Major Areas of Research
- Systems immunology
- Integrative genomics: computational approaches to integrate diverse data types to obtain novel biological insights; biological circuit reconstruction
- Single-cell genomics; the function and consequence of phenotypic heterogeneity and stochastic gene expression in immune cells
We develop and apply systems biology approaches—combining computation, modeling, and experiments—to study the immune system at both the organismal and cellular levels.
Systems Human Immunology
At the organismal level, we have been utilizing natural perturbations (disease and genetic variation) and ethical interventions (particularly vaccination) to systemically perturb the immune system and comprehensively assess its statuses pre- and post-perturbation using multiplexed technologies in human cohorts. The resulting multi-modal data sets are analyzed and modeled in an integrative manner to 1) uncover biomarkers of immune responsiveness and health, 2) infer connectivities among components of the immune system, and ultimately, 3) understand how immune responses are orchestrated across scales—from molecules to cells to cell-to-cell interactions in space and time.
Immune Cells and Their Adaptations to the Environment: From Cell Populations to Single Cells
An immune response is a collective outcome of the coordination among a large number of cells operating over space and time. Throughout this process, individual cells (and cell populations) monitor and adapt to environmental signals. We are broadly interested in understanding how immune cells adapt to their environment and make decisions. Macrophages are particularly good models in this context. They are highly plastic and respond with diverse functional phenotypes to environmental challenges. In addition to host defense, macrophages play important roles in tissue homeostasis and have been implicated in a number of conditions in health and disease (e.g., atherosclerosis, obesity, Type II diabetes). We are studying macrophage adaptation at both the cell-population and single-cell levels. We are utilizing dense measurements from flow cytometry, microfluidic qPCR (Fluidigm), RNAseq, ChIP-Seq, and CAGE-Seq to comprehensively characterize cellular states pre- and post-environmental stimulation, followed by integrative modeling and analysis. We are particularly interested in assessing cell-to-cell heterogeneity in transcriptional responses and understanding its functional consequences at both the network and cellular levels.
Biological Circuit Inference
We are broadly interested in the general problem of inferring biological circuits at several levels, including interactions among cell types and among biomolecules within and across cells. In addition to circuit topology, we are interested in understanding the mapping between topology and function, as well as the associated operational and design principles. Our general approach to tackle this problem has been to utilize arrays of experimental perturbations or natural variations in cells and organisms to “generate” diverse cellular and/or organismal phenotypes followed by comprehensive, multi-modal characterization of the resulting states. We then use this dense sampling and measurement of the state-space to computational model and infer functional relationships among systems components. To close the loop, both experimental and computational follow-ups are used to further test and validate the resulting models and hypotheses.
Our main experimental tools include next-generation sequencing, flow cytometry, and quantitative PCR. We are also developing and adopting approaches for highly multiplexed single-cell profiling. On the computational and modeling end, we employ or develop methods motivated by (or borrow verbatim from) multivariate statistics, Bayesian network inference, linear models, and graph algorithms, as well as dynamical and stochastic modeling. We also aim to develop or turn internal toolkits into broadly distributed tools when it is apparent that they are useful in more general settings, particularly in settings where experimental biologists with little or no computational training can be empowered to generate and test hypotheses using large, complex data sets.
Omics Compendia Commons (OmiCC): a crowdsourcing platform for gene expression data reuse and (meta-) analysis
Fellowship and Training Opportunities
Postdoctoral/predoctoral fellowships and staff positions are available to work on systems biology and immunology. Motivated individuals with interdisciplinary mindset/outlook and backgrounds in computational biology, immunology, medicine, genomics, experimental biology (e.g., molecular genetics), and bioinformatics are welcome to inquire.
Dr. Tsang leads a laboratory focusing on systems and quantitative immunology at the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH). He also co-directs the Trans-NIH Center for Human Immunology (CHI) and leads its research program in systems human immunology. Dr. Tsang trained in computer engineering and computer science at the University of Waterloo and received his Ph.D. in biophysics from Harvard University.
