Born in France, Dr. Gaudilliere studied Engineering at Ecole Polytechnique before completing an MD-PhD degree from the Harvard-MIT Health Sciences and Technology program and a postdoctoral fellowship at Stanford University (Dr. Garry Nolan laboratory). Research in the Gaudilliere lab combines high parameter mass cytometry (suspension and imaging mass cytometry) with other proteomics approaches to study how the human immune system responds and adapts to physiological or pathological stressors. Ongoing studies focus on several clinical scenarios including, 1) immune mechanisms of surgical recovery and complications (NIGMSR35), 2) pregnancy and preterm birth (NICHDP01, DDCF, BMGF, ITI, MOD), 3) immune dysfunction and outcomes prediction in patients with COVID19 (Fast Grant, CEND award).
Dr. Gaudilliere is a Board-Certified Anesthesiologist and works clinically in the operating room 25% of his time.
Honors & Awards
Promise in biomedical research, Doris Duke Charitable Foundation (2017)
Kozaka research award, International Anesthesia Research Society (2013)
Stanford Society for Physician Scholar's award, SSPS (2011, 2012)
Albert J. Ryan Fellowship, Harvard Medical School (2004)
Carnot Foundation Scholarship, Ecole Polytechnique, Paris, France (2000)
George Lurcy's Fellowship, Harvard University (2000)
BS, Ecole Polytechnique, Engineering (2000)
PhD, Harvard Medical School, Neurobiology (2005)
Medical Education: Harvard Medical School (2009) MA
Residency: Stanford University Anesthesiology Residency (2013) CA
Board Certification: American Board of Anesthesiology, Anesthesia (2014)
Brice Gaudilliere, Gabriela Fragiadakis, Martin Angst, Garry Nolan. "United States Patent S13-373 Flow cytometry methods for the diagnosis of prolonged convalescence after traumatic injury", Leland Stanford Junior University
Azad Bonni, Aryaman Shalizi, Brice Gaudilliere. "United StatesMethods and compositions for modulating synapse formation", Harvard University
Current Research and Scholarly Interests
The advent of high dimensional flow cytometry has revolutionized our ability to study and visualize the human immune system. Our group combines high parameter mass cytometry (a.k.a Cytometry by Time of Flight Mass Spectrometry, CyTOF), with advanced bio-computational methods to study how the human immune system responds and adapts to acute physiological perturbations. The laboratory currently focuses on two clinical scenarios: surgical trauma and pregnancy.
Deep immune profiling of patients undergoing and recovering from surgery: Using high dimensional mass cytometry, we have recently shown that the signaling behavior of specific innate immune cells measured before surgery in patients blood was strongly associated with surgical recovery. Prospective validation of reported immune correlates of surgical recovery are underway. Ongoing work in humans and animal models focuses on the mechanisms by which pre-operative habilitation interventions may alter a patient’s immune state to improve recovery after surgery.
Deep Immune profiling of normal and preterm pregnancy: Our group is an integral component of a multi-disciplinary effort aiming at understanding the mechanisms of preterm birth, and identifying predictive factors of premature delivery. We have now developed a pipeline and the analytical framework to integrate the single-cell analysis of immune signaling networks by mass cytometry and proteomic profiling of secreted serum factors with the precise phenotyping of pregnancy-related clinical outcomes. In a pilot cross-sectional study of non-pregnant women, we identified candidate immune signatures that differentiated women with a history of preterm or term pregnancies. Longitudinal studies in pregnant patients are ongoing to validate these findings.
Detection of Immune Changes as a Result of Surgical Trauma in Human Subject
Surgical trauma triggers a massive inflammatory response. Over time, both the innate and adaptive branches of the immune system are affected by surgical trauma. The purpose of this study to characterize the cellular and molecular mechanisms immune response to surgical trauma. Additionally, detailed information about patients' recovery profile will be recorded over a period of 6 weeks, with the eventual goal of linking immune responses to recovery profiles.
Immune Modulation by Enhanced vs Standard Prehabilitation Program Before Major Surgery
Over 30 million surgeries are performed annually in the US. Up to 30% of surgical patients experience delayed surgical recovery, marked by prolonged post-surgical pain, opioid consumption, and functional impairment, which contributes $8 billion annually to US health care costs. Novel interventions that improve the resolution of pain, minimize opioid exposure, and accelerate functional recovery after surgery are urgently needed. Multi-modal pre-operative optimization programs (or "prehab") integrating exercise, nutrition, and stress reduction have been shown to safely and effectively improve outcomes after surgery. However, no objective biological markers assess prehab effectiveness and are able to tailor prehab programs to individual patients. Surgery is a profound immunological perturbation, during which a complex network of innate and adaptive immune cells is mobilized to organize the recovery process of wound healing, tissue repair, and pain resolution. As such, the in-depth assessment of a patient's immune system before surgery is a promising approach to tailor prehab programs to modifiable biological markers associated with surgical recovery. The primary goal of this clinical trial is to determine the effect of a personalized prehab program on patients immunological status before surgery.
