All Publications

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

    Abstract

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

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

    View details for PubMedID 38168992

    View details for PubMedCentralID 7003173

  • Neuroimaging is the new "spatial omic": multi-omic approaches to neuro-inflammation and immuno-thrombosis in acute ischemic stroke. Seminars in immunopathology Maier, B., Tsai, A. S., Einhaus, J. F., Desilles, J., Ho-Tin-Noe, B., Gory, B., Sirota, M., Leigh, R., Lemmens, R., Albers, G., Olivot, J., Mazighi, M., Gaudilliere, B. 2023

    Abstract

    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

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

    Abstract

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

    View details for DOI 10.1097/SLA.0000000000005348

    View details for PubMedID 34954754

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

    Abstract

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

    View details for DOI 10.1097/MCC.0000000000000883

    View details for PubMedID 34545029

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

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

    View details for Web of Science ID 000579336000001

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

    Abstract

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

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

    View details for PubMedID 32719355

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

    Abstract

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

    View details for DOI 10.1093/brain/awz022

    View details for PubMedID 30860258

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

    View details for DOI 10.4049/jimmunol.1700421

    View details for Web of Science ID 000409218000022

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

    View details for DOI 10.1126/sciimmunol.aan2946

    View details for Web of Science ID 000434327200004

  • Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data. iScience Einhaus, J., Gaudilliere, D. K., Hedou, J., Feyaerts, D., Ozawa, M. G., Sato, M., Ganio, E. A., Tsai, A. S., Stelzer, I. A., Bruckman, K. C., Amar, J. N., Sabayev, M., Bonham, T. A., Gillard, J., Diop, M., Cambriel, A., Mihalic, Z. N., Valdez, T., Liu, S. Y., Feirrera, L., Lam, D. K., Sunwoo, J. B., Schürch, C. M., Gaudilliere, B., Han, X. 2023; 26 (12): 108486

    Abstract

    Oral squamous cell carcinoma (OSCC), a prevalent and aggressive neoplasm, poses a significant challenge due to poor prognosis and limited prognostic biomarkers. Leveraging highly multiplexed imaging mass cytometry, we investigated the tumor immune microenvironment (TIME) in OSCC biopsies, characterizing immune cell distribution and signaling activity at the tumor-invasive front. Our spatial subsetting approach standardized cellular populations by tissue zone, improving feature reproducibility and revealing TIME patterns accompanying loss-of-differentiation. Employing a machine-learning pipeline combining reliable feature selection with multivariable modeling, we achieved accurate histological grade classification (AUC = 0.88). Three model features correlated with clinical outcomes in an independent cohort: granulocyte MAPKAPK2 signaling at the tumor front, stromal CD4+ memory T cell size, and the distance of fibroblasts from the tumor border. This study establishes a robust modeling framework for distilling complex imaging data, uncovering sentinel characteristics of the OSCC TIME to facilitate prognostic biomarkers discovery for recurrence risk stratification and immunomodulatory therapy development.

    View details for DOI 10.1016/j.isci.2023.108486

    View details for PubMedID 38125025

    View details for PubMedCentralID PMC10730356

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

    View details for Web of Science ID 001058985600289

  • Integrated Mass Cytometry Accurately Predicts Hemorrhagic Transformation Following Acute Ischaemic Stroke Tsai, A., Hedou, J., Einhaus, J., Feyaerts, D., Verdonk, F., Choisy, B., Desilles, J., Ho-Tin-Noe, B., Olivot, J., Mazighi, M., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2023: 261-262

    View details for Web of Science ID 001058985600098

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

    Abstract

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

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

    View details for PubMedID 36909508

    View details for PubMedCentralID PMC10002850

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

    Abstract

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

    View details for DOI 10.1016/j.xcrm.2022.100680

    View details for PubMedID 35839768

  • Risk factors for prolonged time to hospital discharge after ambulatory cholecystectomy under general anaesthesia. A retrospective cohort study. International journal of surgery (London, England) Picard, L., Duceau, B., Cambriel, A., Voron, T., Makoudi, S., Tsai, A., Yazid, L., Soulier, A., Paugam, C., Lescot, T., Bonnet, F., Verdonk, F. 2022: 106706

