses indicate that this sample size will provide 90% power to detect an average cognitive change of at least 0.23 standard deviations in either direction.StrokeCog will provide novel insight into the relationships between immune events and cognitive change late after stroke.

  View details for DOI 10.1186/s12883-020-01897-9

  View details for PubMedID 32847540

• Systematic Immunophenotyping Reveals Sex-Specific Responses After Painful Injury in Mice. Frontiers in immunology Tawfik, V. L., Huck, N. A., Baca, Q. J., Ganio, E. A., Haight, E. S., Culos, A. n., Ghaemi, S. n., Phongpreecha, T. n., Angst, M. S., Clark, J. D., Aghaeepour, N. n., Gaudilliere, B. n. 2020; 11: 1652

  Abstract

  Many diseases display unequal prevalence between sexes. The sex-specific immune response to both injury and persistent pain remains underexplored and would inform treatment paradigms. We utilized high-dimensional mass cytometry to perform a comprehensive analysis of phenotypic and functional immune system differences between male and female mice after orthopedic injury. Multivariate modeling of innate and adaptive immune cell responses after injury using an elastic net algorithm, a regularized regression method, revealed sex-specific divergence at 12 h and 7 days after injury with a stronger immune response to injury in females. At 12 h, females upregulated STAT3 signaling in neutrophils but downregulated STAT1 and STAT6 signals in T regulatory cells, suggesting a lack of engagement of immune suppression pathways by females. Furthermore, at 7 days females upregulated MAPK pathways (p38, ERK, NFkB) in CD4T memory cells, setting up a possible heightened immune memory of painful injury. Taken together, our findings provide the first comprehensive and functional analysis of sex-differences in the immune response to painful injury.

  View details for DOI 10.3389/fimmu.2020.01652

  View details for PubMedID 32849569

  View details for PubMedCentralID PMC7403191

• Effects of Selective Exclusion of Patients on Preterm Birth Test Performance. Obstetrics and gynecology McElrath, T. F., Cantonwine, D. n., Stevenson, D. K., Shaw, G. M., Aghaeepour, N. n., Quake, S. n. 2020; 135 (5): 1228–29

  View details for DOI 10.1097/AOG.0000000000003855

  View details for PubMedID 32332399

• Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries. JAMA network open Jehan, F. n., Sazawal, S. n., Baqui, A. H., Nisar, M. I., Dhingra, U. n., Khanam, R. n., Ilyas, M. n., Dutta, A. n., Mitra, D. K., Mehmood, U. n., Deb, S. n., Mahmud, A. n., Hotwani, A. n., Ali, S. M., Rahman, S. n., Nizar, A. n., Ame, S. M., Moin, M. I., Muhammad, S. n., Chauhan, A. n., Begum, N. n., Khan, W. n., Das, S. n., Ahmed, S. n., Hasan, T. n., Khalid, J. n., Rizvi, S. J., Juma, M. H., Chowdhury, N. H., Kabir, F. n., Aftab, F. n., Quaiyum, A. n., Manu, A. n., Yoshida, S. n., Bahl, R. n., Rahman, A. n., Pervin, J. n., Winston, J. n., Musonda, P. n., Stringer, J. S., Litch, J. A., Ghaemi, M. S., Moufarrej, M. N., Contrepois, K. n., Chen, S. n., Stelzer, I. A., Stanley, N. n., Chang, A. L., Hammad, G. B., Wong, R. J., Liu, C. n., Quaintance, C. C., Culos, A. n., Espinosa, C. n., Xenochristou, M. n., Becker, M. n., Fallahzadeh, R. n., Ganio, E. n., Tsai, A. S., Gaudilliere, D. n., Tsai, E. S., Han, X. n., Ando, K. n., Tingle, M. n., Maric, I. n., Wise, P. H., Winn, V. D., Druzin, M. L., Gibbs, R. S., Darmstadt, G. L., Murray, J. C., Shaw, G. M., Stevenson, D. K., Snyder, M. P., Quake, S. R., Angst, M. S., Gaudilliere, B. n., Aghaeepour, N. n. 2020; 3 (12): e2029655

  Abstract

  Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies.To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB.This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019.Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites.The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation.Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways.This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

  View details for DOI 10.1001/jamanetworkopen.2020.29655

  View details for PubMedID 33337494

• Landscape of coordinated immune responses to H1N1 challenge in humans. The Journal of clinical investigation Rahil, Z. n., Leylek, R. n., Schürch, C. M., Chen, H. n., Bjornson-Hooper, Z. n., Christensen, S. R., Gherardini, P. F., Bhate, S. S., Spitzer, M. H., Fragiadakis, G. K., Mukherjee, N. n., Kim, N. n., Jiang, S. n., Yo, J. n., Gaudilliere, B. n., Affrime, M. n., Bock, B. n., Hensley, S. E., Idoyaga, J. n., Aghaeepour, N. n., Kim, K. n., Nolan, G. P., McIlwain, D. R. 2020

  Abstract

  Influenza is a significant cause of morbidity and mortality worldwide. Here we show changes in the abundance and activation states of more than 50 immune cell subsets in 35 individuals over 11 time points during human A/California/2009 (H1N1) virus challenge monitored using mass cytometry along with other clinical assessments. Peak change in monocyte, B cell, and T cell subset frequencies coincided with peak virus shedding, followed by marked activation of T and NK cells. Results led to the identification of CD38 as a critical regulator of plasmacytoid dendritic cell function in response to influenza virus. Machine learning using study-derived clinical parameters and single-cell data effectively classified and predicted susceptibility to infection. The coordinated immune cell dynamics defined in this study provide a framework for identifying novel correlates of protection in the evaluation of future influenza therapeutics.

  View details for DOI 10.1172/JCI137265

  View details for PubMedID 33044226

• Changes in pregnancy-related serum biomarkers early in gestation are associated with later development of preeclampsia. PloS one Hao, S. n., You, J. n., Chen, L. n., Zhao, H. n., Huang, Y. n., Zheng, L. n., Tian, L. n., Maric, I. n., Liu, X. n., Li, T. n., Bianco, Y. K., Winn, V. D., Aghaeepour, N. n., Gaudilliere, B. n., Angst, M. S., Zhou, X. n., Li, Y. M., Mo, L. n., Wong, R. J., Shaw, G. M., Stevenson, D. K., Cohen, H. J., Mcelhinney, D. B., Sylvester, K. G., Ling, X. B. 2020; 15 (3): e0230000

  Abstract

  Placental protein expression plays a crucial role during pregnancy. We hypothesized that: (1) circulating levels of pregnancy-associated, placenta-related proteins throughout gestation reflect the temporal progression of the uncomplicated, full-term pregnancy, and can effectively estimate gestational ages (GAs); and (2) preeclampsia (PE) is associated with disruptions in these protein levels early in gestation; and can identify impending PE. We also compared gestational profiles of proteins in the human and mouse, using pregnant heme oxygenase-1 (HO-1) heterozygote (Het) mice, a mouse model reflecting PE-like symptoms.Serum levels of placenta-related proteins-leptin (LEP), chorionic somatomammotropin hormone like 1 (CSHL1), elabela (ELA), activin A, soluble fms-like tyrosine kinase 1 (sFlt-1), and placental growth factor (PlGF)-were quantified by ELISA in blood serially collected throughout human pregnancies (20 normal subjects with 66 samples, and 20 subjects who developed PE with 61 samples). Multivariate analysis was performed to estimate the GA in normal pregnancy. Mean-squared errors of GA estimations were used to identify impending PE. The human protein profiles were then compared with those in the pregnant HO-1 Het mice.An elastic net-based gestational dating model was developed (R2 = 0.76) and validated (R2 = 0.61) using serum levels of the 6 proteins measured at various GAs from women with normal uncomplicated pregnancies. In women who developed PE, the model was not (R2 = -0.17) associated with GA. Deviations from the model estimations were observed in women who developed PE (P = 0.01). The model developed with 5 proteins (ELA excluded) performed similarly from sera from normal human (R2 = 0.68) and WT mouse (R2 = 0.85) pregnancies. Disruptions of this model were observed in both human PE-associated (R2 = 0.27) and mouse HO-1 Het (R2 = 0.30) pregnancies. LEP outperformed sFlt-1 and PlGF in differentiating impending PE at early human and late mouse GAs.Serum placenta-related protein profiles are temporally regulated throughout normal pregnancies and significantly disrupted in women who develop PE. LEP changes earlier than the well-established biomarkers (sFlt-1 and PlGF). There may be evidence of a causative action of HO-1 deficiency in LEP upregulation in a PE-like murine model.

  View details for DOI 10.1371/journal.pone.0230000

  View details for PubMedID 32126118

• Multivariate prediction of dementia in Parkinson's disease. NPJ Parkinson's disease Phongpreecha, T. n., Cholerton, B. n., Mata, I. F., Zabetian, C. P., Poston, K. L., Aghaeepour, N. n., Tian, L. n., Quinn, J. F., Chung, K. A., Hiller, A. L., Hu, S. C., Edwards, K. L., Montine, T. J. 2020; 6: 20

  Abstract

  Cognitive impairment in Parkinson's disease (PD) is pervasive with potentially devastating effects. Identification of those at risk for cognitive decline is vital to identify and implement appropriate interventions. Robust multivariate approaches, including fixed-effect, mixed-effect, and multitask learning models, were used to study associations between biological, clinical, and cognitive factors and for predicting cognitive status longitudinally in a well-characterized prevalent PD cohort (n = 827). Age, disease duration, sex, and GBA status were the primary biological factors associated with cognitive status and progression to dementia. Specific cognitive tests were better predictors of subsequent cognitive status for cognitively unimpaired and dementia groups. However, these models could not accurately predict future mild cognitive impairment (PD-MCI). Data collected from a large PD cohort thus revealed the primary biological and cognitive factors associated with dementia, and provide clinicians with data to aid in the identification of risk for dementia. Sex differences and their potential relationship to genetic status are also discussed.

  View details for DOI 10.1038/s41531-020-00121-2

  View details for PubMedID 32885039

  View details for PubMedCentralID PMC7447766

• Author Correction: Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nature communications Ganio, E. A., Stanley, N. n., Lindberg-Larsen, V. n., Einhaus, J. n., Tsai, A. S., Verdonk, F. n., Culos, A. n., Ghaemi, S. n., Rumer, K. K., Stelzer, I. A., Gaudilliere, D. n., Tsai, E. n., Fallahzadeh, R. n., Choisy, B. n., Kehlet, H. n., Aghaeepour, N. n., Angst, M. S., Gaudilliere, B. n. 2020; 11 (1): 4495

  Abstract

  An amendment to this paper has been published and can be accessed via a link at the top of the paper.

  View details for DOI 10.1038/s41467-020-18410-y

  View details for PubMedID 32883978

• Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nature communications Ganio, E. A., Stanley, N. n., Lindberg-Larsen, V. n., Einhaus, J. n., Tsai, A. S., Verdonk, F. n., Culos, A. n., Gahemi, S. n., Rumer, K. K., Stelzer, I. A., Gaudilliere, D. n., Tsai, E. n., Fallahzadeh, R. n., Choisy, B. n., Kehlet, H. n., Aghaeepour, N. n., Angst, M. S., Gaudilliere, B. n. 2020; 11 (1): 3737

  Abstract

  Glucocorticoids (GC) are a controversial yet commonly used intervention in the clinical management of acute inflammatory conditions, including sepsis or traumatic injury. In the context of major trauma such as surgery, concerns have been raised regarding adverse effects from GC, thereby necessitating a better understanding of how GCs modulate the immune response. Here we report the results of a randomized controlled trial (NCT02542592) in which we employ a high-dimensional mass cytometry approach to characterize innate and adaptive cell signaling dynamics after a major surgery (primary outcome) in patients treated with placebo or methylprednisolone (MP). A robust, unsupervised bootstrap clustering of immune cell subsets coupled with random forest analysis shows profound (AUC = 0.92, p-value = 3.16E-8) MP-induced alterations of immune cell signaling trajectories, particularly in the adaptive compartments. By contrast, key innate signaling responses previously associated with pain and functional recovery after surgery, including STAT3 and CREB phosphorylation, are not affected by MP. These results imply cell-specific and pathway-specific effects of GCs, and also prompt future studies to examine GCs' effects on clinical outcomes likely dependent on functional adaptive immune responses.