Immune-modulation Effects of an Arginine Rich Nutritional Supplement in Surgical Patients
The primary objective of this study is to characterize the immune-modulatory effects of arginine-rich nutritional supplements in patients undergoing surgery. Numerical and functional changes of all circulating immune cells will be assessed with mass cytometry.
Stanford is currently not accepting patients for this trial. For more information, please contact Julian Silva, MA, 650-724-9341.
Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology.
2023; 14 (1): 4013
Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of>40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimensto mass spectrometry, with minimal modifications to protocols and instrumentation.
View details for DOI 10.1038/s41467-023-39616-w
View details for PubMedID 37419873
Integrated Single-Cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study.
Annals of surgery
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
Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset.
Science translational medicine
2021; 13 (592)
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
- Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions NATURE MACHINE INTELLIGENCE 2020
Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma.
2020; 11 (1): 3737
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
An immune clock of human pregnancy.
2017; 2 (15)
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
Clinical recovery from surgery correlates with single-cell immune signatures.
Science translational medicine
2014; 6 (255): 255ra131-?
Delayed recovery from surgery causes personal suffering and substantial societal and economic costs. Whether immune mechanisms determine recovery after surgical trauma remains ill-defined. Single-cell mass cytometry was applied to serial whole-blood samples from 32 patients undergoing hip replacement to comprehensively characterize the phenotypic and functional immune response to surgical trauma. The simultaneous analysis of 14,000 phosphorylation events in precisely phenotyped immune cell subsets revealed uniform signaling responses among patients, demarcating a surgical immune signature. When regressed against clinical parameters of surgical recovery, including functional impairment and pain, strong correlations were found with STAT3 (signal transducer and activator of transcription), CREB (adenosine 3',5'-monophosphate response element-binding protein), and NF-κB (nuclear factor κB) signaling responses in subsets of CD14(+) monocytes (R = 0.7 to 0.8, false discovery rate <0.01). These sentinel results demonstrate the capacity of mass cytometry to survey the human immune system in a relevant clinical context. The mechanistically derived immune correlates point to diagnostic signatures, and potential therapeutic targets, that could postoperatively improve patient recovery.
View details for DOI 10.1126/scitranslmed.3009701
View details for PubMedID 25253674
Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.
NPJ digital medicine
2023; 6 (1): 171
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
Longitudinal clinical phenotyping of post COVID condition in Mexican adults recovering from severe COVID-19: a prospective cohort study.
Frontiers in medicine
2023; 10: 1236702
Few studies have evaluated the presence of Post COVID-19 conditions (PCC) in people from Latin America, a region that has been heavily afflicted by the COVID-19 pandemic. In this study, we describe the frequency, co-occurrence, predictors, and duration of 23 symptoms in a cohort of Mexican patients with PCC.We prospectively enrolled and followed adult patients hospitalized for severe COVID-19 at a tertiary care centre in Mexico City. The incidence of PCC symptoms was determined using questionnaires. Unsupervised clustering of PCC symptom co-occurrence and Kaplan-Meier analyses of symptom persistence were performed. The effect of baseline clinical characteristics was evaluated using Cox regression models and reported with hazard ratios (HR).We found that amongst 192 patients with PCC, respiratory problems were the most prevalent and commonly co-occurred with functional activity impairment. 56% had ≥5 persistent symptoms. Symptom persistence probability at 360 days 0.78. Prior SARS-CoV-2 vaccination and infection during the Delta variant wave were associated with a shorter duration of PCC. Male sex was associated with a shorter duration of functional activity impairment and respiratory symptoms. Hypertension and diabetes were associated with a longer duration of functional impairment. Previous vaccination accelerated PCC recovery.In our cohort, PCC symptoms were frequent (particularly respiratory and neurocognitive ones) and persistent. Importantly, prior SARS-CoV-2 vaccination resulted in a shorter duration of PCC.
View details for DOI 10.3389/fmed.2023.1236702
View details for PubMedID 37727759
View details for PubMedCentralID PMC10505811
Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine.
AJOG global reports
2023; 3 (3): 100244
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
Spatially resolved immune exhaustion within the alloreactive microenvironment predicts liver transplant rejection.