    Abstract

    BACKGROUND: Although predictive models have already integrated demographic factors and comorbidities as risk factors for a prolonged hospital stay, factors related to anaesthesia management in ambulatory surgery have not been yet characterized. This study aims to identify anaesthetic factors associated with a prolonged discharge time in ambulatory surgery.METHODS: All clinical records of patients who underwent ambulatory cholecystectomy in a French University Hospital (Hopital Saint Antoine, Paris) between January 1st, 2012 and December 31st, 2018 were retrospectively reviewed. The primary endpoint was the discharge time, defined as the time between the end of surgery and discharge. A multivariable Cox proportional-hazards model was fitted to investigate the factors associated with a prolonged discharge time.RESULTS: Five hundred and thirty-five (535) patients were included. The median time for discharge was 150 min (interquartile range - IQR [129-192]). A bivariable analysis highlighted a positive correlation between discharge timeline and the doses-weight of ketamine and sufentanil. In the multivariable Cox proportional hazards model analysis, the anaesthesia-related factors independently associated with prolonged discharge time were the dose-weight of ketamine in interaction with the dose weight of sufentanil (HR 0.10 per increment of 0.1 mg/kg of ketamine or 0.2 mug/kg of sufentanil, CI 95% [0.01-0.61], p = 0.013) and the non-use of a non-steroidal anti-inflammatory drug (NSAID) (HR 0.81 [0.67-0.98], p = 0.034). Twenty patients (4%) had unscheduled hospitalization following surgery.CONCLUSION: Anaesthesia management, namely the use of ketamine and the non-use of NSAID, affects time to hospital discharge.

    View details for DOI 10.1016/j.ijsu.2022.106706

    View details for PubMedID 35697325

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

    View details for Web of Science ID 000840283001162

  • Multimodal, coached telehealth prehabilitation has high compliance and improves exercise and cognitive capacity prior to surgery: a pilot study Choisy, B., Hedou, J., Verdonk, F., Gaudilliere, D., Tsai, A. S., Shankar, K., Sato, M., Einhaus, J., Ganio, E. A., Bonham, A., Warrington, B., Ahmad, S., Tingle, M., Ando, K., Bruckman, S., Angst, M. S., Kin, C., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2022: 415

    View details for Web of Science ID 000840283000157

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

    View details for Web of Science ID 000840283000229

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

    View details for Web of Science ID 000675441000486

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

    Abstract

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

    View details for DOI 10.1126/scitranslmed.abd9898

    View details for PubMedID 33952678

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

    Abstract

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

    View details for DOI 10.1101/2021.02.09.430269

    View details for PubMedID 33594362

    View details for PubMedCentralID PMC7885914

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

    Abstract

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

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

    View details for PubMedID 34099760

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

    Abstract

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

    View details for DOI 10.1097/SLA.0000000000005250

    View details for PubMedID 35129529

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

    Abstract

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

    View details for DOI 10.3389/fimmu.2021.725989

    View details for PubMedID 34566984

    View details for PubMedCentralID PMC8458888

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

    Abstract

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

    View details for DOI 10.3389/fimmu.2021.714090

    View details for PubMedID 34497610

    View details for PubMedCentralID PMC8420969

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

    Abstract

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

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

    View details for PubMedID 33294774

    View details for PubMedCentralID PMC7720904

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

    View details for Web of Science ID 000525432600082

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

    Abstract

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

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

    View details for PubMedID 32020337

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

    Abstract

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

    View details for DOI 10.1001/jamanetworkopen.2020.29655

    View details for PubMedID 33337494

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

    Abstract

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

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

    View details for PubMedID 32883978

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

    Abstract

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

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

    View details for PubMedID 32719375

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

    View details for DOI 10.3389/fimmu.2019.01305

    View details for Web of Science ID 000470999000001

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

    Abstract

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

    View details for DOI 10.3389/fimmu.2019.01305

    View details for PubMedID 31263463

    View details for PubMedCentralID PMC6584811

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

    View details for Web of Science ID 000480793600335

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

    View details for Web of Science ID 000480793600305

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

    View details for Web of Science ID 000619263200339

  • Utilizing Single Cell Immune Profiling to Identify Serum-based Biomarkers for Transient Ischemic Attacks Therkelsen, K., Tsai, A., Mlynash, M., Oh, B., Eyngorn, I., Gaudilliere, B., George, P. LIPPINCOTT WILLIAMS & WILKINS. 2019

    View details for Web of Science ID 000475965901285

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

    View details for Web of Science ID 000459610400616

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

    View details for Web of Science ID 000478733402484

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

    Abstract

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

    View details for PubMedID 30561547

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

    Abstract

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

    View details for DOI 10.1177/0022034519857714

    View details for PubMedID 31226001

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

    View details for DOI 10.1093/bioinformatics/bty537

    View details for Web of Science ID 000459313900012

  • Mass Cytometry and Proteomic Based Prediction of the Onset of Labor. Ando, K., Han, X., Ghaemi, S., Tsai, A., Ganio, E., Gaudilliere, D., Culos, T., Shaw, G., Wong, R., Stevenson, D., Carvalho, B., Tingle, M., Angst, M., Aghaeepor, N., Gaudilliere, B., Stanford March Dimes Prematurity SAGE PUBLICATIONS INC. 2018: 153A

    View details for Web of Science ID 000429928200292

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

    Abstract

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

    View details for PubMedID 28794234

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

    Abstract

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

    View details for PubMedID 28864494