  View details for DOI 10.1038/s41467-020-17565-y

  View details for PubMedID 32719355

• VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nature communications Stanley, N. n., Stelzer, I. A., Tsai, A. S., Fallahzadeh, R. n., Ganio, E. n., Becker, M. n., Phongpreecha, T. n., Nassar, H. n., Ghaemi, S. n., Maric, I. n., Culos, A. n., Chang, A. L., Xenochristou, M. n., Han, X. n., Espinosa, C. n., Rumer, K. n., Peterson, L. n., Verdonk, F. n., Gaudilliere, D. n., Tsai, E. n., Feyaerts, D. n., Einhaus, J. n., Ando, K. n., Wong, R. J., Obermoser, G. n., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B. n., Aghaeepour, N. n. 2020; 11 (1): 3738

  Abstract

  High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

  View details for DOI 10.1038/s41467-020-17569-8

  View details for PubMedID 32719375

• Mid-gestation serum lipidomic profile associations with spontaneous preterm birth are influenced by body mass index. PloS one Borkowski, K., Newman, J. W., Aghaeepour, N., Mayo, J. A., Blazenovic, I., Fiehn, O., Stevenson, D. K., Shaw, G. M., Carmichael, S. L. 2020; 15 (11): e0239115

  Abstract

  Spontaneous preterm birth (sPTB) is a major cause of infant morbidity and mortality. While metabolic changes leading to preterm birth are unknown, several factors including dyslipidemia and inflammation have been implicated and paradoxically both low (<18.5 kg/m2) and high (>30 kg/m2) body mass indices (BMIs) are risk factors for this condition. The objective of the study was to identify BMI-associated metabolic perturbations and potential mid-gestation serum biomarkers of preterm birth in a cohort of underweight, normal weight and obese women experiencing either sPTB or full-term deliveries (n = 102; n = 17/group). For this purpose, we combined untargeted metabolomics and lipidomics with targeted metabolic profiling of major regulators of inflammation and metabolism, including oxylipins, endocannabinoids, bile acids and ceramides. Women who were obese and had sPTB showed elevated oxidative stress and dyslipidemia characterized by elevated serum free fatty acids. Women who were underweight-associated sPTB also showed evidence of dyslipidemia characterized by elevated phospholipids, unsaturated triglycerides, sphingomyelins, cholesteryl esters and long-chain acylcarnitines. In normal weight women experiencing sPTB, the relative abundance of 14(15)-epoxyeicosatrienoic acid and 14,15-dihydroxyeicosatrienoic acids to other regioisomers were altered at mid-pregnancy. This phenomenon is not yet associated with any biological process, but may be linked to estrogen metabolism. These changes were differentially modulated across BMI groups. In conclusion, using metabolomics we observed distinct BMI-dependent metabolic manifestations among women who had sPTB. These observations suggest the potential to predict sPTB mid-gestation using a new set of metabolomic markers and BMI stratification. This study opens the door to further investigate the role of cytochrome P450/epoxide hydrolase metabolism in sPTB.

  View details for DOI 10.1371/journal.pone.0239115

  View details for PubMedID 33201881

• Cyt-Geist: Current and Future Challenges in Cytometry: Reports of the CYTO 2019 Conference Workshops. Cytometry. Part A : the journal of the International Society for Analytical Cytology Czechowska, K., Lannigan, J., Aghaeepour, N., Back, J. B., Begum, J., Behbehani, G., Bispo, C., Bitoun, D., Fernandez, A. B., Boova, S. T., Brinkman, R. R., Ciccolella, C. O., Cotleur, B., Davies, D., Dela Cruz, G. V., Del Rio-Guerra, R., Des Lauriers-Cox, A. M., Douagi, I., Dumrese, C., Bonilla Escobar, D. L., Estevam, J., Ewald, C., Fossum, A., Gaudilliere, B., Green, C., Groves, C., Hall, C., Haque, Y., Hedrick, M. N., Hogg, K., Hsieh, E. W., Irish, J., Lederer, J., Leipold, M., Lewis-Tuffin, L. J., Litwin, V., Lopez, P., Nasdala, I., Nedbal, J., Ohlsson-Wilhelm, B. M., Price, K. M., Rahman, A. H., Rayanki, R., Rieger, A. M., Robinson, J. P., Shapiro, H., Sun, Y. S., Tang, V. A., Tesfa, L., Telford, W. G., Walker, R., Welsh, J. A., Wheeler, P., Tarnok, A. 2019; 95 (12): 1236–74

  View details for DOI 10.1002/cyto.a.23941

  View details for PubMedID 31833655

• iRhom2 inhibits bile duct obstruction-induced liver fibrosis. Science signaling Sundaram, B., Behnke, K., Belancic, A., Al-Salihi, M. A., Thabet, Y., Polz, R., Pellegrino, R., Zhuang, Y., Shinde, P. V., Xu, H. C., Vasilevska, J., Longerich, T., Herebian, D., Mayatepek, E., Bock, H. H., May, P., Kordes, C., Aghaeepour, N., Mak, T. W., Keitel, V., Haussinger, D., Scheller, J., Pandyra, A. A., Lang, K. S., Lang, P. A. 2019; 12 (605)

  Abstract

  Chronic liver disease can induce prolonged activation of hepatic stellate cells, which may result in liver fibrosis. Inactive rhomboid protein 2 (iRhom2) is required for the maturation of A disintegrin and metalloprotease 17 (ADAM17, also called TACE), which is responsible for the cleavage of membrane-bound tumor necrosis factor-alpha (TNF-alpha) and its receptors (TNFRs). Here, using the murine bile duct ligation (BDL) model, we showed that the abundance of iRhom2 and activation of ADAM17 increased during liver fibrosis. Consistent with this, concentrations of ADAM17 substrates were increased in plasma samples from mice after BDL and in patients suffering from liver cirrhosis. We observed increased liver fibrosis, accelerated disease progression, and an increase in activated stellate cells after BDL in mice lacking iRhom2 (Rhbdf2-/- ) compared to that in controls. In vitro primary mouse hepatic stellate cells exhibited iRhom2-dependent shedding of the ADAM17 substrates TNFR1 and TNFR2. In vivo TNFR shedding after BDL also depended on iRhom2. Treatment of Rhbdf2-/- mice with the TNF-alpha inhibitor etanercept reduced the presence of activated stellate cells and alleviated liver fibrosis after BDL. Together, these data suggest that iRhom2-mediated inhibition of TNFR signaling protects against liver fibrosis.

  View details for DOI 10.1126/scisignal.aax1194

  View details for PubMedID 31662486

• CytoNorm: A Normalization Algorithm for Cytometry Data. Cytometry. Part A : the journal of the International Society for Analytical Cytology Van Gassen, S., Gaudilliere, B., Angst, M. S., Saeys, Y., Aghaeepour, N. 2019

  Abstract

  High-dimensional flow cytometry has matured to a level that enables deep phenotyping of cellular systems at a clinical scale. The resulting high-content data sets allow characterizing the human immune system at unprecedented single cell resolution. However, the results are highly dependent on sample preparation and measurements might drift over time. While various controls exist for assessment and improvement of data quality in a single sample, the challenges of cross-sample normalization attempts have been limited to aligning marker distributions across subjects. These approaches, inspired by bulk genomics and proteomics assays, ignore the single-cell nature of the data and risk the removal of biologically relevant signals. This work proposes CytoNorm, a normalization algorithm to ensure internal consistency between clinical samples based on shared controls across various study batches. Data from the shared controls is used to learn the appropriate transformations for each batch (e.g., each analysis day). Importantly, some sources of technical variation are strongly influenced by the amount of protein expressed on specific cell types, requiring several population-specific transformations to normalize cells from a heterogeneous sample. To address this, our approach first identifies the overall cellular distribution using a clustering step, and calculates subset-specific transformations on the control samples by computing their quantile distributions and aligning them with splines. These transformations are then applied to all other clinical samples in the batch to remove the batch-specific variations. We evaluated the algorithm on a customized data set with two shared controls across batches. One control sample was used for calculation of the normalization transformations and the second control was used as a blinded test set and evaluated with Earth Mover's distance. Additional results are provided using two real-world clinical data sets. Overall, our method compared favorably to standard normalization procedures. The algorithm is implemented in the R package "CytoNorm" and available via the following link: www.github.com/saeyslab/CytoNorm © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

  View details for DOI 10.1002/cyto.a.23904

  View details for PubMedID 31633883

• Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition). European journal of immunology Cossarizza, A., Chang, H., Radbruch, A., Acs, A., Adam, D., Adam-Klages, S., Agace, W. W., Aghaeepour, N., Akdis, M., Allez, M., Almeida, L. N., Alvisi, G., Anderson, G., Andra, I., Annunziato, F., Anselmo, A., Bacher, P., Baldari, C. T., Bari, S., Barnaba, V., Barros-Martins, J., Battistini, L., Bauer, W., Baumgart, S., Baumgarth, N., Baumjohann, D., Baying, B., Bebawy, M., Becher, B., Beisker, W., Benes, V., Beyaert, R., Blanco, A., Boardman, D. A., Bogdan, C., Borger, J. G., Borsellino, G., Boulais, P. E., Bradford, J. A., Brenner, D., Brinkman, R. R., Brooks, A. E., Busch, D. H., Buscher, M., Bushnell, T. P., Calzetti, F., Cameron, G., Cammarata, I., Cao, X., Cardell, S. L., Casola, S., Cassatella, M. A., Cavani, A., Celada, A., Chatenoud, L., Chattopadhyay, P. K., Chow, S., Christakou, E., Cicin-Sain, L., Clerici, M., Colombo, F. S., Cook, L., Cooke, A., Cooper, A. M., Corbett, A. J., Cosma, A., Cosmi, L., Coulie, P. G., Cumano, A., Cvetkovic, L., Dang, V. D., Dang-Heine, C., Davey, M. S., Davies, D., De Biasi, S., Del Zotto, G., Dela Cruz, G. V., Delacher, M., Della Bella, S., Dellabona, P., Deniz, G., Dessing, M., Di Santo, J. P., Diefenbach, A., Dieli, F., Dolf, A., Dorner, T., Dress, R. J., Dudziak, D., Dustin, M., Dutertre, C., Ebner, F., Eckle, S. B., Edinger, M., Eede, P., Ehrhardt, G. R., Eich, M., Engel, P., Engelhardt, B., Erdei, A., Esser, C., Everts, B., Evrard, M., Falk, C. S., Fehniger, T. A., Felipo-Benavent, M., Ferry, H., Feuerer, M., Filby, A., Filkor, K., Fillatreau, S., Follo, M., Forster, I., Foster, J., Foulds, G. A., Frehse, B., Frenette, P. S., Frischbutter, S., Fritzsche, W., Galbraith, D. W., Gangaev, A., Garbi, N., Gaudilliere, B., Gazzinelli, R. T., Geginat, J., Gerner, W., Gherardin, N. A., Ghoreschi, K., Gibellini, L., Ginhoux, F., Goda, K., Godfrey, D. I., Goettlinger, C., Gonzalez-Navajas, J. M., Goodyear, C. S., Gori, A., Grogan, J. L., Grummitt, D., Grutzkau, A., Haftmann, C., Hahn, J., Hammad, H., Hammerling, G., Hansmann, L., Hansson, G., Harpur, C. M., Hartmann, S., Hauser, A., Hauser, A. E., Haviland, D. L., Hedley, D., Hernandez, D. C., Herrera, G., Herrmann, M., Hess, C., Hofer, T., Hoffmann, P., Hogquist, K., Holland, T., Hollt, T., Holmdahl, R., Hombrink, P., Houston, J. P., Hoyer, B. F., Huang, B., Huang, F., Huber, J. E., Huehn, J., Hundemer, M., Hunter, C. A., Hwang, W. Y., Iannone, A., Ingelfinger, F., Ivison, S. M., Jack, H., Jani, P. K., Javega, B., Jonjic, S., Kaiser, T., Kalina, T., Kamradt, T., Kaufmann, S. H., Keller, B., Ketelaars, S. L., Khalilnezhad, A., Khan, S., Kisielow, J., Klenerman, P., Knopf, J., Koay, H., Kobow, K., Kolls, J. K., Kong, W. T., Kopf, M., Korn, T., Kriegsmann, K., Kristyanto, H., Kroneis, T., Krueger, A., Kuhne, J., Kukat, C., Kunkel, D., Kunze-Schumacher, H., Kurosaki, T., Kurts, C., Kvistborg, P., Kwok, I., Landry, J., Lantz, O., Lanuti, P., LaRosa, F., Lehuen, A., LeibundGut-Landmann, S., Leipold, M. D., Leung, L. Y., Levings, M. K., Lino, A. C., Liotta, F., Litwin, V., Liu, Y., Ljunggren, H., Lohoff, M., Lombardi, G., Lopez, L., Lopez-Botet, M., Lovett-Racke, A. E., Lubberts, E., Luche, H., Ludewig, B., Lugli, E., Lunemann, S., Maecker, H. T., Maggi, L., Maguire, O., Mair, F., Mair, K. H., Mantovani, A., Manz, R. A., Marshall, A. J., Martinez-Romero, A., Martrus, G., Marventano, I., Maslinski, W., Matarese, G., Mattioli, A. V., Maueroder, C., Mazzoni, A., McCluskey, J., McGrath, M., McGuire, H. M., McInnes, I. B., Mei, H. E., Melchers, F., Melzer, S., Mielenz, D., Miller, S. D., Mills, K. H., Minderman, H., Mjosberg, J., Moore, J., Moran, B., Moretta, L., Mosmann, T. R., Muller, S., Multhoff, G., Munoz, L. E., Munz, C., Nakayama, T., Nasi, M., Neumann, K., Ng, L. G., Niedobitek, A., Nourshargh, S., Nunez, G., O'Connor, J., Ochel, A., Oja, A., Ordonez, D., Orfao, A., Orlowski-Oliver, E., Ouyang, W., Oxenius, A., Palankar, R., Panse, I., Pattanapanyasat, K., Paulsen, M., Pavlinic, D., Penter, L., Peterson, P., Peth, C., Petriz, J., Piancone, F., Pickl, W. F., Piconese, S., Pinti, M., Pockley, A. G., Podolska, M. J., Poon, Z., Pracht, K., Prinz, I., Pucillo, C. E., Quataert, S. A., Quatrini, L., Quinn, K. M., Radbruch, H., Radstake, T. R., Rahmig, S., Rahn, H., Rajwa, B., Ravichandran, G., Raz, Y., Rebhahn, J. A., Recktenwald, D., Reimer, D., Reis E Sousa, C., Remmerswaal, E. B., Richter, L., Rico, L. G., Riddell, A., Rieger, A. M., Robinson, J. P., Romagnani, C., Rubartelli, A., Ruland, J., Saalmuller, A., Saeys, Y., Saito, T., Sakaguchi, S., Sala-de-Oyanguren, F., Samstag, Y., Sanderson, S., Sandrock, I., Santoni, A., Sanz, R. B., Saresella, M., Sautes-Fridman, C., Sawitzki, B., Schadt, L., Scheffold, A., Scherer, H. U., Schiemann, M., Schildberg, F. A., Schimisky, E., Schlitzer, A., Schlosser, J., Schmid, S., Schmitt, S., Schober, K., Schraivogel, D., Schuh, W., Schuler, T., Schulte, R., Schulz, A. R., Schulz, S. R., Scotta, C., Scott-Algara, D., Sester, D. P., Shankey, T. V., Silva-Santos, B., Simon, A. K., Sitnik, K. M., Sozzani, S., Speiser, D. E., Spidlen, J., Stahlberg, A., Stall, A. M., Stanley, N., Stark, R., Stehle, C., Steinmetz, T., Stockinger, H., Takahama, Y., Takeda, K., Tan, L., Tarnok, A., Tiegs, G., Toldi, G., Tornack, J., Traggiai, E., Trebak, M., Tree, T. I., Trotter, J., Trowsdale, J., Tsoumakidou, M., Ulrich, H., Urbanczyk, S., van de Veen, W., van den Broek, M., van der Pol, E., Van Gassen, S., Van Isterdael, G., van Lier, R. A., Veldhoen, M., Vento-Asturias, S., Vieira, P., Voehringer, D., Volk, H., von Borstel, A., von Volkmann, K., Waisman, A., Walker, R. V., Wallace, P. K., Wang, S. A., Wang, X. M., Ward, M. D., Ward-Hartstonge, K. A., Warnatz, K., Warnes, G., Warth, S., Waskow, C., Watson, J. V., Watzl, C., Wegener, L., Weisenburger, T., Wiedemann, A., Wienands, J., Wilharm, A., Wilkinson, R. J., Willimsky, G., Wing, J. B., Winkelmann, R., Winkler, T. H., Wirz, O. F., Wong, A., Wurst, P., Yang, J. H., Yang, J., Yazdanbakhsh, M., Yu, L., Yue, A., Zhang, H., Zhao, Y., Ziegler, S. M., Zielinski, C., Zimmermann, J., Zychlinsky, A. 2019; 49 (10): 1457–1973