Allograft rejection is a frequent complication following solid organ transplantation, but defining specific immune subsets mediating alloimmunity has been elusive due to the scarcity of tissue in clinical biopsy specimens. Single cell techniques have emerged as valuable tools for studying mechanisms of disease in complex tissue microenvironments. Here, we developed a highly multiplexed imaging mass cytometry panel, single cell analysis pipeline, and semi-supervised immune cell clustering algorithm to study archival biopsy specimens from 79 liver transplant (LT) recipients with histopathological diagnoses of either no rejection (NR), acute T-cell mediated rejection (TCMR), or chronic rejection (CR). This approach generated a spatially resolved proteomic atlas of 461,816 cells derived from 98 pathologist-selected regions of interest relevant to clinical diagnosis of rejection. We identified 41 distinct cell populations (32 immune and 9 parenchymal cell phenotypes) that defined key elements of the alloimmune microenvironment (AME), identified significant cell-cell interactions, and established higher order cellular neighborhoods. Our analysis revealed that both regulatory (HLA-DR+ Treg) and exhausted T-cell phenotypes (PD1+CD4+ and PD1+CD8+ T-cells), combined with variations in M2 macrophage polarization, were a unique signature of TCMR. TCMR was further characterized by alterations in cell-to-cell interactions among both exhausted immune subsets and inflammatory populations, with expansion of a CD8 enriched cellular neighborhood comprised of Treg, exhausted T-cell subsets, proliferating CD8+ T-cells, and cytotoxic T-cells. These data enabled creation of a predictive model of clinical outcomes using a subset of cell types to differentiate TCMR from NR (AUC = 0.96 ± 0.04) and TCMR from CR (AUC = 0.96 ± 0.06) with high sensitivity and specificity. Collectively, these data provide mechanistic insights into the AME in clinical LT, including a substantial role for immune exhaustion in TCMR with identification of novel targets for more focused immunotherapy in allograft rejection. Our study also offers a conceptual framework for applying spatial proteomics to study immunological diseases in archival clinical specimens.
View details for DOI 10.21203/rs.3.rs-3044385/v1
View details for PubMedID 37461437
View details for PubMedCentralID PMC10350170
Role of Brain Imaging in the Prediction of Intracerebral Hemorrhage Following Endovascular Therapy for Acute Stroke.
Currently most acute ischemic stroke patients presenting with a large vessel occlusion are treated with endovascular therapy (EVT), which results in high rates of successful recanalization. Despite this success, more than half of EVT-treated patients are significantly disabled 3 months later partly due to the occurrence of post-EVT intracerebral hemorrhage. Predicting post-EVT intracerebral hemorrhage is important for individualizing treatment strategies in clinical practice (eg, safe initiation of early antithrombotic therapies), as well as in selecting the optimal candidates for clinical trials that aim to reduce this deleterious outcome. Emerging data suggest that brain and vascular imaging biomarkers may be particularly relevant since they provide insights into the ongoing acute stroke pathophysiology. In this review/perspective, we summarize the accumulating literature on the role of cerebrovascular imaging biomarkers in predicting post-EVT-associated intracerebral hemorrhage. We focus on imaging acquired before EVT, during the EVT procedure, and in the early post-EVT time frames when new therapeutic therapies could be tested. Accounting for the complex pathophysiology of post-EVT-associated intracerebral hemorrhage, this review may provide some guidance for future prospective observational or therapeutic studies.
View details for DOI 10.1161/STROKEAHA.123.040806
View details for PubMedID 37334709
Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.
2023; 9 (21): eade7692
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 2023
A longitudinal examination of parent diagnostic uncertainty in pediatric chronic pain
OXFORD UNIV PRESS INC. 2023: 144-145
View details for Web of Science ID 001061853800310
Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data.
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
Data-driven longitudinal characterization of neonatal health and morbidity.
Science translational medicine
2023; 15 (683): eadc9854
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
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
Neuroimaging is the new "spatial omic": multi-omic approaches to neuro-inflammation and immuno-thrombosis in acute ischemic stroke.
Seminars in immunopathology
Ischemic stroke (IS) is the leading cause of acquired disability and the second leading cause of dementia and mortality. Current treatments for IS are primarily focused on revascularization of the occluded artery. However, only 10% of patients are eligible for revascularization and 50% of revascularized patients remain disabled at 3 months. Accumulating evidence highlight the prognostic significance of the neuro- and thrombo-inflammatory response after IS. However, several randomized trials of promising immunosuppressive or immunomodulatory drugs failed to show positive results. Insufficient understanding of inter-patient variability in the cellular, functional, and spatial organization of the inflammatory response to IS likely contributed to the failure to translate preclinical findings into successful clinical trials. The inflammatory response to IS involves complex interactions between neuronal, glial, and immune cell subsets across multiple immunological compartments, including the blood-brain barrier, the meningeal lymphatic vessels, the choroid plexus, and the skull bone marrow. Here, we review the neuro- and thrombo-inflammatory responses to IS. We discuss how clinical imaging and single-cell omic technologies have refined our understanding of the spatial organization of pathobiological processes driving clinical outcomes in patients with an IS. We also introduce recent developments in machine learning statistical methods for the integration of multi-omic data (biological and radiological) to identify patient-specific inflammatory states predictive of IS clinical outcomes.
