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John S. Tsang, PhD, MMath

Professor of Immunobiology and Biomedical Engineering
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Director, Yale Center for Systems and Engineering Immunology (CSEI)

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Professor of Immunobiology and Biomedical Engineering

Director, Yale Center for Systems and Engineering Immunology (CSEI)

Biography

John Tsang is a systems immunologist, computational biologist, and engineer. He is currently Professor of Immunobiology and Biomedical Engineering at Yale University, Chan Zuckerberg Biohub Investigator and the Yale lead of CZ Biohub New York, and the Founding Director of the Yale Center for Systems and Engineering Immunology (CSEI). The CSEI serves as a home and cross-departmental center of research for systems, quantitative, and synthetic immunology at Yale University. Dr. Tsang earned his PhD in biophysics and systems biology from Harvard University (2008) as an NSERC Postgraduate Scholar, and has Master of Mathematics (MMath) and Bachelor of Applied Science (BASc) degrees in computer science and computer engineering from the University of Waterloo, Canada.

Dr. Tsang's group investigates the molecular and cellular underpinnings of human immune variations in health and disease: why immune system states and responses to perturbations (e.g., to vaccines, viral infections, and diseases) are highly variable across individuals in the human population. Their approach involves the development and application of machine learning, quantitative modeling, and experimental methods, including high-dimensional, longitudinal immune monitoring of human cohorts throughout the lifespan and around the globe, ex vivo experiments, and animal models.

As a scientific conceiver and the Yale lead of CZ Biohub NY, Dr. Tsang is interested in developing a predictive immune cell engineering toolkit to program immune cells as sensors of tissue statuses (e.g., early detection of pre-clinical disease and inflammation). Towards achieving this vision, he and his colleagues are working on quantitatively dissecting the mechanisms and design principles of tissue-blood communications and immune cell trafficking, including cell-cell interaction and signal integration by immune cells in tissues.

He has won multiple awards for his research, including NIH/NIAID Merit Awards recognizing his scientific leadership in systems immunology, COVID-19, and human immunology research. His work on mapping human immune variations and predicting vaccination responses was selected as a Top NIAID Research Advance of 2014. Dr. Tsang has served as an advisor on systems immunology and computational biology for numerous programs and organizations, including the Allen Institute, World Allergy Organization, National Cancer Institute, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, and the Fred Hutchinson Cancer Center. He currently serves on the Editorial Board of PLOS Biology and the Scientific Advisory Board of NIAID ImmPort, the NIAID Influenza IMPRINT Program, the NIH Common Fund Cellular Senescence Network (SenNet), Vaccine and Immunology Statistical Center of the Gates Foundation, the Human Immunome Project, ImmunoScape Inc., and CytoReason Ltd. He has lectured at many meetings and academic institutions and was lead organizer of major scientific conferences, including Keystone and Cold Spring Harbor Laboratory meetings on systems and engineering immunology.

Prior to joining Yale, Dr. Tsang was a tenured Senior Investigator in the National Institutes of Health's Intramural Research Program and led a laboratory focusing on systems and quantitative immunology at the National Institute of Allergy and Infectious Diseases (NIAID). He was the Co-Director of the Trans-NIH Center for Human Immunology (CHI) and led its research program in systems human immunology. He remains an Adjunct Investigator at NIAID.

Appointments

Education & Training

PhD
Harvard University, Biophysics/Systems Biology (2008)
MMath
University of Waterloo, Computer Science
BSc
University of Waterloo, Computer Engineering

Research

Overview

Below are highlights of some ongoing systems immunology projects in the lab, involving the development and application of multimodal immune profiling, single cell analysis, top-down, machine learning, as well as integration with bottom-up dynamical modeling and ex vivo/in vitro/animal models. Please contact John Tsang for additional details and other research directions - we are open to new ideas, questions, and approaches.

1. Systems immunology of maternal-infant dyads - To investigate the origin and development of individuality and personal immune states, we trace immune development and vaccine response starting from pregnancy using multimodal immune profiling, single cell analysis, and systems serology; we monitor and quantitative model vaccine responses in pregnant moms and their infants. Similarly, together with Eva Harris and colleagues, we study immune development in Nicaraguan children by longitudinally following them from infancy to adolescence, e.g., to decipher how immune status and set points are established in humans.

2. Vaccine and infection response heterogeneity in humans - We have a long-standing interest in understanding the molecular and cellular basis of immune variability in the human population. In addition to utilizing exposures such as infections and natural variations including genetic and exposure histories, we utilize vaccines as ethical, timed perturbations to assess the immune system of diverse populations (Sparks, Lau, Liu et al Nature 2023; Tsang Trends in Immunology 2015; Cheung et al eLife 2023; Liu, Martins, Lau, Rachmaninoff Cell 2021; Kotliarov, Sparks et al Nature Medicine 2020; Tsang et al Trends in Immunology 2020; Tsang et al Cell 2014).