  Abstract

  These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

  View details for DOI 10.1002/eji.201970107

  View details for PubMedID 31633216

• Deep immune profiling of peripheral blood reveals a triphasic response and correlations with cognitive outcomes after stroke Buckwalter, M., Tsai, A., Berry, K., Beneyto, M., Gaudilliere, D., Ganio, E., Choisy, B., Djebali, K., Baca, Q., Quach, L., Drag, L., Lansberg, M., Angst, M., Gaudilliere, B., Aghaeepour, N. WILEY. 2019: 170

  View details for Web of Science ID 000478639800424

• Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia. Frontiers in immunology Han, X., Ghaemi, M. S., Ando, K., Peterson, L. S., Ganio, E. A., Tsai, A. S., Gaudilliere, D. K., Stelzer, I. A., Einhaus, J., Bertrand, B., Stanley, N., Culos, A., Tanada, A., Hedou, J., Tsai, E. S., Fallahzadeh, R., Wong, R. J., Judy, A. E., Winn, V. D., Druzin, M. L., Blumenfeld, Y. J., Hlatky, M. A., Quaintance, C. C., Gibbs, R. S., Carvalho, B., Shaw, G. M., Stevenson, D. K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. 2019; 10: 1305

  Abstract

  Preeclampsia is one of the most severe pregnancy complications and a leading cause of maternal death. However, early diagnosis of preeclampsia remains a clinical challenge. Alterations in the normal immune adaptations necessary for the maintenance of a healthy pregnancy are central features of preeclampsia. However, prior analyses primarily focused on the static assessment of select immune cell subsets have provided limited information for the prediction of preeclampsia. Here, we used a high-dimensional mass cytometry immunoassay to characterize the dynamic changes of over 370 immune cell features (including cell distribution and functional responses) in maternal blood during healthy and preeclamptic pregnancies. We found a set of eight cell-specific immune features that accurately identified patients well before the clinical diagnosis of preeclampsia (median area under the curve (AUC) 0.91, interquartile range [0.82-0.92]). Several features recapitulated previously known immune dysfunctions in preeclampsia, such as elevated pro-inflammatory innate immune responses early in pregnancy and impaired regulatory T (Treg) cell signaling. The analysis revealed additional novel immune responses that were strongly associated with, and preceded the onset of preeclampsia, notably abnormal STAT5ab signaling dynamics in CD4+T cell subsets (AUC 0.92, p = 8.0E-5). These results provide a global readout of the dynamics of the maternal immune system early in pregnancy and lay the groundwork for identifying clinically-relevant immune dysfunctions for the prediction and prevention of preeclampsia.

  View details for DOI 10.3389/fimmu.2019.01305

  View details for PubMedID 31263463

  View details for PubMedCentralID PMC6584811

• Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia FRONTIERS IN IMMUNOLOGY Han, X., Ghaemi, M. S., Ando, K., Peterson, L. S., Ganio, E. A., Tsai, A. S., Gaudilliere, D. K., Stelzer, I. A., Einhaus, J., Bertrand, B., Stanley, N., Culos, A., Tanada, A., Hedou, J., Tsai, E. S., Fallahzadeh, R., Wong, R. J., Judy, A. E., Winn, V. D., Druzins, M. L., Blumenfeld, Y. J., Hlatky, M. A., Quaintance, C. C., Gibbs, R. S., Carvalho, B., Shaw, G. M., Stevenson, D. K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. 2019; 10

  View details for DOI 10.3389/fimmu.2019.01305

  View details for Web of Science ID 000470999000001

• A YEAR-LONG IMMUNE PROFILE OF THE SYSTEMIC RESPONSE IN ACUTE STROKE SURVIVORS Tsai, A., Berry, K., Beneyto, M. M., Gaudilliere, D., Ganio, E. A., Culos, A., Ghaemi, M. S., Choisy, B., Djebali, K., Einhaus, J. F., Bertrand, B., Tanada, A., Stanley, N., Fallahzadeh, R., Baca, Q., Quach, L. N., Osborn, E., Drag, L., Lansberg, M., Angst, M., Gaudilliere, B., Buckwalter, M. S., Aghaeepour, N. LIPPINCOTT WILLIAMS & WILKINS. 2019: 155

  View details for Web of Science ID 000480793600335

• DEEP IMMUNE PROFILE OF PREOPERATIVE GLUCOCORTICOID ADMINISTRATION IN PATIENTS UNDERGOING SURGERY Rumer, K., Ganio, E. A., Stanley, N., Einhaus, J., Tsai, A. S., Culos, A., Fallazadeh, R., Lindberg-Larsen, V., Kehlet, H., Angst, M., Aghaeepour, N., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2019: 140

  View details for Web of Science ID 000480793600305

• DEEP IMMUNE PROFILE OF PREOPERATIVE GLUCOCORTICOID ADMINISTRATION IN PATIENTS UNDERGOING SURGERY Gaudilliere, B., Ganio, E. A., Stanley, N., Einhaus, J., Tsai, A. S., Culos, A., Rumer, K., Fallahzadeh, R., Lindberg-Larsen, V., Kehlet, H., Angst, M. S., Aghaeepour, N. LIPPINCOTT WILLIAMS & WILKINS. 2019: 733

  View details for Web of Science ID 000619263200339

• WHOLE SYSTEM IMMUNE PHENOTYPING OF MALE AND FEMALE MICE REVEALS STRIKING SEX SIMILARITIES AND DIFFERENCES IN THE IMMUNE RESPONSE TO INJURY Tawfik, V., Baca, Q., Ganio, E. A., Haight, E., Culos, A., Ghaemi, S., Clark, D., Aghaeepour, N., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2019: 736-737

  View details for Web of Science ID 000619263200341

• How Clinical Flow Cytometry Rebooted Sepsis Immunology CYTOMETRY PART A Monneret, G., Gossez, M., Aghaeepour, N., Gaudilliere, B., Venet, F. 2019; 95A (4): 431–41

  View details for DOI 10.1002/cyto.a.23749

  View details for Web of Science ID 000466792700011

• How Clinical Flow Cytometry Rebooted Sepsis Immunology. Cytometry. Part A : the journal of the International Society for Analytical Cytology Monneret, G., Gossez, M., Aghaeepour, N., Gaudilliere, B., Venet, F. 2019

  Abstract

  On May 2017, the World Health Organization (WHO) recognized sepsis as a global health priority by adopting a resolution to improve the prevention, diagnosis, and management of this deadly disease. While it has long been known that sepsis deeply perturbs immune homeostasis by inducing a tremendous systemic inflammatory response, pivotal observations based on clinical flow cytometry indicate that sepsis indeed initiates a more complex immune response that varies over time, with the concomitant occurrence of both pro- and anti-inflammatory mechanisms. As a resultant, some septic patients enter a stage of protracted immunosuppression. This paved the way for immunostimulation approaches in sepsis. Clinical flow cytometry permitted this evolution by drawing a new picture of pathophysiology and reshaping immune trajectories in patients. Additional information from cytometry by time of flight mass cytometry and other high-dimensional flow cytometry platform should rapidly enrich our understanding of this complex disease. This review reports on landmarks of clinical flow cytometry in sepsis and how this single-cell analysis technique permitted to breach the wall of decades of unfruitful anti-inflammatory-based clinical trials in sepsis. © 2019 International Society for Advancement of Cytometry.

  View details for PubMedID 30887636

• A year-long immune profile of the systemic response in acute stroke survivors. Brain : a journal of neurology Tsai, A. S., Berry, K., Beneyto, M. M., Gaudilliere, D., Ganio, E. A., Culos, A., Ghaemi, M. S., Choisy, B., Djebali, K., Einhaus, J. F., Bertrand, B., Tanada, A., Stanley, N., Fallahzadeh, R., Baca, Q., Quach, L. N., Osborn, E., Drag, L., Lansberg, M. G., Angst, M. S., Gaudilliere, B., Buckwalter, M. S., Aghaeepour, N. 2019

  Abstract

  Stroke is a leading cause of cognitive impairment and dementia, but the mechanisms that underlie post-stroke cognitive decline are not well understood. Stroke produces profound local and systemic immune responses that engage all major innate and adaptive immune compartments. However, whether the systemic immune response to stroke contributes to long-term disability remains ill-defined. We used a single-cell mass cytometry approach to comprehensively and functionally characterize the systemic immune response to stroke in longitudinal blood samples from 24 patients over the course of 1 year and correlated the immune response with changes in cognitive functioning between 90 and 365 days post-stroke. Using elastic net regularized regression modelling, we identified key elements of a robust and prolonged systemic immune response to ischaemic stroke that occurs in three phases: an acute phase (Day 2) characterized by increased signal transducer and activator of transcription 3 (STAT3) signalling responses in innate immune cell types, an intermediate phase (Day 5) characterized by increased cAMP response element-binding protein (CREB) signalling responses in adaptive immune cell types, and a late phase (Day 90) by persistent elevation of neutrophils, and immunoglobulin M+ (IgM+) B cells. By Day 365 there was no detectable difference between these samples and those from an age- and gender-matched patient cohort without stroke. When regressed against the change in the Montreal Cognitive Assessment scores between Days 90 and 365 after stroke, the acute inflammatory phase Elastic Net model correlated with post-stroke cognitive trajectories (r = -0.692, Bonferroni-corrected P = 0.039). The results demonstrate the utility of a deep immune profiling approach with mass cytometry for the identification of clinically relevant immune correlates of long-term cognitive trajectories.

