Dorien Feyaerts

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

• 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

• Uterine Natural Killer Cells Modulate Endometrial Growth and Persistence in Endometriosis Diop, M., Feyaerts, D., Irwin, J., Einhaus, J., Stelzer, I., Bonham, A., Casillas, A., Vo, K., Blish, C., Giudice, L., Gaudilliere, B. ELSEVIER IRELAND LTD. 2023: 25

  View details for DOI 10.1016/j.jri.2023.104054

  View details for Web of Science ID 001069722400062

• Longitudinal clinical phenotyping of post COVID condition in Mexican adults recovering from severe COVID-19: a prospective cohort study. Frontiers in medicine Núñez, I., Gillard, J., Fragoso-Saavedra, S., Feyaerts, D., Islas-Weinstein, L., Gallegos-Guzmán, A. A., Valente-García, U., Meyerowitz, J., Kelly, J. D., Chen, H., Ganio, E., Benkendorff, A., Flores-Gouyonnet, J., Dammann-Beltrán, P., Heredia-González, J. F., Rangel-Gutiérrez, G. A., Blish, C. A., Nadeau, K. C., Nolan, G., Crispín, J. C., McIlwain, D. R., Gaudillière, B., Valdés-Ferrer, S. I. 2023; 10: 1236702

  Abstract

  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

• Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology. Nature communications Bai, Y., Zhu, B., Oliveria, J., Cannon, B. J., Feyaerts, D., Bosse, M., Vijayaragavan, K., Greenwald, N. F., Phillips, D., Schurch, C. M., Naik, S. M., Ganio, E. A., Gaudilliere, B., Rodig, S. J., Miller, M. B., Angelo, M., Bendall, S. C., Rovira-Clave, X., Nolan, G. P., Jiang, S. 2023; 14 (1): 4013

  Abstract

  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

• 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

• An immune signature of postoperative cognitive dysfunction (POCD), a prospective cohort study Verdonk, F., Hedou, J., Bellan, G., Ganio, E., Stelzer, I., Feyaerts, D., Sato, M., Bonham, A., Ando, K., Gaudilliere, D., Gaillard, R., Molliex, S., Sharshar, T., Gaudilliere, B. LIPPINCOTT WILLIAMS & WILKINS. 2023: 466-467

  View details for Web of Science ID 001058985600166

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

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

  View details for Web of Science ID 000968297800002

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

  Abstract

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

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

  View details for PubMedID 38116462

  View details for PubMedCentralID PMC10727505

• 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

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

  Abstract

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

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

  View details for PubMedID 36790488

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

  Abstract

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

  View details for DOI 10.1016/j.patter.2022.100655

  View details for PubMedID 36569558

• Upcoming and urgent challenges in critical care research based on COVID-19 pandemic experience. Anaesthesia, critical care & pain medicine Verdonk, F., Feyaerts, D., Badenes, R., Bastarache, J. A., Bougle, A., Ely, W., Gaudilliere, B., Howard, C., Kotfis, K., Lautrette, A., Le Dorze, M., Mankidy, B. J., Matthay, M. A., Morgan, C. K., Mazeraud, A., Patel, B. V., Pattnaik, R., Reuter, J., Schultz, M. J., Sharshar, T., Shrestha, G. S., Verdonk, C., Ware, L. B., Pirracchio, R., Jabaudon, M. 2022: 101121

  Abstract

  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 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

• Establishment of tissue-resident immune populations in the fetus. Seminars in immunopathology Feyaerts, D., Urbschat, C., Stelzer, I. A. 2022

  Abstract

  The immune system establishes during the prenatal period from distinct waves of stem and progenitor cells and continuously adapts to the needs and challenges of early postnatal and adult life. Fetal immune development not only lays the foundation for postnatal immunity but establishes functional populations of tissue-resident immune cells that are instrumental for fetal immune responses amidst organ growth and maturation. This review aims to discuss current knowledge about the development and function of tissue-resident immune populations during fetal life, focusing on the brain, lung, and gastrointestinal tract as sites with distinct developmental trajectories. While recent progress using system-level approaches has shed light on the fetal immune landscape, further work is required to describe precise roles of prenatal immune populations and their migration and adaptation to respective organ environments. Defining points of prenatal susceptibility to environmental challenges will support the search for potential therapeutic targets to positively impact postnatal health.

