Academic Appointments


All Publications


  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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 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
  • 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

  • Effect of catalyst and reaction conditions on aromatic monomer yields, product distribution, and sugar yields during lignin hydrogenolysis of silver birch wood. Bioresource technology Phongpreecha, T., Christy, K. F., Singh, S. K., Hao, P., Hodge, D. B. 2020; 316: 123907

    Abstract

    The impact of catalyst choice and reaction conditions during catalytic hydrogenolysis of silver birch biomass are assessed for their effect on aromatic monomer yields and selectivities, lignin removal, and sugar yields from enzymatic hydrolysis. At a reaction temperature of 220°C with no supplemental H2, it was demonstrated that both Co/C and Ni/C exhibited aromatic monomer yields of >50%, which were close to the theoretical maximum expected for the lignin based on total beta-O-4 content and exhibited high selectivities for 4-propylguaiacol and 4-propylsyringol. Pd/C exhibited a significantly different set of products, and using a model lignin dimer, showed a product profile that shifted upon inclusion of supplemental H2, suggesting that the generation of surface hydrogen is critical for this catalyst system. Lignin removal during hydrogenolysis could be correlated to glucose yields and inclusion of lignin depolymerizing catalysts significantly improves lignin removal and subsequent enzymatic hydrolysis yields.

    View details for DOI 10.1016/j.biortech.2020.123907

    View details for PubMedID 32739581

  • Impact of dilute acid pretreatment conditions on p-coumarate removal in diverse maize lines. Bioresource technology Saulnier, B. K., Phongpreecha, T., Singh, S. K., Hodge, D. B. 2020; 314: 123750

    Abstract

    Prior work has identified that lignins recovered from dilute acid-pretreated corn stover exhibit superior performance in phenol-formaldehyde resins used in wood adhesive applications when compared to diverse process-modified lignins derived from other sources. This improved performance is hypothesized to be due to the higher content of unsubstituted phenolic groups specifically p-coumarate lignin esters. In this work, a diverse set of corn stover samples are employed that exhibit diversity in p-coumarate content and total lignin content to explore the relationship between dilute acid pretreatment conditions, p-coumarate ester hydrolysis, xylan solubilization, and the resulting glucose enzymatic hydrolysis yields. The goal of this study is to identify pretreatment conditions that preserve a significant fraction of the p-coumarate esters while simultaneously achieving high enzymatic hydrolysis yields. Kinetic parameters for p-coumarate ester hydrolysis were quantified and pretreatment-biomass combinations were identified that result in glucose hydrolysis yields of more than 90% while retaining nearly 50mg p-coumarate/g lignin.

    View details for DOI 10.1016/j.biortech.2020.123750

    View details for PubMedID 32622284

  • 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

  • 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

  • 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

  • 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