  View details for DOI 10.1093/brain/awz022

  View details for PubMedID 30860258

• Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia. Han, X., Ghaemi, M. S., Ando, K., Peterson, L., Ganio, E. A., Tsai, A. S., Gaudilliere, D., Einhaus, J., Tsai, E. S., Stanley, N. M., Culos, A., Taneda, A. H., Fallahzadeh, R., Wong, R. J., Winn, V. D., Stevenson, D. K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. SAGE PUBLICATIONS INC. 2019: 271A

  View details for Web of Science ID 000459610400616

• Understanding health disparities JOURNAL OF PERINATOLOGY Stevenson, D. K., Wong, R. J., Aghaeepour, N., Angst, M. S., Darmstadt, G. L., DiGiulio, D. B., Druzin, M. L., Gaudilliere, B., Gibbs, R. S., Gould, J. B., Katzl, M., Li, J., Moufarrej, M. N., Quaintancel, C. C., Quake, S. R., Reiman, D. A., Shawl, G. M., Snyder, M. P., Wang, X., Wisel, P. H. 2019; 39 (3): 354–58

  View details for DOI 10.1038/s41372-018-0298-1

  View details for Web of Science ID 000459549600003

• A topological view of human CD34(+) cell state trajectories from integrated single-cell output and proteomic data BLOOD Knapp, D. F., Hammond, C. A., Wang, F., Aghaeepour, N., Miller, P. H., Beer, P. A., Pellacani, D., VanInsberghe, M., Hansen, C., Bendall, S. C., Nolan, G. P., Eaves, C. J. 2019; 133 (9): 927–39

  View details for DOI 10.1182/blood-2018-10-878025

  View details for Web of Science ID 000461501400010

• Deep Immune Profiling of the Post-Stroke Peripheral Immune Response Reveals Tri-phasic Response and Correlations With Long-Term Cognitive Outcomes Tsai, A. S., Berry, K., Beneyto, M. M., Gaudilliere, D., Ganio, E. A., Choisy, B., Djebali, K., Baca, Q., Quach, L., Drag, L., Lansberg, M. G., Angst, M. S., Gaudilliere, B., Buckwalter, M. S., Aghaeepour, N. LIPPINCOTT WILLIAMS & WILKINS. 2019

  View details for Web of Science ID 000478733402484

• A topological view of human CD34+ cell state trajectories from integrated single-cell output and proteomic data. Blood Knapp, D. J., Hammond, C. A., Wang, F., Aghaeepour, N., Miller, P. H., Beer, P. A., Pellacani, D., VanInsberghe, M., Hansen, C., Bendall, S. C., Nolan, G. P., Eaves, C. J. 2019

  Abstract

  Recent advances in single-cell molecular analytical methods and clonal growth assays are enabling more refined models of human hematopoietic lineage restriction processes to be conceptualized. Here, we report the results of integrating single-cell proteome measurements with clonally-determined lymphoid, neutrophilic/monocytic, and/or erythroid progeny outputs from over 1,000 index-sorted CD34+ human cord blood cells in short-term cultures with and without stromal cells. Surface phenotypes of functionally examined cells were individually mapped onto a molecular landscape of the entire CD34+ compartment constructed from single-cell mass cytometric measurements of 14 cell surface markers, 20 signaling/cell cycle proteins and 6 transcription factors in approximately 300,000 cells. This analysis demonstrated that conventionally defined subsets of CD34+ CB cells are quite heterogeneous in their functional properties, transcription factor content, and signaling activities. Importantly, this molecular heterogeneity was reduced, but not eliminated in phenotypes that we showed display highly restricted lineage outputs. Integration of the complete proteomic and functional datasets obtained revealed a continuous probabilistic topology of change that includes a multiplicity of lineage restriction trajectories. Each of these reflects progressive but variable changes in the levels of specific signaling intermediates and transcription factors, but shared features of decreasing quiescence. Taken together, our results suggest a model in which increasingly narrowed hematopoietic output capabilities in neonatal CD34+ cord blood cells are determined by a history of external stimulation in combination with innately programmed cell state changes.

  View details for PubMedID 30622121

• Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy BIOINFORMATICS Ghaemi, M., DiGiulio, D. B., Contrepois, K., Callahan, B., Ngo, T. T. M., Lee-McMullen, B., Lehallier, B., Robaczewska, A., Mcilwain, D., Rosenberg-Hasson, Y., Wong, R. J., Quaintance, C., Culos, A., Stanley, N., Tanada, A., Tsai, A., Gaudilliere, D., Ganio, E., Han, X., Ando, K., McNeil, L., Tingle, M., Wise, P., Maric, I., Sirota, M., Wyss-Coray, T., Winn, V. D., Druzin, M. L., Gibbs, R., Darmstadt, G. L., Lewis, D. B., Nia, V., Agard, B., Tibshirani, R., Nolan, G., Snyder, M. P., Relman, D. A., Quake, S. R., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2019; 35 (1): 95–103

  View details for DOI 10.1093/bioinformatics/bty537

  View details for Web of Science ID 000459313900012

• Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics (Oxford, England) Ghaemi, M. S., DiGiulio, D. B., Contrepois, K., Callahan, B., Ngo, T. T., Lee-McMullen, B., Lehallier, B., Robaczewska, A., Mcilwain, D., Rosenberg-Hasson, Y., Wong, R. J., Quaintance, C., Culos, A., Stanley, N., Tanada, A., Tsai, A., Gaudilliere, D., Ganio, E., Han, X., Ando, K., McNeil, L., Tingle, M., Wise, P., Maric, I., Sirota, M., Wyss-Coray, T., Winn, V. D., Druzin, M. L., Gibbs, R., Darmstadt, G. L., Lewis, D. B., Partovi Nia, V., Agard, B., Tibshirani, R., Nolan, G., Snyder, M. P., Relman, D. A., Quake, S. R., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2019; 35 (1): 95–103

  Abstract

  Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.Results: We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementation: Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary information: Supplementary data are available at Bioinformatics online.

  View details for PubMedID 30561547

• Systemic Immunologic Consequences of Chronic Periodontitis. Journal of dental research Gaudilliere, D. K., Culos, A. n., Djebali, K. n., Tsai, A. S., Ganio, E. A., Choi, W. M., Han, X. n., Maghaireh, A. n., Choisy, B. n., Baca, Q. n., Einhaus, J. F., Hedou, J. J., Bertrand, B. n., Ando, K. n., Fallahzadeh, R. n., Ghaemi, M. S., Okada, R. n., Stanley, N. n., Tanada, A. n., Tingle, M. n., Alpagot, T. n., Helms, J. A., Angst, M. S., Aghaeepour, N. n., Gaudilliere, B. n. 2019: 22034519857714

  Abstract

  Chronic periodontitis (ChP) is a prevalent inflammatory disease affecting 46% of the US population. ChP produces a profound local inflammatory response to dysbiotic oral microbiota that leads to destruction of alveolar bone and tooth loss. ChP is also associated with systemic illnesses, including cardiovascular diseases, malignancies, and adverse pregnancy outcomes. However, the mechanisms underlying these adverse health outcomes are poorly understood. In this prospective cohort study, we used a highly multiplex mass cytometry immunoassay to perform an in-depth analysis of the systemic consequences of ChP in patients before (n = 28) and after (n = 16) periodontal treatment. A high-dimensional analysis of intracellular signaling networks revealed immune system-wide dysfunctions differentiating patients with ChP from healthy controls. Notably, we observed exaggerated proinflammatory responses to Porphyromonas gingivalis-derived lipopolysaccharide in circulating neutrophils and monocytes from patients with ChP. Simultaneously, natural killer cell responses to inflammatory cytokines were attenuated. Importantly, the immune alterations associated with ChP were no longer detectable 3 wk after periodontal treatment. Our findings demarcate systemic and cell-specific immune dysfunctions in patients with ChP, which can be temporarily reversed by the local treatment of ChP. Future studies in larger cohorts are needed to test the boundaries of generalizability of our results.

  View details for DOI 10.1177/0022034519857714

  View details for PubMedID 31226001

• Predicting Acute Pain After Surgery: A Multivariate Analysis. Annals of surgery Baca, Q. n., Marti, F. n., Poblete, B. n., Gaudilliere, B. n., Aghaeepour, N. n., Angst, M. S. 2019

  Abstract

  To identify perioperative practice patterns that predictably impact postoperative pain.Despite significant advances in perioperative medicine, a significant portion of patients still experience severe pain after major surgery. Postoperative pain is associated with serious adverse outcomes that are costly to patients and society.The presented analysis took advantage of a unique observational data set providing unprecedented detailed pharmacological information. The data were collected by PAIN OUT, a multinational registry project established by the European Commission to improve postoperative pain outcomes. A multivariate approach was used to derive and validate a model predictive of pain on postoperative day 1 (POD1) in 1008 patients undergoing back surgery.The predictive and validated model was highly significant (P = 8.9E-15) and identified modifiable practice patterns. Importantly, the number of nonopioid analgesic drug classes administered during surgery predicted decreased pain on POD1. At least 2 different nonopioid analgesic drug classes (cyclooxygenase inhibitors, acetaminophen, nefopam, or metamizol) were required to provide meaningful pain relief (>30%). However, only a quarter of patients received at least 2 nonanalgesic drug classes during surgery. In addition, the use of very short-acting opioids predicted increased pain on POD1, suggesting room for improvement in the perioperative management of these patients. Although the model was highly significant, it only accounted for a relatively small fraction of the observed variance.The presented analysis offers detailed insight into current practice patterns and reveals modifications that can be implemented in today's clinical practice. Our results also suggest that parameters other than those currently studied are relevant for postoperative pain including biological and psychological variables.

  View details for DOI 10.1097/SLA.0000000000003400

  View details for PubMedID 31188202

• Understanding health disparities. Journal of perinatology : official journal of the California Perinatal Association Stevenson, D. K., Wong, R. J., Aghaeepour, N., Angst, M. S., Darmstadt, G. L., DiGiulio, D. B., Druzin, M. L., Gaudilliere, B., Gibbs, R. S., B Gould, J., Katz, M., Li, J., Moufarrej, M. N., Quaintance, C. C., Quake, S. R., Relman, D. A., Shaw, G. M., Snyder, M. P., Wang, X., Wise, P. H. 2018

  Abstract

  Based upon our recent insights into the determinants of preterm birth, which is the leading cause of death in children under five years of age worldwide, we describe potential analytic frameworks that provides both a common understanding and, ultimately the basis for effective, ameliorative action. Our research on preterm birth serves as an example that the framing of any human health condition is a result of complex interactions between the genome and the exposome. New discoveries of the basic biology of pregnancy, such as the complex immunological and signaling processes that dictate the health and length of gestation, have revealed a complexity in the interactions (current and ancestral) between genetic and environmental forces. Understanding of these relationships may help reduce disparities in preterm birth and guide productive research endeavors and ultimately, effective clinical and public health interventions.

  View details for PubMedID 30560947

• GateFinder: projection-based gating strategy optimization for flow and mass cytometry BIOINFORMATICS Aghaeepour, N., Simonds, E. F., Knapp, D. F., Bruggner, R., Sachs, K., Culos, A., Gherardini, P., Samusik, N., Fragiadakis, G. K., Bendall, S. C., Gaudilliere, B., Angst, M. S., Eaves, C. J., Weiss, W. A., Fantl, W. J., Nolan, G. P. 2018; 34 (23): 4131–33

  View details for DOI 10.1093/bioinformatics/bty430

  View details for Web of Science ID 000453453300027

• Single-Cell Developmental Classification of B-Cell Precursor Acute Lymphoblastic Leukemia at Diagnosis Reveals Predictors of Relapse Sarno, J., Good, Z., Jager, A., Samusik, N., Aghaeepour, N., Simonds, E., White, L., Lacayo, N., Fantl, W., Fazio, G., Gaipa, G., Biondi, A., Tibshirani, R., Bendall, S., Nolan, G., Davis, K. WILEY. 2018: S67–S68

  View details for Web of Science ID 000445195000131

• SINGLE-CELL DEVELOPMENTAL CLASSIFICATION OF B-CELL PRECURSOR ACUTE LYMPHOBLASTIC LEUKEMIA AT DIAGNOSIS REVEALS PREDICTORS OF RELAPSE Davis, K., Good, Z., Sarno, J., Jager, A., Samusik, N., Aghaeepour, N., Simonds, E., White, L., Lacayo, N., Fantl, W., Fazio, G., Gaipa, G., Biondi, A., Tibshirani, R., Bendall, S., Nolan, G. ELSEVIER SCIENCE INC. 2018: S34

  View details for Web of Science ID 000975534800023

• Residential agricultural pesticide exposures and risks of preeclampsia. Environmental research Shaw, G. M., Yang, W., Roberts, E. M., Aghaeepour, N., Mayo, J. A., Weber, K. A., Maric, I., Carmichael, S. L., Winn, V. D., Stevenson, D. K., English, P. B. 2018; 164: 546–55

  Abstract

  We investigated risks of preeclampsia phenotypes from potential residential pesticide exposures, including 543 individual chemicals and 69 physicochemical groupings that were applied in the San Joaquin Valley of California during the study period, 1998-2011. The study population was derived from birth certificate data linked with Office of Statewide Health Planning and Development maternal and infant hospital discharge data. The following numbers of women with preeclampsia phenotypes were identified: 1045 with superimposed (pre-existing hypertension with preeclampsia) preeclampsia (265 with gestational weeks 20-31 and 780 with gestational weeks 32-36); 3471 with severe preeclampsia (824 with gestational weeks 20-31 and 2647 with gestational weeks 32-36); and 2780 with mild preeclampsia (207 with gestational weeks 20-31 and 2573 with gestational weeks 32-36). The reference population for these groups was 197,461 women who did not have diabetes (gestational or pre-existing), did not have any hypertensive disorder, and who delivered at 37 weeks or later. The frequency of any exposure was lower or about the same in each preeclampsia case group (further delineated by gestational age), and month time period, relative to the frequency in reference population controls. Nearly all odds ratios were below 1.0 for these any vs no exposure comparisons. This study showed a general lack of increased risks between a range of agriculture pesticide exposures near women's residences and various preeclampsia phenotypes.