  View details for DOI 10.1007/s00281-022-00931-x

  View details for PubMedID 35508672

• 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

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

  Abstract

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

  View details for DOI 10.3389/fped.2022.933266

  View details for PubMedID 36582513

• 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

• 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

• 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

• A pregnancy to remember: trained immunity of the uterine mucosae. Mucosal immunology Feyaerts, D., Joosten, I., van der Molen, R. G. 2020

  View details for DOI 10.1038/s41385-020-00362-7

  View details for PubMedID 33299087

• Clusters of Tolerogenic B Cells Feature in the Dynamic Immunological Landscape of the Pregnant Uterus CELL REPORTS Benner, M., Feyaerts, D., Garcia, C., Inci, N., Lopez, S., Fasse, E., Shadmanfar, W., van der Heijden, O. H., Gorris, M. J., Joosten, I., Ferwerda, G., van der Molen, R. G. 2020; 32 (13): 108204

  Abstract

  Well-timed interaction of correctly functioning maternal immune cells is essential to facilitate healthy placenta formation, because the uterine immune environment has to tolerate the semi-allogeneic fetus and allow adequate trophoblast invasion. Here, we assess the uterine immune signature before and during pregnancy. Extensive supervised and unsupervised flow cytometry clustering strategies not only show a general increase in immune memory throughout pregnancy but also reveal the continuous presence of B cells. Contrary to the belief that B cells are merely a consequence of uterine pathology, decidual B cells produce IL-10 and are found to be localized in clusters, together with Foxp3pos T cells. Our findings therefore suggest a role for B cells in healthy pregnancy.

  View details for DOI 10.1016/j.celrep.2020.108204

  View details for Web of Science ID 000573722100014

  View details for PubMedID 32997982

• 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

• Selective expansion and CMV-dependency in pregnancy trained human endometrial NK cells. Cellular & molecular immunology Feyaerts, D., van der Meer, A., Joosten, I., van der Molen, R. G. 2019; 16 (4): 410-411

  View details for DOI 10.1038/s41423-018-0193-x

  View details for PubMedID 30635647

  View details for PubMedCentralID PMC6462002

• Respiratory Syncytial Virus Infects Primary Neonatal and Adult Natural Killer Cells and Affects Their Antiviral Effector Function. The Journal of infectious diseases van Erp, E. A., Feyaerts, D., Duijst, M., Mulder, H. L., Wicht, O., Luytjes, W., Ferwerda, G., van Kasteren, P. B. 2019; 219 (5): 723-733

  Abstract

  Respiratory syncytial virus (RSV) is a major cause of severe acute lower respiratory tract infections in infants. Natural killer (NK) cells are important antiviral effector cells that likely encounter RSV in the presence of virus-specific (maternal) antibodies. As NK cells potentially contribute to immunopathology, we investigated whether RSV affects their antiviral effector functions.We assessed the phenotype and functionality of primary neonatal and adult NK cells by flow cytometry after stimulation with RSV or RSV-antibody complexes.We demonstrate for the first time that RSV infects neonatal and adult NK cells in vitro. Preincubation of virus with subneutralizing concentrations of RSV-specific antibodies significantly increased the percentage of infected NK cells. Upon infection, NK cells were significantly more prone to produce interferon-γ, while secretion of the cytotoxicity molecule perforin was not enhanced.Our findings suggest that (antibody-enhanced) RSV infection of NK cells induces a proinflammatory rather than a cytotoxic response, which may contribute to immunopathology. Considering that most RSV vaccines currently being developed aim at inducing (maternal) antibodies, these results highlight the importance of understanding the interactions between innate effector cells and virus-specific antibodies.