View details for DOI 10.1007/s00281-023-00984-6
View details for PubMedID 36786929
High-multiplex tissue imaging in routine pathology-are we there yet?
Virchows Archiv : an international journal of pathology
High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.
View details for DOI 10.1007/s00428-023-03509-6
View details for PubMedID 36757500
- Harnessing the n+1 dimensions of single-cell omics data for the prediction and prevention of human diseases. Seminars in immunopathology 2023; 45 (1): 1-2
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
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.)
2022; 3 (12): 100655
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
Differential dynamics of bone graft transplantation and mesenchymal stem cell therapy during bone defect healing in a murine critical size defect.
Journal of orthopaedic translation
2022; 36: 64-74
Background: A critical size bone defect is a clinical scenario in which bone is lost or excised due to trauma, infection, tumor, or other causes, and cannot completely heal spontaneously. The most common treatment for this condition is autologous bone grafting to the defect site. However, autologous bone graft is often insufficient in quantity or quality for transplantation to these large defects. Recently, tissue engineering methods using mesenchymal stem cells (MSCs) have been proposed as an alternative treatment. However, the underlying biological principles and optimal techniques for tissue regeneration of bone using stem cell therapy have not been completely elucidated.Methods: In this study, we compare the early cellular dynamics of healing between bone graft transplantation and MSC therapy in a murine chronic femoral critical-size bone defect. We employ high-dimensional mass cytometry to provide a comprehensive view of the differences in cell composition, stem cell functionality, and immunomodulatory activity between these two treatment methods one week after transplantation.Results: We reveal distinct cell compositions among tissues from bone defect sites compared with original bone graft, show active recruitment of MSCs to the bone defect sites, and demonstrate the phenotypic diversity of macrophages and T cells in each group that may affect the clinical outcome.Conclusion: Our results provide critical data and future directions on the use of MSCs for treating critical size defects to regenerate bone.Translational Potential of this article: This study showed systematic comparisons of the cellular and immunomodulatory profiles among different interventions to improve the healing of the critical-size bone defect. The results provided potential strategies for designing robust therapeutic interventions for the unmet clinical need of treating critical-size bone defects.
View details for DOI 10.1016/j.jot.2022.05.010
View details for PubMedID 35979174
- Correction to: Establishment of tissue-resident immune populations in the fetus. Seminars in immunopathology 2022
Upcoming and urgent challenges in critical care research based on COVID-19 pandemic experience.
Anaesthesia, critical care & pain medicine
While the coronavirus disease 2019 (COVID-19) pandemic placed a heavy burden on healthcare systems worldwide, it also induced urgent mobilisation of research teams to develop treatments preventing or curing the disease and its consequences. It has, therefore, challenged critical care research to rapidly focus on specific fields while forcing critical care physicians to make difficult ethical decisions. This narrative review aims to summarise critical care research -from organisation to research fields- in this pandemic setting and to highlight opportunities to improve research efficiency in the future, based on what is learned from COVID-19. This pressure on research revealed, i.e., i/ the need to harmonise regulatory processes between countries, allowing simplified organisation of international research networks to improve their efficiency in answering large-scale questions; ii/ the importance of developing translational research from which therapeutic innovations can emerge; iii/ the need for improved triage and predictive scores to rationalise admission to the intensive care unit. In this context, key areas for future critical care research and better pandemic preparedness are artificial intelligence applied to healthcare, characterisation of long-term symptoms, and ethical considerations. Such collaborative research efforts should involve groups from both high and low-to-middle income countries to propose worldwide solutions. As a conclusion, stress tests on healthcare organisations should be viewed as opportunities to design new research frameworks and strategies. Worldwide availability of research networks ready to operate is essential to be prepared for next pandemics. Importantly, researchers and physicians should prioritise realistic and ethical goals for both clinical care and research.
View details for DOI 10.1016/j.accpm.2022.101121
View details for PubMedID 35781076
Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.
Cell reports. Medicine
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
Signature for Pain Recovery IN Teens (SPRINT): protocol for a multisite prospective signature study in chronic musculoskeletal pain.
2022; 12 (6): e061548
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
A data-driven health index for neonatal morbidities.
2022; 25 (4): 104143
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
Association of Prehabilitation With Postoperative Opioid Use in Colorectal Surgery: An Observational Cohort Study.