  View details for PubMedID 29614386

• Residential agricultural pesticide exposures and risks of preeclampsia ENVIRONMENTAL RESEARCH Shaw, G. M., Yang, W., Roberts, E. M., Aghaeepour, N., Mayo, J. A., Weber, K. A., Maric, I., Carmichael, S. L., Winn, V. D., Stevenson, D. K., English, P. B. 2018; 164: 546–55

  View details for DOI 10.1016/j.envres.2018.03.020

  View details for Web of Science ID 000431387100065

• Single-cell analysis identifies a CD33(+) subset of human cord blood cells with high regenerative potential NATURE CELL BIOLOGY Knapp, D. F., Hammond, C. A., Hui, T., van Loenhout, M. T. J., Wang, F., Aghaeepour, N., Miller, P. H., Moksa, M., Rabu, G. M., Beer, P. A., Pellacani, D., Humphries, R., Hansen, C., Hirst, M., Eaves, C. J. 2018; 20 (6): 710–20

  Abstract

  Elucidation of the identity and diversity of mechanisms that sustain long-term human blood cell production remains an important challenge. Previous studies indicate that, in adult mice, this property is vested in cells identified uniquely by their ability to clonally regenerate detectable, albeit highly variable levels and types, of mature blood cells in serially transplanted recipients. From a multi-parameter analysis of the molecular features of very primitive human cord blood cells that display long-term cell outputs in vitro and in immunodeficient mice, we identified a prospectively separable CD33+CD34+CD38-CD45RA-CD90+CD49f+ phenotype with serially transplantable, but diverse, cell output profiles. Single-cell measurements of the mitogenic response, and the transcriptional, DNA methylation and 40-protein content of this and closely related phenotypes revealed subtle but consistent differences both within and between each subset. These results suggest that multiple regulatory mechanisms combine to maintain different cell output activities of human blood cell precursors with high regenerative potential.

  View details for PubMedID 29802403

• Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nature medicine Good, Z., Sarno, J., Jager, A., Samusik, N., Aghaeepour, N., Simonds, E. F., White, L., Lacayo, N. J., Fantl, W. J., Fazio, G., Gaipa, G., Biondi, A., Tibshirani, R., Bendall, S. C., Nolan, G. P., Davis, K. L. 2018; 24 (4): 474–83

  Abstract

  Insight into the cancer cell populations that are responsible for relapsed disease is needed to improve outcomes. Here we report a single-cell-based study of B cell precursor acute lymphoblastic leukemia at diagnosis that reveals hidden developmentally dependent cell signaling states that are uniquely associated with relapse. By using mass cytometry we simultaneously quantified 35 proteins involved in B cell development in 60 primary diagnostic samples. Each leukemia cell was then matched to its nearest healthy B cell population by a developmental classifier that operated at the single-cell level. Machine learning identified six features of expanded leukemic populations that were sufficient to predict patient relapse at diagnosis. These features implicated the pro-BII subpopulation of B cells with activated mTOR signaling, and the pre-BI subpopulation of B cells with activated and unresponsive pre-B cell receptor signaling, to be associated with relapse. This model, termed 'developmentally dependent predictor of relapse' (DDPR), significantly improves currently established risk stratification methods. DDPR features exist at diagnosis and persist at relapse. By leveraging a data-driven approach, we demonstrate the predictive value of single-cell 'omics' for patient stratification in a translational setting and provide a framework for its application to human cancer.

  View details for PubMedID 29505032

• ASSESSMENT OF MATERNAL PERIPHERAL IMMUNE SYSTEM BY MASS CYTOMETRY TO PREDICT THE ONSET OF LABOR Ando, K., Han, X., Ghaemi, S., Angst, M., Carvalho, B., Aghaeepour, N., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2018: 403

  View details for Web of Science ID 000460106500230

• Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse NATURE MEDICINE Good, Z., Sarno, J., Jager, A., Samusik, N., Aghaeepour, N., Simonds, E. F., White, L., Lacayo, N. J., Fantl, W. J., Fazio, G., Gaipa, G., Biondi, A., Tibshirani, R., Bendall, S. C., Nolan, G. P., Davis, K. L. 2018; 24 (4): 474-+

  View details for DOI 10.1038/nm.4505

  View details for Web of Science ID 000429639800018

• Multiomics Analysis of the Immunome, Transcriptome, Microbiome, Proteome, and Metabolome in Term Pregnancy. Ghaemi, S., Angst, M., Gaudilliere, B., Aghaeepour, N., Stanford Univ SAGE PUBLICATIONS INC. 2018: 81A–82A

  View details for Web of Science ID 000429928200075

• Commonly Occurring Cell Subsets in High-Grade Serous Ovarian Tumors Identified by Single-Cell Mass Cytometry CELL REPORTS Gonzalez, V. D., Samusik, N., Chen, T. J., Savig, E. S., Aghaeepour, N., Quigley, D. A., Huang, Y., Giangarra, V., Borowsky, A. D., Hubbard, N. E., Chen, S., Han, G., Ashworth, A., Kipps, T. J., Berek, J. S., Nolan, G. P., Fantl, W. J. 2018; 22 (7): 1875–88

  Abstract

  We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson's diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.

  View details for PubMedID 29444438

• GateFinder: Projection-based Gating Strategy Optimization for Flow and Mass Cytometry. Bioinformatics (Oxford, England) Aghaeepour, N. n., Simonds, E. F., Knapp, D. J., Bruggner, R. n., Sachs, K. n., Culos, A. n., Gherardini, P. F., Samusik, N. n., Fragiadakis, G. n., Bendall, S. n., Gaudilliere, B. n., Angst, M. S., Eaves, C. J., Weiss, W. A., Fantl, W. n., Nolan, G. n. 2018

  Abstract

  High-parameter single-cell technologies can reveal novel cell populations of interest, but studying or validating these populations using lower-parameter methods remains challenging.Here we present GateFinder, an algorithm that enriches high-dimensional cell types with simple, stepwise polygon gates requiring only two markers at a time. A series of case studies of complex cell types illustrates how simplified enrichment strategies can enable more efficient assays, reveal novel biomarkers, and clarify underlying biology.The GateFinder algorithm is implemented as a free and open-source package for BioConductor: https://nalab.stanford.edu/gatefinder.gnolan@stanford.edu or naghaeep@stanford.edu.Supplementary data are available at Bioinformatics online.

  View details for PubMedID 29850785

• Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery JOURNAL OF IMMUNOLOGY Aghaeepour, N., Kin, C., Ganio, E. A., Jensen, K. P., Gaudilliere, D. K., Tingle, M., Tsai, A., Lancero, H. L., Choisy, B., McNeil, L. S., Okada, R., Shelton, A. A., Nolan, G. P., Angst, M. S., Gaudilliere, B. L. 2017; 199 (6): 2171–80

  View details for DOI 10.4049/jimmunol.1700421

  View details for Web of Science ID 000409218000022

• An immune clock of human pregnancy SCIENCE IMMUNOLOGY Aghaeepour, N., Ganio, E. A., Mcilwain, D., Tsai, A. S., Tingle, M., Van Gassen, S., Gaudilliere, D. K., Baca, Q., McNeil, L., Okada, R., Ghaemi, M. S., Furman, D., Wong, R. J., Winn, V. D., Druzin, M. L., El-Sayed, Y. Y., Quaintance, C., Gibbs, R., Darmstadt, G. L., Shaw, G. M., Stevenson, D. K., Tibshirani, R., Nolan, G. P., Lewis, D. B., Angst, M. S., Gaudilliere, B. 2017; 2 (15)

  View details for DOI 10.1126/sciimmunol.aan2946

  View details for Web of Science ID 000434327200004

• MOLECULAR AND BIOLOGICAL ANALYSIS OF HUMAN HEMATOPOIETIC STEM CELLS AT SINGLE-CELL RESOLUTION Eaves, C., Knapp, D. F., Hammond, C. A., Hui, A., van Loenhout, M., Pellacani, D., Wang, F., Miller, P. H., Lorzadeh, A., Aghaeepour, N., Moksa, M., Vaninsberghe, M., Rabu, G. M., Beer, P. A., Humphries, R. K., Bendall, S., Nolan, G. P., Hansen, C., Hirst, M. ELSEVIER SCIENCE INC. 2017: S24

  View details for Web of Science ID 000972742900003

• IDENTIFICATION OF SMALL MOLECULE KINASE INHIBITORS WITH SPECIFIC ACTIVITY IN PEDIATRIC GLIOMA Simonds, E., Aghaeepour, N., Cayanan, G., Park, J., Nolan, G., Weiss, W. OXFORD UNIV PRESS INC. 2017: 27

  View details for Web of Science ID 000402766800112

• Subglottic Stenosis Following Cardiac Surgery With Cardiopulmonary Bypass in Infants and Children. Pediatric critical care medicine Kruse, K. E., Purohit, P. J., Cadman, C. R., Su, F., Aghaeepour, N., Hammer, G. B. 2017; 18 (5): 429-433

  Abstract

  To determine the 1) incidence of subglottic stenosis in infants and children following cardiac surgery with cardiopulmonary bypass and 2) risk factors associated with its development.Retrospective cohort study.Tertiary children's hospital in California.Infants and children who underwent cardiac surgery with cardiopulmonary bypass.Diagnosis of subglottic stenosis by tracheoscopy.The incidence of subglottic stenosis at our institution during the study period was 0.7%. Young age (p = 0.014), prolonged cardiopulmonary bypass (p = 0.03), and prolonged mechanical ventilation (p < 0.01) were associated with the development of subglottic stenosis. Gender, chromosomal anomaly, presence of a cuffed endotracheal tube, and lowest core temperature during cardiopulmonary bypass were not associated with the development of subglottic stenosis.The incidence of subglottic stenosis was less than that previously reported in this population. Although the incidence is relatively low, subglottic stenosis is a serious complication of tracheal intubation and all measures to prevent subglottic stenosis should be undertaken.

  View details for DOI 10.1097/PCC.0000000000001125

  View details for PubMedID 28277376

• Multicenter Systems Analysis of Human Blood Reveals Immature Neutrophils in Males and During Pregnancy. Journal of immunology Blazkova, J., Gupta, S., Liu, Y., Gaudilliere, B., Ganio, E. A., Bolen, C. R., Saar-Dover, R., Fragiadakis, G. K., Angst, M. S., Hasni, S., Aghaeepour, N., Stevenson, D., Baldwin, N., Anguiano, E., Chaussabel, D., Altman, M. C., Kaplan, M. J., Davis, M. M., Furman, D. 2017; 198 (6): 2479-2488

  Abstract

  Despite clear differences in immune system responses and in the prevalence of autoimmune diseases between males and females, there is little understanding of the processes involved. In this study, we identified a gene signature of immature-like neutrophils, characterized by the overexpression of genes encoding for several granule-containing proteins, which was found at higher levels (up to 3-fold) in young (20-30 y old) but not older (60 to >89 y old) males compared with females. Functional and phenotypic characterization of peripheral blood neutrophils revealed more mature and responsive neutrophils in young females, which also exhibited an elevated capacity in neutrophil extracellular trap formation at baseline and upon microbial or sterile autoimmune stimuli. The expression levels of the immature-like neutrophil signature increased linearly with pregnancy, an immune state of increased susceptibility to certain infections. Using mass cytometry, we also find increased frequencies of immature forms of neutrophils in the blood of women during late pregnancy. Thus, our findings show novel sex differences in innate immunity and identify a common neutrophil signature in males and in pregnant women.

  View details for DOI 10.4049/jimmunol.1601855

  View details for PubMedID 28179497

• Distinct signaling programs control human hematopoietic stem cell survival and proliferation. Blood Knapp, D. J., Hammond, C. A., Aghaeepour, N., Miller, P. H., Pellacani, D., Beer, P. A., Sachs, K., Qiao, W., Wang, W., Humphries, R. K., Sauvageau, G., Zandstra, P. W., Bendall, S. C., Nolan, G. P., Hansen, C., Eaves, C. J. 2017; 129 (3): 307-318

  Abstract

  Several growth factors (GFs) that together promote quiescent human hematopoietic stem cell (HSC) expansion ex vivo have been identified; however, the molecular mechanisms by which these GFs regulate the survival, proliferation. and differentiation of human HSCs remain poorly understood. We now describe experiments in which we used mass cytometry to simultaneously measure multiple surface markers, transcription factors, active signaling intermediates, viability, and cell-cycle indicators in single CD34(+) cord blood cells before and up to 2 hours after their stimulation with stem cell factor, Fms-like tyrosine kinase 3 ligand, interleukin-3, interleukin-6, and granulocyte colony-stimulating factor (5 GFs) either alone or combined. Cells with a CD34(+)CD38(-)CD45RA(-)CD90(+)CD49f(+) (CD49f(+)) phenotype (∼10% HSCs with >6-month repopulating activity in immunodeficient mice) displayed rapid increases in activated STAT1/3/5, extracellular signal-regulated kinase 1/2, AKT, CREB, and S6 by 1 or more of these GFs, and β-catenin only when the 5 GFs were combined. Certain minority subsets within the CD49f(+) compartment were poorly GF-responsive and, among the more GF-responsive subsets of CD49f(+) cells, different signaling intermediates correlated with the levels of the myeloid- and lymphoid-associated transcription factors measured. Phenotypically similar, but CD90(-)CD49f(-) cells (MPPs) contained lower baseline levels of multiple signaling intermediates than the CD90(+)CD49f(+) cells, but showed similar response amplitudes to the same GFs. Importantly, we found activation or inhibition of AKT and β-catenin directly altered immediate CD49f(+) cell survival and proliferation. These findings identify rapid signaling events that 5 GFs elicit directly in the most primitive human hematopoietic cell types to promote their survival and proliferation.