By generating and combining data from diverse human cohorts, including cross-sectional, longitudinal, and household studies, we recently launched the Flu Diversity Project together with Sarah Cobey, Ben Cowling and colleagues. We seek to understand how vaccines and prior exposures shape personal immune status in both antigen-specific and -agnostic ways, and why influenza vaccine and infection responses are so heterogeneous across individuals and populations.

A related issue is vaccine hypo-responsiveness, which has been recognized as a major roadblock for vaccine efficacy for some populations. For example, experimental malaria vaccines like PfSPZ are generally known to have high efficacy in US and EU based trials, but their protection efficacy drops significantly (including in children) in endemic regions of Africa. We have ongoing projects in collaboration with Maria Yazdanbakhsh, Claudia Daubenberger, Steve Hoffman, Carlota Dobaño, Gemma Moncunill and colleagues, in which we use systems immunology and quantitative modeling approaches to study how baseline immune status and set points differ across geographic regions, and how those differences may explain malaria vaccine hypo-responsiveness. This research could illuminate novel strategies to modulate baseline immune system states to "restore" vaccine efficacy.

3. DARPA AIM - Assessing Immune Memory - We seek to understand why some vaccines (e.g., yellow fever) can induce ultra-long lasting protection and immune memory (even with only one dose of the vaccine) while others, like COVID-19 mRNA vaccines, seem to provide less durable protection. What are the early response predictors of durability and memory? How can we program the immune system for long-lasting durable memory responses? We are integrating animal models, human studies, and extensive multiomics single cell longitudinal analyses and computational modeling to address these issues.

4. Tissue inflammation and homeostasis - We are interested in understanding, in quantitative and network biology terms, how immune cells traffick to tissues and how homeostasis is maintained and deviations from homeostasis is detected. We are a part of the CZI Single Cell Inflammation Program and by using skin as a model, we aim to answer some of these questions in humans. We are also seeking to develop complementary animal and organoid models to quantify and model tissue dynamics.

5. Methodological research - We are broadly interested in developing, refining, and scaling up computational and experimental methods to enable systems immunology. We are also broadly interested in studying the "design principles" of the immune system and immune responses.

For example, to enable multimodal profiling and monitoring of human immune states in populations over time (e.g., before and after vaccination) and to develop predictive models and identify predictors and determinants of immune response outcomes, we have developed and scaled up approaches for sample-multiplexed, multimodal single cell analysis (e.g., see Liu, Martins, Lau, and Rachmaninoff et al Cell 2021 and Sparks, Lau, Liu et al Nature 2023). These include computational denoising and normalization methods (e.g., see Mulè, Martins, Tsang Nat. Comm. 2022), barcoding schemes combining genetics and hashtag multiplexing (e.g., enables pooled and sort approaches to enrich for rare cell populations), a machine learning toolkit/R package to identify predictors of responses (Candia and Tsang, BMC Bioinformatics 2020), shallow sequencing to develop machine learning predictors of cancer outcomes (Milanez-Almeida et al Nature Medicine 2020), and an approach to analyze single cell stimulation responses (Farmer et al Biorxiv 2022) . We have also been integrating dynamical/stochastic mechanistic modeling and machine learning to achieve fast prediction of emergent phenotypes and intuitive/interpretable understanding of the key determinants of the phenotypes (see Park et al. Biorxiv 2019, Martins and Narayanan et al Cell Systems 2017 and Wong et al. Cell 2021).

My team developed a web-based, crowdsourcing platform (OMiCC) for the management, reuse and meta-analysis of large-scale public data sets; OMiCC enables scientists without specialized training to utilize large-scale data from multiple studies to generate and test biological hypotheses (Sparks et al Nature Biotech 2016 and Liu et al STAR Protocols 2022). We illustrated, through a crowdsourcing experiment involving NIH volunteer scientists, how OMiCC can enable a group of non-computational biologists to utilize publicly available gene expression data to construct a multi-study “virtual” dataset of autoimmune diseases in both humans and animal models followed by meta-analysis to uncover disease signatures (Sparks et al Immunity 2016 and Lau et al F1000 Research 2016).

Medical Research Interests

Cell Engineering; Computational Biology; COVID-19; Epigenomics; Gene Regulatory Networks; Genomics; Homeostasis; Maternal-Fetal Relations; Precision Medicine; Single-Cell Analysis; Systems Biology; Vaccination

Research at a Glance

Research Interests

Research topics John S. Tsang is interested in exploring.

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