Before joining the NIH, He worked as a software engineer in Silicon Valley, pursued genomics and proteomics in Rosetta Inpharmatics and Caprion Proteomics, and conducted systems biology and bioinformatics research on microRNAs and integrative genomics at the Massachusetts Institute of Technology (MIT) and Merck Research Laboratories.
Dr. Tsang has won several awards for his research, including NIAID Merit Awards for the development of a data reuse and crowdsourcing platform OMiCC and for leading a system biology study of human immune variability and influenza vaccination, which was selected as one of the top 20 NIAID Research Advances of 2014. He has served as a scientific advisor on systems immunology and bioinformatics for a number of programs and organizations including ImmPort (the clinical and molecular data repository for NIAID), the Committee on Precision Medicine for the World Allergy Organization, the NIAID Modeling Immunity for Biodefense Program, the Allen Institute, and the Immuno-Epidemiology Program at the National Cancer Institute.
Staff and Fellows
Manikandan Narayanan (Ph.D., Computational Genomics, UC-Berkeley, United States)
Yong Lu (Ph.D., Computer Science, Carnegie Mellon University, United States)
Andrew Martins (Ph.D., Immunology, University of Western Ontario, Canada)
Rachel Sparks (M.D., M.P.H., University of Washington, United States)
Neha Bansal (B.Sc., Biological Sciences, George Mason University, United States)
Candace Liu (B.Sc., Chemical and Biomolecular Engineering, Johns Hopkins University, United States)
William Lau (M.Sc., Biomedical Engineering, Johns Hopkins University, United States)—visiting Ph.D. student from George Mason University and Center for Information Technology of NIH
Kyemyung Park—Ph.D. student, Biophysics Program, University of Maryland, College Park, United States
Bethan Fixsen (former postbac)—currently MSTP M.D./Ph.D. student, University of California, San Diego
Naisha Shah (former postdoc)—currently research scientist at Human Longevity, Inc.
Katherine Wendelsdorf (former postdoc)—currently scientist, Ingenuity Systems
Michael Smith (former rotation student)—currently bioinformatics analyst, MedImmune
Martins AJ, Narayanan M, Prüste T, Fixsen B, Park K, Gottschalk RA, Lu Y, Andrews-Pfannkoch C, Lau WW, Wendelsdorf KV, Tsang JS. Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network. Cell Syst. 2017 Apr 26;4(4):379-392. Commentary from Trends in Immunology: http://www.sciencedirect.com/science/article/pii/S1471490617301448
Lu Y, Biancotto A, Cheung F, Remmers E, Shah N, McCoy JP, Tsang JS. Systematic analysis of cell-to-cell expression variation of T lymphocytes in a human cohort identifies aging and genetic associations. Immunity. 2016 Nov 15;45(5):1162-1175. Preview from Immunity: http://www.cell.com/immunity/fulltext/S1074-7613(16)30430-7.
Narayanan M, Martins AJ, Tsang JS. Robust inference of cell-to-cell expression variations from single- and K-cell profiling. PLoS Comput Biol. 2016 Jul 20;12(7):e1005016.
Shah N*, Guo Y*, Wendelsdorf KV*, Lu Y, Sparks R, Tsang JS. A crowdsourcing approach for reusing and meta-analyzing gene expression data. Nat Biotechnol. 2016 Aug;34(8):803-6. *equal contribution
Tsang JS. Utilizing population variation, vaccination, and systems biology to study human immunology. Trends Immunol. 2015 Aug; 36 (8): 479-93.
Tsang JS*, Schwartzberg PL*, Kotliarov Y, Biancotto A, Xie Z, Germain RN, Wang E, Olnes MJ, Narayanan M, Golding H, Moir S, Dickler HB, Perl S, Cheung F; Baylor HIPC Center; CHI Consortium. Global analyses of human immune variation reveal baseline predictors of postvaccination responses. Cell. 2014 Apr 10;157(2):499-513. *senior and corresponding author
Systems Human Immunology
Single-Cell Variations – Functions and Networks
Public Data Reuse and Crowdsourcing