  View details for DOI 10.1182/blood-2016-09-740654

  View details for PubMedID 27827829

• A Proteomic Clock of Human Pregnancy. American journal of obstetrics and gynecology Aghaeepour, N. n., Lehallier, B. n., Baca, Q. n., Ganio, E. A., Wong, R. J., Ghaemi, M. S., Culos, A. n., El-Sayed, Y. Y., Blumenfeld, Y. J., Druzin, M. L., Winn, V. D., Gibbs, R. S., Tibshirani, R. n., Shaw, G. M., Stevenson, D. K., Gaudilliere, B. n., Angst, M. S. 2017

  Abstract

  Early detection of maladaptive processes underlying pregnancy-related pathologies is desirable, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analysis of plasma proteins features prominently among molecular approaches used to detect deviations from normal pregnancy. However, derivation of proteomic signatures sufficiently predictive of pregnancy-related outcomes has been challenging. An important obstacle hindering such efforts were limitations in assay technology, which prevented the broad examination of the plasma proteome.The recent availability of a highly-multiplexed platform affording the simultaneous measurement of 1,310 plasma proteins opens the door for a more explorative approach. The major aim of this study was to examine whether analysis of plasma collected during gestation of term pregnancy would allow identifying a set of proteins that tightly track gestational age. Establishing precisely-timed plasma proteomic changes during term pregnancy is a critical step in identifying deviations from regular patterns due to fetal and maternal maladaptations. A second aim was to gain insight into functional attributes of identified proteins, and link such attributes to relevant immunological changes.Pregnant women participated in this longitudinal study. In two subsequent subsets of 21 (training cohort) and 10 (validation cohort) women, specific blood specimens were collected during the first (7-14 wks), second (15-20 wks), and third (24-32 wks) trimesters, and 6 wks post-partum for analysis with a highly-multiplexed aptamer-based platform. An elastic net algorithm was applied to infer a proteomic model predicting gestational age. A bootstrapping procedure and piece-wise regression analysis was used to extract the minimum number of proteins required for predicting gestational age without compromising predictive power. Gene ontology analysis was applied to infer enrichment of molecular functions among proteins included in the proteomic model. Changes in abundance of proteins with such functions were linked to immune features predictive of gestational age at the time of sampling in pregnancies delivering at term.An independently validated model consisting of 74 proteins strongly predicted gestational age (p = 3.8x10-14, R = 0.97). The model could be reduced to eight proteins without losing its predictive power (p = 1.7x10-3, R = 0.91). The three top ranked proteins were glypican 3, chorionic somatomammotropin hormone, and granulins. Proteins activating the Janus kinase (JAK) and signal transducer and activator of transcription (STAT) pathway were enriched in the proteomic model, chorionic somatomammotropin hormone being the top ranked protein. Abundance of chorionic somatomammotropin hormone strongly correlated with STAT5 signaling activity in CD4 T cells, the endogenous cell-signaling event most predictive of gestational age.Results indicate that precisely timed changes in the plasma proteome during term pregnancy mirror a "proteomic clock". Importantly, the combined use of several plasma proteins was required for accurate prediction. The exciting promise of such a "clock" is that deviations from its regular chronological profile may assist in the early diagnoses of pregnancy-relate pathologies and point to underlying pathophysiology. Functional analysis of the proteomic model generated the novel hypothesis that somatomammotropin hormone may critically regulate T-cell function during pregnancy.

  View details for PubMedID 29277631

• SINGLE-CELL ANALYSIS AND MODELLING OF CELL POPULATION HETEROGENEITY Samusik, N., Aghaeepour, N., Bendall, S. edited by Altman, R. B., Dunker, A. K., Hunter, L., Ritchie, M. D., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2017: 557–63

  View details for Web of Science ID 000391254200051

• Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery. Journal of immunology (Baltimore, Md. : 1950) Aghaeepour, N. n., Kin, C. n., Ganio, E. A., Jensen, K. P., Gaudilliere, D. K., Tingle, M. n., Tsai, A. n., Lancero, H. L., Choisy, B. n., McNeil, L. S., Okada, R. n., Shelton, A. A., Nolan, G. P., Angst, M. S., Gaudilliere, B. L. 2017

  Abstract

  Application of high-content immune profiling technologies has enormous potential to advance medicine. Whether these technologies reveal pertinent biology when implemented in interventional clinical trials is an important question. The beneficial effects of preoperative arginine-enriched dietary supplements (AES) are highly context specific, as they reduce infection rates in elective surgery, but possibly increase morbidity in critically ill patients. This study combined single-cell mass cytometry with the multiplex analysis of relevant plasma cytokines to comprehensively profile the immune-modifying effects of this much-debated intervention in patients undergoing surgery. An elastic net algorithm applied to the high-dimensional mass cytometry dataset identified a cross-validated model consisting of 20 interrelated immune features that separated patients assigned to AES from controls. The model revealed wide-ranging effects of AES on innate and adaptive immune compartments. Notably, AES increased STAT1 and STAT3 signaling responses in lymphoid cell subsets after surgery, consistent with enhanced adaptive mechanisms that may protect against postsurgical infection. Unexpectedly, AES also increased ERK and P38 MAPK signaling responses in monocytic myeloid-derived suppressor cells, which was paired with their pronounced expansion. These results provide novel mechanistic arguments as to why AES may exert context-specific beneficial or adverse effects in patients with critical illness. This study lays out an analytical framework to distill high-dimensional datasets gathered in an interventional clinical trial into a fairly simple model that converges with known biology and provides insight into novel and clinically relevant cellular mechanisms.

  View details for PubMedID 28794234

• An immune clock of human pregnancy. Science immunology Aghaeepour, N. n., Ganio, E. A., Mcilwain, D. n., Tsai, A. S., Tingle, M. n., Van Gassen, S. n., Gaudilliere, D. K., Baca, Q. n., McNeil, L. n., Okada, R. n., Ghaemi, M. S., Furman, D. n., Wong, R. J., Winn, V. D., Druzin, M. L., El-Sayed, Y. Y., Quaintance, C. n., Gibbs, R. n., Darmstadt, G. L., Shaw, G. M., Stevenson, D. K., Tibshirani, R. n., Nolan, G. P., Lewis, D. B., Angst, M. S., Gaudilliere, B. n. 2017; 2 (15)

  Abstract

  The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies.

  View details for PubMedID 28864494

• Mapping the Fetomaternal Peripheral Immune System at Term Pregnancy. Journal of immunology Fragiadakis, G. K., Baca, Q. J., Gherardini, P. F., Ganio, E. A., Gaudilliere, D. K., Tingle, M., Lancero, H. L., McNeil, L. S., Spitzer, M. H., Wong, R. J., Shaw, G. M., Darmstadt, G. L., Sylvester, K. G., Winn, V. D., Carvalho, B., Lewis, D. B., Stevenson, D. K., Nolan, G. P., Aghaeepour, N., Angst, M. S., Gaudilliere, B. L. 2016

  Abstract

  Preterm labor and infections are the leading causes of neonatal deaths worldwide. During pregnancy, immunological cross talk between the mother and her fetus is critical for the maintenance of pregnancy and the delivery of an immunocompetent neonate. A precise understanding of healthy fetomaternal immunity is the important first step to identifying dysregulated immune mechanisms driving adverse maternal or neonatal outcomes. This study combined single-cell mass cytometry of paired peripheral and umbilical cord blood samples from mothers and their neonates with a graphical approach developed for the visualization of high-dimensional data to provide a high-resolution reference map of the cellular composition and functional organization of the healthy fetal and maternal immune systems at birth. The approach enabled mapping of known phenotypical and functional characteristics of fetal immunity (including the functional hyperresponsiveness of CD4(+) and CD8(+) T cells and the global blunting of innate immune responses). It also allowed discovery of new properties that distinguish the fetal and maternal immune systems. For example, examination of paired samples revealed differences in endogenous signaling tone that are unique to a mother and her offspring, including increased ERK1/2, MAPK-activated protein kinase 2, rpS6, and CREB phosphorylation in fetal Tbet(+)CD4(+) T cells, CD8(+) T cells, B cells, and CD56(lo)CD16(+) NK cells and decreased ERK1/2, MAPK-activated protein kinase 2, and STAT1 phosphorylation in fetal intermediate and nonclassical monocytes. This highly interactive functional map of healthy fetomaternal immunity builds the core reference for a growing data repository that will allow inferring deviations from normal associated with adverse maternal and neonatal outcomes.

  View details for PubMedID 27793998

• In Reply. Anesthesiology Angst, M. S., Fragiadakis, G. K., Gaudillière, B., Aghaeepour, N., Nolan, G. P. 2016; 124 (6): 1414-1415

  View details for DOI 10.1097/ALN.0000000000001091

  View details for PubMedID 27187126

• SYSTEMS-WIDE MODULATION OF PATIENTS IMMUNE RESPONSE TO SURGERY BY PRE-OPERATIVE IMMUNE ENHANCING NUTRIENTS Gaudilliere, B., Aghaeepour, N., Ganio, E., Lancero, H., Gaudilliere, D., McNeil, L., Tingle, M., Angst, M. LIPPINCOTT WILLIAMS & WILKINS. 2016: 31–32

  View details for Web of Science ID 000377692000058

• PERIOPERATIVE TLR4 MODULATION IN A MOUSE MODEL OF SURGICAL TRAUMA: A SYSTEMS-WIDE ANALYSIS BY SINGLE-CELL MASS CYTOMETRY Tawfik, V. L., Ganio, E. A., Aghaeepour, N., Angst, M. S., Clark, D. J., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2016

  View details for Web of Science ID 000403582200387

• COMPLEX MODULATION OF THE IMMUNE RESPONSE TO SURGERY BY IMMUNE ENHANCING NUTRIENTS Gaudilliere, B., Aghaeepour, N., Ganio, E. A., Lancero, H., McNeil, L., Tingle, M. S., Angst, M. S. LIPPINCOTT WILLIAMS & WILKINS. 2016

  View details for Web of Science ID 000403582200252

• Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium SCIENTIFIC REPORTS Finak, G., Langweiler, M., Jaimes, M., Malek, M., Taghiyar, J., Korin, Y., Raddassi, K., Devine, L., Obermoser, G., Pekalski, M. L., Pontikos, N., Diaz, A., Heck, S., Villanova, F., Terrazzini, N., Kern, F., Qian, Y., Stanton, R., Wang, K., Brandes, A., Ramey, J., Aghaeepour, N., Mosmann, T., Scheuermann, R. H., Reed, E., Palucka, K., pascual, V., Blomberg, B. B., Nestle, F., Nussenblatt, R. B., Brinkman, R. R., Gottardo, R., Maecker, H., McCoy, J. P. 2016; 6

  Abstract

  Standardization of immunophenotyping requires careful attention to reagents, sample handling, instrument setup, and data analysis, and is essential for successful cross-study and cross-center comparison of data. Experts developed five standardized, eight-color panels for identification of major immune cell subsets in peripheral blood. These were produced as pre-configured, lyophilized, reagents in 96-well plates. We present the results of a coordinated analysis of samples across nine laboratories using these panels with standardized operating procedures (SOPs). Manual gating was performed by each site and by a central site. Automated gating algorithms were developed and tested by the FlowCAP consortium. Centralized manual gating can reduce cross-center variability, and we sought to determine whether automated methods could streamline and standardize the analysis. Within-site variability was low in all experiments, but cross-site variability was lower when central analysis was performed in comparison with site-specific analysis. It was also lower for clearly defined cell subsets than those based on dim markers and for rare populations. Automated gating was able to match the performance of central manual analysis for all tested panels, exhibiting little to no bias and comparable variability. Standardized staining, data collection, and automated gating can increase power, reduce variability, and streamline analysis for immunophenotyping.

  View details for DOI 10.1038/srep20686

  View details for PubMedID 26861911

• A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes CYTOMETRY PART A Aghaeepour, N., Chattopadhyay, P., Chikina, M., Dhaene, T., Van Gassen, S., Kursa, M., Lambrecht, B. N., Malek, M., McLachlan, G. J., Qian, Y., Qiu, P., Saeys, Y., Stanton, R., Tong, D., Vens, C., Walkowiak, S., Wang, K., Finak, G., Gottardo, R., Mosmann, T., Nolan, G. P., Scheuermann, R. H., Brinkman, R. R. 2016; 89A (1): 16-21

  Abstract

  The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.

  View details for DOI 10.1002/cyto.a.22732

  View details for Web of Science ID 000369061600004

  View details for PubMedCentralID PMC4874734

• SESSION INTRODUCTION. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Samusik, N., Aghaeepour, N., Bendall, S. 2016; 22: 557-563

  Abstract

  Recent technological developments allow gathering single-cell measurements across different domains (genomic, transcriptomics, proteomics, imaging etc). Sophisticated computational algorithms are required in order to harness the power of single-cell data. This session is dedicated to computational methods for single-cell analysis in various biological domains, modelling of population heterogeneity, as well as translational applications of single cell data.

  View details for PubMedID 27897006

• A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry. Part A : the journal of the International Society for Analytical Cytology Aghaeepour, N., Chattopadhyay, P., Chikina, M., Dhaene, T., Van Gassen, S., Kursa, M., Lambrecht, B. N., Malek, M., McLachlan, G. J., Qian, Y., Qiu, P., Saeys, Y., Stanton, R., Tong, D., Vens, C., Walkowiak, S., Wang, K., Finak, G., Gottardo, R., Mosmann, T., Nolan, G. P., Scheuermann, R. H., Brinkman, R. R. 2016; 89 (1): 16-21

  Abstract

  The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.

