John S. Tsang, PhD, MMath
Research & Publications
Biography
News
Locations
Research Summary
Our lab combines computational and experimental approaches, including multimodal immune profiling, data science, machine learning, quantitative dynamical modeling, and ex vivo experiments and animal models, to investigate the basis of human immune response variability. We are also becoming increasingly interested in unraveling the quantitative mechanisms of immune cell trafficking and effector function in tissues, and the programming of immune cells as sensors and modulators of human health.
We are hiring - We are accepting students from several Yale graduate programs, including Computational Biology and Bioinformatics/BBS, Immunobiology/BBS, Physical and Engineering Biology, and Biomedical Engineering. Please reach out to discuss rotation projects and research in the lab.
We also have openings for postdocs, staff computational biologists, a laboratory technician, and a doctoral-level grant manager - some of these are based at the new Yale Center for Systems and Engineering Immunology (CSEI).
We have a diverse and supportive research environment comprising computational biologists, experimental scientists, hybrid scientists (both computational and experimental), and quantitative modelers with cross-disciplinary expertise and academic backgrounds hailed from different parts of the world. We welcome applicants to bring their own ideas, approaches, and questions to explore fundamental systems immunology and applications in human health and disease.
Keywords - Systems and quantitative immunology; human immunology; single cell biology; predicting and modeling immune responses; synthetic immunology: predictive engineering of immune cell trafficking and sensing; immune homeostasis maintenance; immune set point establishment mechanisms; integrating dynamical modeling and machine learning to understand emergent immune system behavior over space and time.
Extensive Research Description
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 always 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).
Coauthors
Research Interests
Homeostasis; Vaccination; Computational Biology; Genomics; Maternal-Fetal Relations; Systems Biology; Gene Regulatory Networks; Precision Medicine; Epigenomics; Single-Cell Analysis; Cell Engineering; COVID-19
Selected Publications
- Influenza vaccination reveals sex dimorphic imprints of prior mild COVID-19Sparks R, Lau W, Liu C, Han K, Vrindten K, Sun G, Cox M, Andrews S, Bansal N, Failla L, Manischewitz J, Grubbs G, King L, Koroleva G, Leimenstoll S, Snow L, Chen J, Tang J, Mukherjee A, Sellers B, Apps R, McDermott A, Martins A, Bloch E, Golding H, Khurana S, Tsang J. Influenza vaccination reveals sex dimorphic imprints of prior mild COVID-19. Nature 2023, 614: 752-761. PMID: 36599369, DOI: 10.1038/s41586-022-05670-5.
- Considering innate immune responses in SARS-CoV-2 infection and COVID-19Diamond M, Lambris J, Ting J, Tsang J. Considering innate immune responses in SARS-CoV-2 infection and COVID-19. Nature Reviews Immunology 2022, 22: 465-470. PMID: 35788185, PMCID: PMC9252555, DOI: 10.1038/s41577-022-00744-x.
- Normalizing and denoising protein expression data from droplet-based single cell profilingMulè M, Martins A, Tsang J. Normalizing and denoising protein expression data from droplet-based single cell profiling. Nature Communications 2022, 13: 2099. PMID: 35440536, PMCID: PMC9018908, DOI: 10.1038/s41467-022-29356-8.