The Journal of surgical research
1800; 273: 226-232
INTRODUCTION: Preoperative optimization programs have demonstrated positive effects on perioperative physical function and surgical outcomes. In nonsurgical populations, physical activity and healthy diet may reduce pain and pain medication requirement, but this has not been studied in surgical patients. Our aim was to determine whether a preoperative diet and exercise intervention affects postoperative pain and pain medication use.METHODS: Patients undergoing abdominal colorectal surgery were invited to participate in a web-based patient engagement program. Those enrolling in the first and third time periods received information on the standard perioperative pathway (enhanced recovery after surgery [ERAS]). Those enrolling in the second time period also received reminders on nutrition and exercise (PREHAB+ERAS). The primary outcome was postoperative inpatient opioid use. The secondary outcomes were inpatient postoperative pain scores and nonopioid pain medication use.RESULTS: The ERAS and PREHAB+ERAS groups were similar in demographic and operative characteristics. Subgroup analysis of patients who activated their accounts demonstrated that the two groups had similar average maximum daily pain scores, but the PREHAB+ERAS group (n=158) used 15.9 fewer oral morphine equivalents per postoperative inpatient day than the ERAS group (n=92), representing a 30% decrease (53mg versus 37.1mg, P=0.04). The two groups used comparable amounts of acetaminophen, gabapentin, and ketorolac. Generalized linear models demonstrated that PREHAB+ERAS, minimally invasive surgery, and older age were associated with lower inpatient opioid use.CONCLUSIONS: Access to a web-based preoperative diet and exercise program may reduce inpatient opioid use after major elective colorectal surgery. Further studies are necessary to determine whether the degree of adherence to nutrition and physical activity recommendations has a dose-dependent effect on opioid use.
View details for DOI 10.1016/j.jss.2021.12.023
View details for PubMedID 35101683
Perinatal infection, inflammation, preterm birth, and brain injury: A review with proposals for future investigations.
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
Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.
Frontiers in pediatrics
2022; 10: 933266
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
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
- From Mass to Flow: Emerging Sepsis Diagnostics Based on Flow Cytometry Analysis of Neutrophils. American journal of respiratory and critical care medicine 2021
Harnessing the Potential of Multiomics Studies for Precision Medicine in Infectious Disease.
Open forum infectious diseases
2021; 8 (11): ofab483
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
Understanding how biologic and social determinants affect disparities in preterm birth and outcomes of preterm infants in the NICU.
Seminars in perinatology
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
Data-Driven Modeling of Pregnancy-Related Complications.
Trends in molecular medicine
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
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
Human influenza virus challenge identifies cellular correlates of protection for oral vaccination.
Cell host & microbe
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
A Peripheral Immune Signature of Labor Induction.
Frontiers in immunology
2021; 12: 725989
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
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
Single-Cell Analysis of the Neonatal Immune System Across the Gestational Age Continuum.
Frontiers in immunology
2021; 12: 714090
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
Human immune system adaptations to simulated microgravity revealed by single-cell mass cytometry.
2021; 11 (1): 11872
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
Deleterious and Protective Psychosocial and Stress-Related Factors Predict Risk of Spontaneous Preterm Birth.
American journal of perinatology
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
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
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.
2020; 6 (48)
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
2020; 2 (10): 619-628
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
- Towards personalized medicine in maternal and child health: integrating biologic and social determinants. Pediatric research 2020
Multiomic immune clockworks of pregnancy.
Seminars in immunopathology
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
Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries.
JAMA network open
2020; 3 (12): e2029655
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
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
Hypothalamic circuitry underlying stress-induced insomnia and peripheral immunosuppression
2020; 6 (37)
View details for DOI 10.1126/sciadv.abc2590
Systematic Immunophenotyping Reveals Sex-Specific Responses After Painful Injury in Mice.
Frontiers in immunology
2020; 11: 1652
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
VoPo leverages cellular heterogeneity for predictive modeling of single-cell data.
2020; 11 (1): 3738
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
Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities.
Nature reviews. Neurology
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
Changes in pregnancy-related serum biomarkers early in gestation are associated with later development of preeclampsia.
2020; 15 (3): e0230000
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
- 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 2019; 95 (12): 1236–74
CytoNorm: A Normalization Algorithm for Cytometry Data.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
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
2019; 49 (10): 1457–1973
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
- Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia FRONTIERS IN IMMUNOLOGY 2019; 10
- A pilot study showing a stronger H1N1 influenza vaccination response during pregnancy in women who subsequently deliver preterm JOURNAL OF REPRODUCTIVE IMMUNOLOGY 2019; 132: 16–20
- How Clinical Flow Cytometry Rebooted Sepsis Immunology CYTOMETRY PART A 2019; 95A (4): 431–41
A year-long immune profile of the systemic response in acute stroke survivors.