  View details for DOI 10.1002/cyto.a.22732

  View details for PubMedID 26447924

• Automated analysis of flow cytometry data comes of age. Cytometry. Part A : the journal of the International Society for Analytical Cytology Brinkman, R. R., Aghaeepour, N. n., Finak, G. n., Gottardo, R. n., Mosmann, T. n., Scheuermann, R. H. 2016; 89 (1): 13–15

  View details for PubMedID 26812230

• Patient-specific Immune States before Surgery Are Strong Correlates of Surgical Recovery ANESTHESIOLOGY Fragiadakis, G. K., Gaudilliere, B., Ganio, E. A., Aghaeepour, N., Tingle, M., Nolan, G. P., Angst, M. S. 2015; 123 (6): 1241-1255

  Abstract

  Recovery after surgery is highly variable. Risk-stratifying patients based on their predicted recovery profile will afford individualized perioperative management strategies. Recently, application of mass cytometry in patients undergoing hip arthroplasty revealed strong immune correlates of surgical recovery in blood samples collected shortly after surgery. However, the ability to interrogate a patient's immune state before surgery and predict recovery is highly desirable in perioperative medicine.To evaluate a patient's presurgical immune state, cell-type-specific intracellular signaling responses to ex vivo ligands (lipopolysaccharide, interleukin [IL]-6, IL-10, and IL-2/granulocyte macrophage colony-stimulating factor) were quantified by mass cytometry in presurgical blood samples. Selected ligands modulate signaling processes perturbed by surgery. Twenty-three cell surface and 11 intracellular markers were used for the phenotypic and functional characterization of major immune cell subsets. Evoked immune responses were regressed against patient-centered outcomes, contributing to protracted recovery including functional impairment, postoperative pain, and fatigue.Evoked signaling responses varied significantly and defined patient-specific presurgical immune states. Eighteen signaling responses correlated significantly with surgical recovery parameters (|R| = 0.37 to 0.70; false discovery rate < 0.01). Signaling responses downstream of the toll-like receptor 4 in cluster of differentiation (CD) 14 monocytes were particularly strong correlates, accounting for 50% of observed variance. Immune correlates identified in presurgical blood samples mirrored correlates identified in postsurgical blood samples.Convergent findings in pre- and postsurgical analyses provide validation of reported immune correlates and suggest a critical role of the toll-like receptor 4 signaling pathway in monocytes for the clinical recovery process. The comprehensive assessment of patients' preoperative immune state is promising for predicting important recovery parameters and may lead to clinical tests using standard flow cytometry.

  View details for DOI 10.1097/ALN.0000000000000887

  View details for PubMedID 26655308

• Deletions in the cytoplasmic domain of iRhom1 and iRhom2 promote shedding of the TNF receptor by the protease ADAM17 SCIENCE SIGNALING Maney, S. K., Mcllwain, D. R., Polz, R., Pandyra, A. A., Sundaram, B., Wolff, D., Ohishi, K., Maretzky, T., Brooke, M. A., Evers, A., Vasudevan, A. A., Aghaeepour, N., Scheller, J., Muenk, C., Haeussinger, D., Mak, T. W., Nolan, G. P., Kelsell, D. P., Blobel, C. P., Lang, K. S., Lang, P. A. 2015; 8 (401)

  Abstract

  The protease ADAM17 (a disintegrin and metalloproteinase 17) catalyzes the shedding of various transmembrane proteins from the surface of cells, including tumor necrosis factor (TNF) and its receptors. Liberation of TNF receptors (TNFRs) from cell surfaces can dampen the cellular response to TNF, a cytokine that is critical in the innate immune response and promotes programmed cell death but can also promote sepsis. Catalytically inactive members of the rhomboid family of proteases, iRhom1 and iRhom2, mediate the intracellular transport and maturation of ADAM17. Using a genetic screen, we found that the presence of either iRhom1 or iRhom2 lacking part of their extended amino-terminal cytoplasmic domain (herein referred to as ΔN) increases ADAM17 activity, TNFR shedding, and resistance to TNF-induced cell death in fibrosarcoma cells. Inhibitors of ADAM17, but not of other ADAM family members, prevented the effects of iRhom-ΔN expression. iRhom1 and iRhom2 were functionally redundant, suggesting a conserved role for the iRhom amino termini. Cells from patients with a dominantly inherited cancer susceptibility syndrome called tylosis with esophageal cancer (TOC) have amino-terminal mutations in iRhom2. Keratinocytes from TOC patients exhibited increased TNFR1 shedding compared with cells from healthy donors. Our results explain how loss of the amino terminus in iRhom1 and iRhom2 impairs TNF signaling, despite enhancing ADAM17 activity, and may explain how mutations in the amino-terminal region contribute to the cancer predisposition syndrome TOC.

  View details for DOI 10.1126/scisignal.aac5356

  View details for Web of Science ID 000365866400003

• Implementing Mass Cytometry at the Bedside to Study the Immunological Basis of Human Diseases: Distinctive Immune Features in Patients with a History of Term or Preterm Birth. Cytometry. Part A : the journal of the International Society for Analytical Cytology Gaudillière, B., Ganio, E. A., Tingle, M., Lancero, H. L., Fragiadakis, G. K., Baca, Q. J., Aghaeepour, N., Wong, R. J., Quaintance, C., El-Sayed, Y. Y., Shaw, G. M., Lewis, D. B., Stevenson, D. K., Nolan, G. P., Angst, M. S. 2015; 87 (9): 817-829

  Abstract

  Single-cell technologies have immense potential to shed light on molecular and biological processes that drive human diseases. Mass cytometry (or Cytometry by Time Of Flight mass spectrometry, CyTOF) has already been employed in clinical studies to comprehensively survey patients' circulating immune system. As interest in the "bedside" application of mass cytometry is growing, the delineation of relevant methodological issues is called for. This report uses a newly generated dataset to discuss important methodological considerations when mass cytometry is implemented in a clinical study. Specifically, the use of whole blood samples versus peripheral blood mononuclear cells (PBMCs), design of mass-tagged antibody panels, technical and analytical implications of sample barcoding, and application of traditional and unsupervised approaches to analyze high-dimensional mass cytometry datasets are discussed. A mass cytometry assay was implemented in a cross-sectional study of 19 women with a history of term or preterm birth to determine whether immune traits in peripheral blood differentiate the two groups in the absence of pregnancy. Twenty-seven phenotypic and 11 intracellular markers were simultaneously analyzed in whole blood samples stimulated with lipopolysaccharide (LPS at 0, 0.1, 1, 10, and 100 ng mL(-1) ) to examine dose-dependent signaling responses within the toll-like receptor 4 (TLR4) pathway. Complementary analyses, grounded in traditional or unsupervised gating strategies of immune cell subsets, indicated that the prpS6 and pMAPKAPK2 responses in classical monocytes are accentuated in women with a history of preterm birth (FDR<1%). The results suggest that women predisposed to preterm birth may be prone to mount an exacerbated TLR4 response during the course of pregnancy. This important hypothesis-generating finding points to the power of single-cell mass cytometry to detect biologically important differences in a relatively small patient cohort. © 2015 International Society for Advancement of Cytometry.

  View details for DOI 10.1002/cyto.a.22720

  View details for PubMedID 26190063

• Implementing Mass Cytometry at the Bedside to Study the Immunological Basis of Human Diseases: Distinctive Immune Features in Patients with a History of Term or Preterm Birth CYTOMETRY PART A Gaudilliere, B., Ganio, E. A., Tingle, M., Lancero, H. L., Fragiadakis, G. K., Baca, Q. J., Aghaeepour, N., Wong, R. J., Quaintance, C., El-Sayed, Y. Y., Shaw, G. M., Lewis, D. B., Stevenson, D. K., Nolan, G. P., Angst, M. S. 2015; 87A (9): 817-829

  Abstract

  Single-cell technologies have immense potential to shed light on molecular and biological processes that drive human diseases. Mass cytometry (or Cytometry by Time Of Flight mass spectrometry, CyTOF) has already been employed in clinical studies to comprehensively survey patients' circulating immune system. As interest in the "bedside" application of mass cytometry is growing, the delineation of relevant methodological issues is called for. This report uses a newly generated dataset to discuss important methodological considerations when mass cytometry is implemented in a clinical study. Specifically, the use of whole blood samples versus peripheral blood mononuclear cells (PBMCs), design of mass-tagged antibody panels, technical and analytical implications of sample barcoding, and application of traditional and unsupervised approaches to analyze high-dimensional mass cytometry datasets are discussed. A mass cytometry assay was implemented in a cross-sectional study of 19 women with a history of term or preterm birth to determine whether immune traits in peripheral blood differentiate the two groups in the absence of pregnancy. Twenty-seven phenotypic and 11 intracellular markers were simultaneously analyzed in whole blood samples stimulated with lipopolysaccharide (LPS at 0, 0.1, 1, 10, and 100 ng mL(-1) ) to examine dose-dependent signaling responses within the toll-like receptor 4 (TLR4) pathway. Complementary analyses, grounded in traditional or unsupervised gating strategies of immune cell subsets, indicated that the prpS6 and pMAPKAPK2 responses in classical monocytes are accentuated in women with a history of preterm birth (FDR<1%). The results suggest that women predisposed to preterm birth may be prone to mount an exacerbated TLR4 response during the course of pregnancy. This important hypothesis-generating finding points to the power of single-cell mass cytometry to detect biologically important differences in a relatively small patient cohort. © 2015 International Society for Advancement of Cytometry.

  View details for DOI 10.1002/cyto.a.22720

  View details for Web of Science ID 000360590500009

• Thinking outside the gate: single-cell assessments in multiple dimensions. Immunity Kvistborg, P. n., Gouttefangeas, C. n., Aghaeepour, N. n., Cazaly, A. n., Chattopadhyay, P. K., Chan, C. n., Eckl, J. n., Finak, G. n., Hadrup, S. R., Maecker, H. T., Maurer, D. n., Mosmann, T. n., Qiu, P. n., Scheuermann, R. H., Welters, M. J., Ferrari, G. n., Brinkman, R. R., Britten, C. M. 2015; 42 (4): 591–92

  View details for PubMedID 25902473

• State-of-the-Art in the Computational Analysis of Cytometry Data. Cytometry. Part A : the journal of the International Society for Analytical Cytology Brinkman, R. R., Aghaeepour, N. n., Finak, G. n., Gottardo, R. n., Mosmann, T. n., Scheuermann, R. H. 2015; 87 (7): 591–93

  View details for PubMedID 26111230

• Deletions in the cytoplasmic domain of iRhom1 and iRhom2 promote shedding of the TNF receptor by the protease ADAM17. Science signaling Maney, S. K., McIlwain, D. R., Polz, R., Pandyra, A. A., Sundaram, B., Wolff, D., Ohishi, K., Maretzky, T., Brooke, M. A., Evers, A., Vasudevan, A. A., Aghaeepour, N., Scheller, J., Münk, C., Häussinger, D., Mak, T. W., Nolan, G. P., Kelsell, D. P., Blobel, C. P., Lang, K. S., Lang, P. A. 2015; 8 (401): ra109-?

  Abstract

  The protease ADAM17 (a disintegrin and metalloproteinase 17) catalyzes the shedding of various transmembrane proteins from the surface of cells, including tumor necrosis factor (TNF) and its receptors. Liberation of TNF receptors (TNFRs) from cell surfaces can dampen the cellular response to TNF, a cytokine that is critical in the innate immune response and promotes programmed cell death but can also promote sepsis. Catalytically inactive members of the rhomboid family of proteases, iRhom1 and iRhom2, mediate the intracellular transport and maturation of ADAM17. Using a genetic screen, we found that the presence of either iRhom1 or iRhom2 lacking part of their extended amino-terminal cytoplasmic domain (herein referred to as ΔN) increases ADAM17 activity, TNFR shedding, and resistance to TNF-induced cell death in fibrosarcoma cells. Inhibitors of ADAM17, but not of other ADAM family members, prevented the effects of iRhom-ΔN expression. iRhom1 and iRhom2 were functionally redundant, suggesting a conserved role for the iRhom amino termini. Cells from patients with a dominantly inherited cancer susceptibility syndrome called tylosis with esophageal cancer (TOC) have amino-terminal mutations in iRhom2. Keratinocytes from TOC patients exhibited increased TNFR1 shedding compared with cells from healthy donors. Our results explain how loss of the amino terminus in iRhom1 and iRhom2 impairs TNF signaling, despite enhancing ADAM17 activity, and may explain how mutations in the amino-terminal region contribute to the cancer predisposition syndrome TOC.

