Research
Projects in the lab involve systems and computational immunology, which are both approaches and a source of questions for understanding the human immune system and health. On the approach side, our work often involve 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 experiments using ex vivo/in vitro/animal models.
We highlight some of our ongoing projects below. Please contact John Tsang for additional details and other research directions - We are open to new ideas in the broad realm of systems and engineering immunology, including new biological questions and computational/technological approaches.
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.
Human immune variation - 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.
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.
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.
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).