Research interests

Our research focuses on developing computational algorithms and conducting big data mining from single-cell data to understand the mechanism of gene regulation and metabolic reprogramming in defining cell identities. We have developed a series of algorithms and web resources for single-cell transcriptomics, epigenomics, CRISPR screens, and spatial multi-omic data analysis and re-use. Through integrating single-cell and spatial multi-omic datasets, we try to link the changes in immune cell identity to disease phenotypes in cancer, developmental disorders and aging-associated disease.

Computational methods for single-cell and spatial multi-omics

Single-cell multi-omics is important for understanding the cellular heterogeneity of complex biological systems, however, there is a lack of effectively integrated analysis methods. We aim to develop machine learning algorithms for efficiently integrating and analyzing single-cell and spatial multi-omic data. We have developed MAESTRO for integrative analysis of scRNA-seq and scATAC-seq data. STRIDE for improving the resolution of Spatial Transcriptomic data by integrating with scRNA-seq using topic modeling. Developed a scRNA-seq database TISCH for comprehensive visualization of the gene expression and cell-type composition in tumor microenvironment. Now we are working on multiple challenging questions in the single-cell field including automatic cell-type annotation, predicting and unraveling the gene regulation networks, improving the resolution and coverage of spatial transcriptomics, and inferring the cellular cross-talks and metabolic status.

Mechanism in generating and remodeling immunosuppressive tumor microenvironment

Cancer arises from the evasion of immune surveillance, and the immunosuppressive tumor microenvironment has a vital impact on tumor development and metastasis. We aim to integrate single-cell and spatial multi-omics data with comprehensive bioinformatics data analysis to investigate the effects of epigenetic remodeling and metabolic reprogramming in altering the immune cell identities in the TME, and tried to develop potential immune remodeling methods for cancer treatment. We have been working closely with oncologists and immunologists to study the mechanism of carcinogenesis, immune and drug resistance in the different cancer types. We are also performing large data integration analyses to discover phenotypic driving immune and stromal cell types using pan-cancer analyses.

Regulation of cell identity transitions in tumor immunity, developmental and aging-related diseases

Cell identity transition in a broad sense encompasses cell type differentiation, which plays an important role in the development, immune and aging-related diseases. Investigation of epigenetic remodeling and metabolic reprogramming will help to understand the molecular mechanisms of cell-type transition from both intrinsic and extrinsic perspectives, which could be further used to regulate and remodel the transition process. We have revealed the role of histone modifications such as H3K4me3, H3K27me3, H3K9me3, and nucleosome positioning in regulating cell fate decisions during early mammalian embryonic development, and demonstrated the function of broad H3K4me3 and lineage-specific H3K9me3 in cell-type differentiation (Nature 2016, Nat Cell Biol 2018, Cell Res 2022, Cell Stem Cell 2022). We have also developed reprogramming methods based on epigenetic regulators in somatic nuclear-transferred embryo development (Cell Discov 2016, Cell Stem Cell 2018).