  View details for DOI 10.1126/scisignal.aac5356

  View details for PubMedID 26535007

• Microfluidic and mass cytometric analyses of single human hematopoietic stem cells demonstrate distinct proliferation and survival responses activated by differentially signaling growth factors. Knapp, D. J., Aghaeepour, N., Miller, P. H., Rabu, G. M., Beer, P. A., Ricicova, M., Lecault, V., Da Costa, D., VanInsberghe, M., Piret, J., Bendall, S. C., Nolan, G. P., Hansen, C., Eaves, C. J. AMER SOC CELL BIOLOGY. 2014

  View details for Web of Science ID 000352094106091

• Computational analysis optimizes the flow cytometric evaluation for lymphoma. Cytometry. Part B, Clinical cytometry Craig, F. E., Brinkman, R. R., Eyck, S. T., Aghaeepour, N. 2013

  Abstract

  Background: Although many clinical laboratories are adopting higher color flow cytometric assays, the approach to optimizing panel design and data analysis is often traditional and subjective. In order to address the question "What is the best flow cytometric strategy to reliably distinguish germinal center B-cell lymphoma (GC-L) from lymphoid hyperplasia (GC-H)?" we applied a computational tool that identifies target populations correlated with a desired outcome, in this case diagnosis. Design: Cases of GC-H and GC-L with a germinal center phenotype, evaluated by flow cytometric immunophenotyping using CD45, CD20, kappa, lambda, CD19, CD5, CD10, CD38, were analyzed with flowType and RchyOptimyx to construct cellular hierarchies that best distinguished the two diagnostic groups. Results: The population CD5-CD19+CD10+CD38- had the highest predictive power. Manual reanalysis confirmed significantly higher CD10+/CD38-B-cells in GC-L (median 12.44%, range 0.74 - 63.29, n=52) than GC-H (median 0.24%, 0.03 - 4.49, n=48, p=0.0001), but was not entirely specific. Difficulties encountered using this computational approach included the presence of CD10+ granulocytes, continuously variable B-cell expression of CD38, more variable intensity antigen staining in GC-L and inability to assess the contribution of light chain restriction. Conclusion: Computational analysis with construction of cellular hierarchies related to diagnosis helped guide manual analysis of high dimensional flow cytometric data. This approach highlighted the diagnostic utility of CD38 expression in the evaluation of B-cells with a CD10+ GC phenotype. In contrast to computational analysis of non-neoplastic cell populations, evaluation of neoplastic cells must be able to take into consideration increased variability in antigen expression. © 2013 Clinical Cytometry Society.

  View details for DOI 10.1002/cytob.21115

  View details for PubMedID 23873623

• Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery. PloS one Villanova, F., Di Meglio, P., Inokuma, M., Aghaeepour, N., Perucha, E., Mollon, J., Nomura, L., Hernandez-Fuentes, M., Cope, A., Prevost, A. T., Heck, S., Maino, V., Lord, G., Brinkman, R. R., Nestle, F. O. 2013; 8 (7): e65485

  Abstract

  Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.

  View details for DOI 10.1371/journal.pone.0065485

  View details for PubMedID 23843942

  View details for PubMedCentralID PMC3701052

• Ensemble-based prediction of RNA secondary structures. BMC bioinformatics Aghaeepour, N., Hoos, H. H. 2013; 14 (1): 139

  Abstract

  BACKGROUND: Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past ve years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. RESULTS: In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. CONCLUSIONS: Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between false negative and false positive base pair predictions. Finally, AveRNA can make use of arbitrary sets of secondary structure prediction procedures and can therefore be used to leverage improvements in prediction accuracy offered by algorithms and energy models developed in the future. Our data, MATLAB software and a web-based version of AveRNA are publicly available at http://www.cs.ubc.ca/labs/beta/Software/AveRNA.

  View details for DOI 10.1186/1471-2105-14-139

  View details for PubMedID 23617269

• Critical assessment of automated flow cytometry data analysis techniques NATURE METHODS Aghaeepour, N., Finak, G., Hoos, H., Mosmann, T. R., Brinkman, R., Gottardo, R., Scheuermann, R. H. 2013; 10 (3): 228-238

  Abstract

  Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

  View details for DOI 10.1038/NMETH.2365

  View details for Web of Science ID 000316650800018

  View details for PubMedID 23396282

• RchyOptimyx: cellular hierarchy optimization for flow cytometry. Cytometry. Part A : the journal of the International Society for Analytical Cytology Aghaeepour, N., Jalali, A., O'Neill, K., Chattopadhyay, P. K., Roederer, M., Hoos, H. H., Brinkman, R. R. 2012; 81 (12): 1022-1030

  Abstract

  Analysis of high-dimensional flow cytometry datasets can reveal novel cell populations with poorly understood biology. Following discovery, characterization of these populations in terms of the critical markers involved is an important step, as this can help to both better understand the biology of these populations and aid in designing simpler marker panels to identify them on simpler instruments and with fewer reagents (i.e., in resource poor or highly regulated clinical settings). However, current tools to design panels based on the biological characteristics of the target cell populations work exclusively based on technical parameters (e.g., instrument configurations, spectral overlap, and reagent availability). To address this shortcoming, we developed RchyOptimyx (cellular hieraRCHY OPTIMization), a computational tool that constructs cellular hierarchies by combining automated gating with dynamic programming and graph theory to provide the best gating strategies to identify a target population to a desired level of purity or correlation with a clinical outcome, using the simplest possible marker panels. RchyOptimyx can assess and graphically present the trade-offs between marker choice and population specificity in high-dimensional flow or mass cytometry datasets. We present three proof-of-concept use cases for RchyOptimyx that involve 1) designing a panel of surface markers for identification of rare populations that are primarily characterized using their intracellular signature; 2) simplifying the gating strategy for identification of a target cell population; 3) identification of a non-redundant marker set to identify a target cell population.

  View details for DOI 10.1002/cyto.a.22209

  View details for PubMedID 23044634

• Improved accuracy and reproducibility of enumeration of platelet-monocyte complexes through use of doublet-discriminator strategy CYTOMETRY PART B-CLINICAL CYTOMETRY Streitz, M., Fuhrmann, S., Thomas, D., Cheek, E., Nomura, L., Maecker, H., Martus, P., Aghaeepour, N., Brinkman, R. R., Volk, H., Kern, F. 2012; 82B (6): 360-368

  Abstract

  Recent publications have suggested that altered proportions of functional CD4 T-cell subsets correlate with active pulmonary TB. Also, CD27-expression on tuberculin-activated IFN-γ(+) CD4 T-cells is known to differ significantly between patients with active pulmonary TB and healthy TB-unexposed BCG vaccinees. Here, we explore links between CD4 T-cell phenotype, multiple functional subsets, and control of TB.We examined ex-vivo overnight tuberculin activated CD4 T-cells in regards to CD27-expression and the activation markers, CD154 upregulation, IFN-γ, TNF-α, IL-2, and degranulation in 44 individuals, including cases of clinically active pulmonary TB, and hospital staff with prolonged TB exposure, some of whom had latent TB.Active pulmonary TB generally showed an excess of TNF-α(+) subsets over IFN-γ(+) subsets, paralleled by decreased CD27 expression on activated IFN-γ(+) or CD154(+) CD4 T-cells. The single subset distinguishing best between active pulmonary TB and high TB exposure was CD154(+) /TNF-α(+) / IFN-γ(-) /IL-2(-) /degranulation(-) (AUROC 0.90). The ratio between the frequencies of TNF-α(+) /IFN-γ(+) CD4 T-cells was an effective alternative parameter (AUROC 0.87).Functional subsets and phenotype of tuberculin induced CD4 T-cells differ between stages of TB infection. Predominance of TNF-α(+) CD4 T-cells in active infection suggests an increased effort of the immune system to contain disease.

  View details for DOI 10.1002/cyto.b.21040

  View details for Web of Science ID 000310386300003

• The phenotypic distribution and functional profile of tuberculin-specific CD4 T-cells characterizes different stages of TB infection. Cytometry. Part B, Clinical cytometry Streitz, M., Fuhrmann, S., Thomas, D., Cheek, E., Nomura, L., Maecker, H., Martus, P., Aghaeepour, N., Brinkman, R. R., Volk, H., Kern, F. 2012; 82 (6): 360-368

  Abstract

  Recent publications have suggested that altered proportions of functional CD4 T-cell subsets correlate with active pulmonary TB. Also, CD27-expression on tuberculin-activated IFN-γ(+) CD4 T-cells is known to differ significantly between patients with active pulmonary TB and healthy TB-unexposed BCG vaccinees. Here, we explore links between CD4 T-cell phenotype, multiple functional subsets, and control of TB.We examined ex-vivo overnight tuberculin activated CD4 T-cells in regards to CD27-expression and the activation markers, CD154 upregulation, IFN-γ, TNF-α, IL-2, and degranulation in 44 individuals, including cases of clinically active pulmonary TB, and hospital staff with prolonged TB exposure, some of whom had latent TB.Active pulmonary TB generally showed an excess of TNF-α(+) subsets over IFN-γ(+) subsets, paralleled by decreased CD27 expression on activated IFN-γ(+) or CD154(+) CD4 T-cells. The single subset distinguishing best between active pulmonary TB and high TB exposure was CD154(+) /TNF-α(+) / IFN-γ(-) /IL-2(-) /degranulation(-) (AUROC 0.90). The ratio between the frequencies of TNF-α(+) /IFN-γ(+) CD4 T-cells was an effective alternative parameter (AUROC 0.87).Functional subsets and phenotype of tuberculin induced CD4 T-cells differ between stages of TB infection. Predominance of TNF-α(+) CD4 T-cells in active infection suggests an increased effort of the immune system to contain disease.

  View details for DOI 10.1002/cyto.b.21041

  View details for PubMedID 22961735

• Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays(*) BIOINFORMATICS Aghaeepour, N., Chattopadhyay, P. K., Ganesan, A., O'Neill, K., Zare, H., Jalali, A., Hoos, H. H., Roederer, M., Brinkman, R. R. 2012; 28 (7): 1009-1016

  Abstract

  Polychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted.To address this problem, we developed a computational approach that automatically reveals all possible cell subsets. From tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. We demonstrate the utility of this approach in a large (n=466), retrospective, 14-parameter PFC study of early HIV infection, where we identify three T-cell subsets that strongly predict progression to AIDS (only one of which was identified by an initial manual analysis).The 'flowType: Phenotyping Multivariate PFC Assays' package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/Supplementary data are available at Bioinformatics online.rbrinkman@bccrc.ca.

  View details for DOI 10.1093/bioinformatics/bts082

  View details for Web of Science ID 000302298900014

  View details for PubMedID 22383736

  View details for PubMedCentralID PMC3315712

• Hematopoietic Stem Cell Subtypes Expand Differentially during Development and Display Distinct Lymphopoietic Programs CELL STEM CELL Benz, C., Copley, M. R., Kent, D. G., Wohrer, S., Cortes, A., Aghaeepour, N., Ma, E., Mader, H., Rowe, K., Day, C., Treloar, D., Brinkman, R. R., Eaves, C. J. 2012; 10 (3): 273-283

  Abstract

  Adult hematopoietic stem cells (HSCs) with serially transplantable activity comprise two subtypes. One shows a balanced output of mature lymphoid and myeloid cells; the other appears selectively lymphoid deficient. We now show that both of these HSC subtypes are present in the fetal liver (at a 1:10 ratio) with the rarer, lymphoid-deficient HSCs immediately gaining an increased representation in the fetal bone marrow, suggesting that the marrow niche plays a key role in regulating their ensuing preferential amplification. Clonal analysis of HSC expansion posttransplant showed that both subtypes display an extensive but variable self-renewal activity with occasional interconversion. Clonal analysis of their differentiation programs demonstrated functional and molecular as well as quantitative HSC subtype-specific differences in the lymphoid progenitors they generate but an indistinguishable production of multipotent and myeloid-restricted progenitors. These findings establish a level of heterogeneity in HSC differentiation and expansion control that may have relevance to stem cell populations in other hierarchically organized tissues.

  View details for DOI 10.1016/j.stem.2012.02.007

  View details for Web of Science ID 000301466500009

  View details for PubMedID 22385655

• Automated Analysis of Multidimensional Flow Cytometry Data Improves Diagnostic Accuracy Between Mantle Cell Lymphoma and Small Lymphocytic Lymphoma AMERICAN JOURNAL OF CLINICAL PATHOLOGY Zare, H., Bashashati, A., Kridel, R., Aghaeepour, N., Haffari, G., Connors, J. M., Gascoyne, R. D., Gupta, A., Brinkman, R. R., Weng, A. P. 2012; 137 (1): 75-85

  Abstract

  Mantle cell lymphoma (MCL) and small lymphocytic lymphoma (SLL) exhibit similar but distinct immunophenotypic profiles. Many cases can be diagnosed readily by flow cytometry (FCM) alone; however, ambiguous cases are frequently encountered and necessitate additional studies, including immunohistochemical staining for cyclin D1 and fluorescence in situ hybridization for IgH-CCND1 rearrangement. To determine if greater diagnostic accuracy could be achieved from FCM data alone, we developed an unbiased, machine-based algorithm to identify features that best distinguish between the 2 diseases. By applying conventional diagnostic criteria to the flow cytometry data, we were able to assign 28 of 44 (64%) MCL and 48 of 70 (69%) SLL cases correctly. In contrast, we were able to assign all 44 (100%) MCL and 68 of 70 (97%) SLL cases correctly using a novel set of criteria, as identified by our automated approach. The most discriminating feature was the CD20/CD23 mean fluorescence intensity ratio, and we found unexpectedly that inclusion of FMC7 expression in the diagnostic algorithm actually reduced its accuracy. This study demonstrates that computational methods can be used on existing clinical FCM data to improve diagnostic accuracy and suggests similar computational approaches could be used to identify novel prognostic markers and perhaps subdivide existing or define new diagnostic entities.

  View details for DOI 10.1309/AJCPMMLQ67YOMGEW

  View details for Web of Science ID 000298340600010

  View details for PubMedID 22180480

• Rapid cell population identification in flow cytometry data. Cytometry. Part A : the journal of the International Society for Analytical Cytology Aghaeepour, N., Nikolic, R., Hoos, H. H., Brinkman, R. R. 2011; 79 (1): 6-13

  Abstract

  We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.

  View details for DOI 10.1002/cyto.a.21007

  View details for PubMedID 21182178

  View details for PubMedCentralID PMC3137288