  View details for DOI 10.1093/infdis/jiy566

  View details for PubMedID 30252097

  View details for PubMedCentralID PMC6376914

• Placental disposition of the immunosuppressive drug tacrolimus in renal transplant recipients and in ex vivo perfused placental tissue. European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences Freriksen, J. J., Feyaerts, D., van den Broek, P. H., van der Heijden, O. W., van Drongelen, J., van Hamersvelt, H. W., Russel, F. G., van der Molen, R. G., Greupink, R. 2018; 119: 244-248

  Abstract

  Currently, tacrolimus is the most potent immunosuppressive agent for renal transplant recipients and is commonly prescribed during pregnancy. As data on placental exposure and transfer are limited, we studied tacrolimus placental handling in samples obtained from renal transplant recipients. We found transfer to venous umbilical cord blood, but particularly noted a strong placental accumulation. In patient samples, tissue concentrations in a range of 55-82 ng/g were found. More detailed ex vivo dual-side perfusions of term placentas from healthy women revealed a tissue-to-maternal perfusate concentration ratio of 113 ± 49 (mean ± SEM), underlining the placental accumulation found in vivo. During the 3 h ex vivo perfusion interval no placental transfer to the fetal circulation was observed. In addition, we found a non-homogeneous distribution of tacrolimus across the perfused cotyledons. In conclusion, we observed extensive accumulation of tacrolimus in placental tissue. This warrants further studies into potential effects on placental function and immune cells of the placenta.

  View details for DOI 10.1016/j.ejps.2018.04.017

  View details for PubMedID 29655601

• Endometrial natural killer (NK) cells reveal a tissue-specific receptor repertoire. Human reproduction (Oxford, England) Feyaerts, D., Kuret, T., van Cranenbroek, B., van der Zeeuw-Hingrez, S., van der Heijden, O. W., van der Meer, A., Joosten, I., van der Molen, R. G. 2018; 33 (3): 441-451

  Abstract

  Is the natural killer (NK) cell receptor repertoire of endometrial NK (eNK) cells tissue-specific?The NK cell receptor (NKR) expression profile in pre-pregnancy endometrium appears to have a unique tissue-specific phenotype, different from that found in NK cells in peripheral blood, suggesting that these cells are finely tuned towards the reception of an allogeneic fetus.NK cells are important for successful pregnancy. After implantation, NK cells encounter extravillous trophoblast cells and regulate trophoblast invasion. NK cell activity is amongst others regulated by C-type lectin heterodimer (CD94/NKG2) and killer cell immunoglobulin-like (KIR) receptors. KIR expression on decidual NK cells is affected by the presence of maternal HLA-C and biased towards KIR2D expression. However, little is known about NKR expression on eNK cells prior to pregnancy.In this study, matched peripheral and menstrual blood (a source of endometrial cells) was obtained from 25 healthy females with regular menstrual cycles. Menstrual blood was collected during the first 36 h of menstruation using a menstrual cup, a non-invasive technique to obtain endometrial cells.KIR and NKG2 receptor expression on eNK cells was characterized by 10-color flow cytometry, and compared to matched pbNK cells of the same female. KIR and HLA-C genotypes were determined by PCR-SSOP techniques. Anti-CMV IgG antibodies in plasma were measured by chemiluminescence immunoassay.KIR expression patterns of eNK cells collected from the same female do not differ over consecutive menstrual cycles. The percentage of NK cells expressing KIR2DL2/L3/S2, KIR2DL3, KIR2DL1, LILRB1 and/or NKG2A was significantly higher in eNK cells compared to pbNK cells, while no significant difference was observed for NKG2C, KIR2DL1/S1, and KIR3DL1. The NKR repertoire of eNK cells was clearly different from pbNK cells, with eNK cells co-expressing more than three NKR simultaneously. In addition, outlier analysis revealed 8 and 15 NKR subpopulation expansions in eNK and pbNK cells, respectively. In contrast to the pbNK cell population, the expansions present in the eNK cell population were independent of CMV status and HLA-C genotype. Moreover, the typical NKG2C imprint induced by CMV infection on pbNK cells was not observed on eNK cells from the same female, suggesting a rapid local turnover of eNK cells and/or a distinct licensing process.Based on our previous work and the parameters studied here, menstrual blood-derived eNK cells closely resemble biopsy-derived eNK cells. However, sampling is not done at the exact same time during the menstrual cycle, and therefore we cannot exclude some, as yet undetected, differences.Our data reveals that NK cells in the pre-implantation endometrium appear to have a dedicated tissue-specific phenotype, different from NK cells in peripheral blood. This may indicate that eNK cells are finely tuned to receive an allogeneic fetus. Studying the endometrial NKR repertoire of women with pregnancy related problems could provide clues to understand the pathogenesis of pregnancy complications.No external funding was obtained for the present study. None of the authors has any conflict of interest to declare.NA.