Brain : a journal of neurology
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.
SAGE PUBLICATIONS INC. 2019: 271A
View details for Web of Science ID 000459610400616
- Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy BIOINFORMATICS 2019; 35 (1): 95–103
Predicting Acute Pain After Surgery: A Multivariate Analysis.
Annals of surgery
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
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 (Oxford, England)
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://firstname.lastname@example.org or email@example.com.Supplementary data are available at Bioinformatics online.
View details for PubMedID 29850785
Freehand Versus Guided Surgery: Factors Influencing Accuracy of Dental Implant Placement.
2017; 26 (4): 500-509
Patient anatomy, practitioner experience, and surgical approach are all factors that influence implant accuracy. However, the relative importance of each factor is poorly understood. The present study aimed to identify which factors most critically determine implant accuracy to aid the practitioner in case selection for guided versus freehand surgery.One practitioner's ideal implant angulation and position was compared with his achieved position radiographically for 450 implants placed using a conventional freehand method. The relative contribution of 11 demographic, anatomical, and surgical factors to the accuracy of implant placement was systematically quantified.The most important predictors of angulation and position accuracy were the number of adjacent implants placed and the tooth-borne status of the site. Immediate placement also significantly increased position accuracy, whereas cases with narrow sites were significantly more accurate in angulation. Accuracy also improved with the practitioner's experience.These results suggest tooth-borne, single-implant cases performed later in the practitioner's experience are most appropriate for freehand placement, whereas guided surgery should be considered to improve accuracy for multiple-implant cases in edentulous or partially edentulous sites.
View details for DOI 10.1097/ID.0000000000000620
View details for PubMedID 28731896
Multicenter Systems Analysis of Human Blood Reveals Immature Neutrophils in Males and During Pregnancy.
Journal of immunology
2017; 198 (6): 2479-2488
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
Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states
2017; 23 (2): 174-184
Low-grade, chronic inflammation has been associated with many diseases of aging, but the mechanisms responsible for producing this inflammation remain unclear. Inflammasomes can drive chronic inflammation in the context of an infectious disease or cellular stress, and they trigger the maturation of interleukin-1β (IL-1β). Here we find that the expression of specific inflammasome gene modules stratifies older individuals into two extremes: those with constitutive expression of IL-1β, nucleotide metabolism dysfunction, elevated oxidative stress, high rates of hypertension and arterial stiffness; and those without constitutive expression of IL-1β, who lack these characteristics. Adenine and N(4)-acetylcytidine, nucleotide-derived metabolites that are detectable in the blood of the former group, prime and activate the NLRC4 inflammasome, induce the production of IL-1β, activate platelets and neutrophils and elevate blood pressure in mice. In individuals over 85 years of age, the elevated expression of inflammasome gene modules was associated with all-cause mortality. Thus, targeting inflammasome components may ameliorate chronic inflammation and various other age-associated conditions.
View details for DOI 10.1038/nm.4267
View details for Web of Science ID 000393729000009
View details for PubMedID 28092664
Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery.
Journal of immunology (Baltimore, Md. : 1950)
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
- The road ahead: Implementing mass cytometry in clinical studies, one cell at a time. Cytometry. Part B, Clinical cytometry 2017; 92 (1): 10-11
- Mass cytometry: The time to settle down. Cytometry. Part A : the journal of the International Society for Analytical Cytology 2017; 91 (1): 12-13
A Proteomic Clock of Human Pregnancy.
American journal of obstetrics and gynecology
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
Deep Immune Profiling in Trauma and Sepsis: Flow Is the Way to Go!
Critical care medicine
2017; 45 (9): 1577–78
View details for PubMedID 28816846
Mapping the Fetomaternal Peripheral Immune System at Term Pregnancy.
Journal of immunology
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 2016; 124 (6): 1414-1415
Patient-specific Immune States before Surgery Are Strong Correlates of Surgical Recovery
2015; 123 (6): 1241-1255
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
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
2015; 87 (9): 817-829
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
Transient partial permeabilization with saponin enables cellular barcoding prior to surface marker staining.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
2014; 85 (12): 1011-1019
Fluorescent cellular barcoding and mass-tag cellular barcoding are cytometric methods that enable high sample throughput, minimize inter-sample variation, and reduce reagent consumption. Previously employed barcoding protocols require that barcoding be performed after surface marker staining, complicating combining the technique with measurement of alcohol-sensitive surface epitopes. This report describes a method of barcoding fixed cells after a transient partial permeabilization with 0.02% saponin that results in efficient and consistent barcode staining with fluorescent or mass-tagged reagents while preserving surface marker staining. This approach simplifies barcoding protocols and allows direct comparison of surface marker staining of multiple samples without concern for variations in the antibody cocktail volume, antigen-antibody ratio, or machine sensitivity. Using this protocol, cellular barcoding can be used to reliably detect subtle differences in surface marker expression. © 2014 International Society for Advancement of Cytometry.