- Immunopathological signatures in multisystem inflammatory syndrome in children and pediatric COVID-19Sacco K, Castagnoli R, Vakkilainen S, Liu C, Delmonte OM, Oguz C, Kaplan IM, Alehashemi S, Burbelo PD, Bhuyan F, de Jesus AA, Dobbs K, Rosen LB, Cheng A, Shaw E, Vakkilainen MS, Pala F, Lack J, Zhang Y, Fink DL, Oikonomou V, Snow AL, Dalgard CL, Chen J, Sellers BA, Montealegre Sanchez GA, Barron K, Rey-Jurado E, Vial C, Poli MC, Licari A, Montagna D, Marseglia GL, Licciardi F, Ramenghi U, Discepolo V, Lo Vecchio A, Guarino A, Eisenstein EM, Imberti L, Sottini A, Biondi A, Mató S, Gerstbacher D, Truong M, Stack MA, Magliocco M, Bosticardo M, Kawai T, Danielson JJ, Hulett T, Askenazi M, Hu S, Cohen J, Su H, Kuhns D, Lionakis M, Snyder T, Holland S, Goldbach-Mansky R, Tsang J, Notarangelo L. Immunopathological signatures in multisystem inflammatory syndrome in children and pediatric COVID-19. Nature Medicine 2022, 28: 1050-1062. PMID: 35177862, PMCID: PMC9119950, DOI: 10.1038/s41591-022-01724-3.
- A local regulatory T cell feedback circuit maintains immune homeostasis by pruning self-activated T cellsWong H, Park K, Gola A, Baptista A, Miller C, Deep D, Lou M, Boyd L, Rudensky A, Savage P, Altan-Bonnet G, Tsang J, Germain R. A local regulatory T cell feedback circuit maintains immune homeostasis by pruning self-activated T cells. Cell 2021, 184: 3981-3997.e22. PMID: 34157301, PMCID: PMC8390950, DOI: 10.1016/j.cell.2021.05.028.
- Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19Liu C, Martins AJ, Lau WW, Rachmaninoff N, Chen J, Imberti L, Mostaghimi D, Fink DL, Burbelo PD, Dobbs K, Delmonte OM, Bansal N, Failla L, Sottini A, Quiros-Roldan E, Han KL, Sellers BA, Cheung F, Sparks R, Chun TW, Moir S, Lionakis MS; NIAID COVID Consortium; COVID Clinicians; Rossi C, Su HC, Kuhns DB, Cohen JI, Notarangelo LD, Tsang JS. Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19. Cell. 2021 Apr 1;184(7):1836-1857.e22. doi: 10.1016/j.cell.2021.02.018. Epub 2021 Feb 10. PMID: 33713619; PMCID: PMC7874909.
- Improving Vaccine-Induced Immunity: Can Baseline Predict Outcome?Tsang J, Dobaño C, VanDamme P, Moncunill G, Marchant A, Othman R, Sadarangani M, Koff W, Kollmann T. Improving Vaccine-Induced Immunity: Can Baseline Predict Outcome? Trends In Immunology 2020, 41: 457-465. PMID: 32340868, PMCID: PMC7142696, DOI: 10.1016/j.it.2020.04.001.
- Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupusKotliarov Y, Sparks R, Martins A, Mulè M, Lu Y, Goswami M, Kardava L, Banchereau R, Pascual V, Biancotto A, Chen J, Schwartzberg P, Bansal N, Liu C, Cheung F, Moir S, Tsang J. Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus. Nature Medicine 2020, 26: 618-629. PMID: 32094927, PMCID: PMC8392163, DOI: 10.1038/s41591-020-0769-8.
- Cancer prognosis with shallow tumor RNA sequencingMilanez-Almeida P, Martins A, Germain R, Tsang J. Cancer prognosis with shallow tumor RNA sequencing. Nature Medicine 2020, 26: 188-192. PMID: 32042193, DOI: 10.1038/s41591-019-0729-3.
- Machine learning of stochastic gene network phenotypesMachine learning of stochastic gene network phenotypes Kyemyung Park, Thorsten Prüstel, Yong Lu, John S. Tsang bioRxiv 825943; doi: https://doi.org/10.1101/825943
- Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene NetworkMartins A, Narayanan M, Prüstel T, Fixsen B, Park K, Gottschalk R, Lu Y, Andrews-Pfannkoch C, Lau W, Wendelsdorf K, Tsang J. Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network. Cell Systems 2017, 4: 379-392.e12. PMID: 28365150, PMCID: PMC8392141, DOI: 10.1016/j.cels.2017.03.002.
- Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic AssociationsLu Y, Biancotto A, Cheung F, Remmers E, Shah N, McCoy J, Tsang J. Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations. Immunity 2016, 45: 1162-1175. PMID: 27851916, PMCID: PMC6532399, DOI: 10.1016/j.immuni.2016.10.025.
- A crowdsourcing approach for reusing and meta-analyzing gene expression dataShah N, Guo Y, Wendelsdorf K, Lu Y, Sparks R, Tsang J. A crowdsourcing approach for reusing and meta-analyzing gene expression data. Nature Biotechnology 2016, 34: 803-806. PMID: 27323300, PMCID: PMC6871002, DOI: 10.1038/nbt.3603.
- Utilizing population variation, vaccination, and systems biology to study human immunologyTsang J. Utilizing population variation, vaccination, and systems biology to study human immunology. Trends In Immunology 2015, 36: 479-493. PMID: 26187853, PMCID: PMC4979540, DOI: 10.1016/j.it.2015.06.005.
- Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination ResponsesTsang J, Schwartzberg P, Kotliarov Y, Biancotto A, Xie Z, Germain R, Wang E, Olnes M, Narayanan M, Golding H, Moir S, Dickler H, Perl S, Cheung F, Center T, Consortium T, Obermoser G, Chaussabel D, Palucka K, Chen J, Fuchs J, Ho J, Khurana S, King L, Langweiler M, Liu H, Manischewitz J, Pos Z, Posada J, Schum P, Shi R, Valdez J, Wang W, Zhou H, Kastner D, Marincola F, McCoy J, Trinchieri G, Young N. Global Analyses of Human Immune Variation Reveal Baseline Predictors of Postvaccination Responses. Cell 2014, 157: 499-513. PMID: 24725414, PMCID: PMC4139290, DOI: 10.1016/j.cell.2014.03.031.
- Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpointMulè M, Martins A, Cheung F, Farmer R, Sellers B, Quiel J, Jain A, Kotliarov Y, Bansal N, Chen J, Schwartzberg P, Tsang J. Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint. Immunity 2024, 57: 1160-1176.e7. PMID: 38697118, DOI: 10.1016/j.immuni.2024.04.009.
- Sex and prior exposure jointly shape innate immune responses to a live herpesvirus vaccineCheung F, Apps R, Dropulic L, Kotliarov Y, Chen J, Jordan T, Langweiler M, Candia J, Biancotto A, Han K, Rachmaninoff N, Pietz H, Wang K, Tsang J, Cohen J. Sex and prior exposure jointly shape innate immune responses to a live herpesvirus vaccine. ELife 2023, 12: e80652. PMID: 36648132, PMCID: PMC9844983, DOI: 10.7554/elife.80652.
- Adaptive immune responses to SARS-CoV-2 persist in the pharyngeal lymphoid tissue of childrenXu Q, Milanez-Almeida P, Martins A, Radtke A, Hoehn K, Oguz C, Chen J, Liu C, Tang J, Grubbs G, Stein S, Ramelli S, Kabat J, Behzadpour H, Karkanitsa M, Spathies J, Kalish H, Kardava L, Kirby M, Cheung F, Preite S, Duncker P, Kitakule M, Romero N, Preciado D, Gitman L, Koroleva G, Smith G, Shaffer A, McBain I, McGuire P, Pittaluga S, Germain R, Apps R, Schwartz D, Sadtler K, Moir S, Chertow D, Kleinstein S, Khurana S, Tsang J, Mudd P, Schwartzberg P, Manthiram K. Adaptive immune responses to SARS-CoV-2 persist in the pharyngeal lymphoid tissue of children. Nature Immunology 2022, 24: 186-199. PMID: 36536106, PMCID: PMC10777159, DOI: 10.1038/s41590-022-01367-z.
- Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2-associated multisystem inflammatory syndrome in childrenRamaswamy A, Brodsky NN, Sumida TS, Comi M, Asashima H, Hoehn KB, Li N, Liu Y, Shah A, Ravindra NG, Bishai J, Khan A, Lau W, Sellers B, Bansal N, Guerrerio P, Unterman A, Habet V, Rice AJ, Catanzaro J, Chandnani H, Lopez M, Kaminski N, Dela Cruz CS, Tsang JS, Wang Z, Yan X, Kleinstein SH, van Dijk D, Pierce RW, Hafler DA, Lucas CL. Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2-associated multisystem inflammatory syndrome in children. Immunity 2021, 54: 1083-1095.e7. PMID: 33891889, PMCID: PMC8043654, DOI: 10.1016/j.immuni.2021.04.003.
- Intravenous nanoparticle vaccination generates stem-like TCF1+ neoantigen-specific CD8+ T cellsBaharom F, Ramirez-Valdez RA, Tobin KKS, Yamane H, Dutertre CA, Khalilnezhad A, Reynoso GV, Coble VL, Lynn GM, Mulè MP, Martins AJ, Finnigan JP, Zhang XM, Hamerman JA, Bhardwaj N, Tsang JS, Hickman HD, Ginhoux F, Ishizuka AS, Seder RA. Intravenous nanoparticle vaccination generates stem-like TCF1+ neoantigen-specific CD8+ T cells. Nature Immunology 2020, 22: 41-52. PMID: 33139915, PMCID: PMC7746638, DOI: 10.1038/s41590-020-00810-3.
- Overexpression of T-bet in HIV infection is associated with accumulation of B cells outside germinal centers and poor affinity maturationAustin JW, Buckner CM, Kardava L, Wang W, Zhang X, Melson VA, Swanson RG, Martins AJ, Zhou JQ, Hoehn KB, Fisk JN, Dimopoulos Y, Chassiakos A, O'Dell S, Smelkinson MG, Seamon CA, Kwan RW, Sneller MC, Pittaluga S, Doria-Rose NA, McDermott A, Li Y, Chun TW, Kleinstein SH, Tsang JS, Petrovas C, Moir S. Overexpression of T-bet in HIV infection is associated with accumulation of B cells outside germinal centers and poor affinity maturation. Science Translational Medicine 2019, 11 PMID: 31776286, PMCID: PMC7479651, DOI: 10.1126/scitranslmed.aax0904.
- Resident Macrophages Cloak Tissue Microlesions to Prevent Neutrophil-Driven Inflammatory DamageUderhardt S, Martins A, Tsang J, Lämmermann T, Germain R. Resident Macrophages Cloak Tissue Microlesions to Prevent Neutrophil-Driven Inflammatory Damage. Cell 2019, 177: 541-555.e17. PMID: 30955887, PMCID: PMC6474841, DOI: 10.1016/j.cell.2019.02.028.
- Solving Immunology?Vodovotz Y, Xia A, Read EL, Bassaganya-Riera J, Hafler DA, Sontag E, Wang J, Tsang JS, Day JD, Kleinstein SH, Butte AJ, Altman MC, Hammond R, Sealfon SC. Solving Immunology? Trends In Immunology 2016, 38: 116-127. PMID: 27986392, PMCID: PMC5695553, DOI: 10.1016/j.it.2016.11.006.
- Abnormal B cell memory subsets dominate HIV-specific responses in infected individualsKardava L, Moir S, Shah N, Wang W, Wilson R, Buckner CM, Santich BH, Kim LJ, Spurlin EE, Nelson AK, Wheatley AK, Harvey CJ, McDermott AB, Wucherpfennig KW, Chun TW, Tsang JS, Li Y, Fauci AS. Abnormal B cell memory subsets dominate HIV-specific responses in infected individuals. Journal Of Clinical Investigation 2014, 124: 3252-3262. PMID: 24892810, PMCID: PMC4071400, DOI: 10.1172/jci74351.