  View details for DOI 10.1093/humrep/dey001

  View details for PubMedID 29447367

• Human uterine lymphocytes acquire a more experienced and tolerogenic phenotype during pregnancy. Scientific reports Feyaerts, D., Benner, M., van Cranenbroek, B., van der Heijden, O. W., Joosten, I., van der Molen, R. G. 2017; 7 (1): 2884

  Abstract

  Pregnancy requires a delicate immune balance that nurtures the allogeneic fetus, while maintaining reactivity against pathogens. Despite increasing knowledge, data is lacking on the transition of pre-pregnancy endometrial lymphocytes to a pregnancy state. Here, we immunophenotyped lymphocytes from endometrium (MMC), term decidua parietalis (DPMC), and PBMC for direct comparison. We found that the immune cell composition of MMC and DPMC clearly differ from each other, with less NK-cells, and more NKT-cells and T-cells in DPMC. An increased percentage of central memory and effector memory T-cells, and less naive T-cells in DPMC indicates that decidual T-cells are more experienced than endometrial T-cells. The increased percentage of CD4+CD25highCD127- Treg in DPMC, including differentiated Treg, is indicative of a more experienced and tolerogenic environment during pregnancy. The Th cell composition of both MMC and DPMC was different from PBMC, with a preference for Th1 over Th2 in the uterine environment. Between MMC and DPMC, percentages of Th cell subsets did not differ significantly. Our results suggest that already before pregnancy a tightly controlled Th1/Th2/Th17 balance is present. These findings create opportunities to further investigate the underlying immune mechanism of pregnancy complications using menstrual blood as a source for endometrial lymphocytes.

  View details for DOI 10.1038/s41598-017-03191-0

  View details for PubMedID 28588205

  View details for PubMedCentralID PMC5460245

• 1,25-Dihydroxyvitamin D3 and its analog TX527 promote a stable regulatory T cell phenotype in T cells from type 1 diabetes patients. PloS one Van Belle, T. L., Vanherwegen, A. S., Feyaerts, D., De Clercq, P., Verstuyf, A., Korf, H., Gysemans, C., Mathieu, C. 2014; 9 (10): e109194

  Abstract

  The emergence of regulatory T cells (Tregs) as central mediators of peripheral tolerance in the immune system has led to an important area of clinical investigation to target these cells for the treatment of autoimmune diseases such as type 1 diabetes. We have demonstrated earlier that in vitro treatment of T cells from healthy individuals with TX527, a low-calcemic analog of bioactive vitamin D, can promote a CD4+ CD25high CD127low regulatory profile and imprint a migratory signature specific for homing to sites of inflammation. Towards clinical application of vitamin D-induced Tregs in autologous adoptive immunotherapy for type 1 diabetes, we show here that 1,25-dihydroxyvitamin D3 [1,25(OH)2D3] and TX527 similarly imprint T cells from type 1 diabetes patients with a CD4+ CD25high CD127low regulatory profile, modulate surface expression of skin- and inflammation-homing receptors, and increase expression of CTLA-4 and OX-40. Also, 1,25(OH)2D3 and TX527 treatment inhibit the production of effector cytokines IFN-γ, IL-9, and IL-17. Importantly, 1,25(OH)2D3 and TX527 promote the induction of IL-10-producing CD4+ CD25high CD127low T cells with a stable phenotype and the functional capacity to suppress proliferation of autologous responder T cells in vitro. These findings warrant additional validation of vitamin D-induced Tregs in view of future autologous adoptive immunotherapy in type 1 diabetes.

  View details for DOI 10.1371/journal.pone.0109194

  View details for PubMedID 25279717

  View details for PubMedCentralID PMC4184870