View details for DOI 10.1002/cyto.a.22573
View details for PubMedID 25274027
A FOXO-Pak1 transcriptional pathway controls neuronal polarity
GENES & DEVELOPMENT
2010; 24 (8): 799-813
Neuronal polarity is essential for normal brain development and function. However, cell-intrinsic mechanisms that govern the establishment of neuronal polarity remain to be identified. Here, we report that knockdown of endogenous FOXO proteins in hippocampal and cerebellar granule neurons, including in the rat cerebellar cortex in vivo, reveals a requirement for the FOXO transcription factors in the establishment of neuronal polarity. The FOXO transcription factors, including the brain-enriched protein FOXO6, play a critical role in axo-dendritic polarization of undifferentiated neurites, and hence in a switch from unpolarized to polarized neuronal morphology. We also identify the gene encoding the protein kinase Pak1, which acts locally in neuronal processes to induce polarity, as a critical direct target gene of the FOXO transcription factors. Knockdown of endogenous Pak1 phenocopies the effect of FOXO knockdown on neuronal polarity. Importantly, exogenous expression of Pak1 in the background of FOXO knockdown in both primary neurons and postnatal rat pups in vivo restores the polarized morphology of neurons. These findings define the FOXO proteins and Pak1 as components of a cell-intrinsic transcriptional pathway that orchestrates neuronal polarity, thus identifying a novel function for the FOXO transcription factors in a unique aspect of neural development.
View details for DOI 10.1101/gad.1880510
View details for Web of Science ID 000276730300008
View details for PubMedID 20395366
View details for PubMedCentralID PMC2854394
PIASx is a MEF2 SUMO E3 ligase that promotes postsynaptic dendritic morphogenesis
JOURNAL OF NEUROSCIENCE
2007; 27 (37): 10037-10046
Postsynaptic morphogenesis of dendrites is essential for the establishment of neural connectivity in the brain, but the mechanisms that govern postsynaptic dendritic differentiation remain poorly understood. Sumoylation of the transcription factor myocyte enhancer factor 2A (MEF2A) promotes the differentiation of postsynaptic granule neuron dendritic claws in the cerebellar cortex. Here, we identify the protein PIASx as a MEF2 SUMO E3 ligase that represses MEF2-dependent transcription in neurons. Gain-of-function and genetic knockdown experiments in rat cerebellar slices and in the postnatal cerebellum in vivo reveal that PIASx drives the differentiation of granule neuron dendritic claws in the cerebellar cortex. MEF2A knockdown suppresses PIASx-induced dendritic claw differentiation, and expression of sumoylated MEF2A reverses PIASx knockdown-induced loss of dendritic claws. These findings define the PIASx-MEF2 sumoylation signaling link as a key mechanism that orchestrates postsynaptic dendritic claw morphogenesis in the cerebellar cortex and suggest novel functions for SUMO E3 ligases in brain development and plasticity.
View details for DOI 10.1523/JNEUROSCI.0361-07.2007
View details for Web of Science ID 000249415000024
View details for PubMedID 17855618
Transcription factor Sp4 regulates dendritic patterning during cerebellar maturation
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2007; 104 (23): 9882-9887
Integration of inputs by a neuron depends on dendritic arborization patterns. In mammals, the genetic programs that regulate dynamic remodeling of dendrites during development and in response to activity are incompletely understood. Here we report that knockdown of the transcription factor Sp4 led to an increased number of highly branched dendrites during maturation of cerebellar granule neurons in dissociated cultures and in cerebellar cortex. Time-course analysis revealed that depletion of Sp4 led to persistent generation of dendritic branches and a failure in resorption of transient dendrites. Depolarization induced a reduction in the number of dendrites, and knockdown of Sp4 blocked depolarization-induced remodeling. Furthermore, overexpression of Sp4 wild type, but not a mutant lacking the DNA-binding domain, was sufficient to promote dendritic pruning in nondepolarizing conditions. These findings indicate that the transcription factor Sp4 controls dendritic patterning during cerebellar development by limiting branch formation and promoting activity-dependent pruning.
View details for DOI 10.1073/pnas.0701946104
View details for Web of Science ID 000247114100061
View details for PubMedID 17535924
View details for PubMedCentralID PMC1887555
A calcium-regulated MEF2 surnoylation switch controls postsynaptic differentiation
2006; 311 (5763): 1012-1017
Postsynaptic differentiation of dendrites is an essential step in synapse formation. We report here a requirement for the transcription factor myocyte enhancer factor 2A (MEF2A) in the morphogenesis of postsynaptic granule neuron dendritic claws in the cerebellar cortex. A transcriptional repressor form of MEF2A that is sumoylated at lysine-403 promoted dendritic claw differentiation. Activity-dependent calcium signaling induced a calcineurin-mediated dephosphorylation of MEF2A at serine-408 and, thereby, promoted a switch from sumoylation to acetylation at lysine-403, which led to inhibition of dendritic claw differentiation. Our findings define a mechanism underlying postsynaptic differentiation that may modulate activity-dependent synapse development and plasticity in the brain.
View details for DOI 10.1126/science.1122513
View details for Web of Science ID 000235456900049
A CaMKII-NeuroD signaling pathway specifies dendritic morphogenesis
2004; 41 (2): 229-241
The elaboration of dendrites is fundamental to the establishment of neuronal polarity and connectivity, but the mechanisms that underlie dendritic morphogenesis are poorly understood. We found that the genetic knockdown of the transcription factor NeuroD in primary granule neurons including in organotypic cerebellar slices profoundly impaired the generation and maintenance of dendrites while sparing the development of axons. We also found that NeuroD mediated neuronal activity-dependent dendritogenesis. The activity-induced protein kinase CaMKII catalyzed the phosphorylation of NeuroD at distinct sites, including endogenous NeuroD at Ser336 in primary neurons, and thereby stimulated dendritic growth. These findings uncover an essential function for NeuroD in granule neuron dendritic morphogenesis. Our study also defines the CaMKII-NeuroD signaling pathway as a novel mechanism underlying activity-regulated dendritic growth that may play important roles in the developing and mature brain.
View details for Web of Science ID 000221457800009
View details for PubMedID 14741104
Characterization of a neurotrophin signaling mechanism that mediates neuron survival in a temporally specific pattern
JOURNAL OF NEUROSCIENCE
2003; 23 (19): 7326-7336
The temporally specific nature of neurotrophic factor-induced responses is a general feature of mammalian nervous system development, the mechanisms of which remain to be elucidated. We characterized a mechanism underlying the temporal specificity by which BDNF selectively promotes the survival of newly generated, but not mature, granule neurons of the mammalian cerebellum. We found that BDNF specifically induces the extracellular signal-regulated kinase 5 (ERK5)-myocyte enhancer factor (MEF2) signaling pathway in newly generated granule neurons and thereby induces transcription of neurotrophin-3 (NT-3), a novel gene target of MEF2. Inhibition of endogenous ERK5, MEF2, or NT-3 in neurons by several approaches including disruption of the NT-3 gene in mice revealed a requirement for the ERK5-MEF2-NT-3 signaling pathway in BDNF-induced survival of newly generated granule neurons. These findings define a novel mechanism that underlies the antiapoptotic effect of neurotrophins in a temporally defined pattern in the developing mammalian brain.
View details for Web of Science ID 000184817700011
View details for PubMedID 12917366
RNA interference reveals a requirement for myocyte enhancer factor 2A in activity-dependent neuronal survival
JOURNAL OF BIOLOGICAL CHEMISTRY
2002; 277 (48): 46442-46446
RNA interference (RNAi) provides a powerful method of gene silencing in eukaryotic cells, including proliferating mammalian cells. However, the utility of RNAi as a method of gene knock-down in primary postmitotic mammalian neurons remained unknown. Here, we asked if RNAi might be utilized to allow the assessment of the biological function of a specific gene in the nervous system. We employed a U6 promoter-driven DNA template approach to induce hairpin RNA-triggered RNAi to characterize the role of the transcription factor myocyte enhancer factor 2A (MEF2A) in the neuronal activity-dependent survival of granule neurons of the developing rat cerebellum. We found that the expression of MEF2A hairpin RNAs leads to the efficient and specific inhibition of endogenous MEF2A protein expression in primary cerebellar granule neurons. We also found that RNAi of MEF2A reduces significantly MEF2 response element-mediated transcription in granule neurons and inhibits activity-dependent granule neuron survival. Taken together, our RNAi experiments have revealed that MEF2A plays a critical role in activity-dependent neuronal survival. In addition, our findings indicate that RNAi does operate in postmitotic mammalian neurons and thus offers a rapid genetic method of studying gene function in the development and function of the mammalian nervous system.
View details for DOI 10.1074/jbc.M206653200
View details for Web of Science ID 000179529300096
View details for PubMedID 12235147