The Wang Lab

My research interests lie in developing computational methods and mathematical models to quantitatively model cellular phenotypes and uncover links between cellular phenotypes and individual phenotypes such as disease. The general directions are as follows:

I. Intelligent Algorithms for Enhancing, Generating, and Functionally Analyzing Single-Cell Spatial Multi-Omics

Single-cell and spatial multi-omic technologies have revolutionized our understanding of cellular heterogeneity in complex biological systems. However, corresponding analyses currently face various challenges, including inadequate resolution, coverage, and difficulty integrating and generating heterogeneous multi-modal data. To address these issues, we have developed a series of intelligent algorithms. STRIDE (Nucleic Acids Res. 2022) and Cellist (Nat Genet. 2026) enhance low-resolution and high-resolution spatial transcriptomics data signals, respectively, elevating them to the single-cell level. SCRIP (Nucleic Acids Res. 2022) and SCRIPro (Bioinform. 2024) construct gene regulatory networks using single-cell and spatial multi-omics data. EvaCCI assesses intercellular interactions (Genome Biol. 2022), while SCREE analyzes multimodal single-cell CRISPR screen data (Brief. Bioinformatics 2023). These algorithms enhance the usability of single-cell spatial omics data and lay the foundation for the integrative analysis of spatiotemporal multimodal data, as well as computational modeling of cellular phenotypes in physiological and pathological conditions.

II. Virtual Cell and Tissue Modeling Using Generative AI and Large-Scale Spatiotemporal Multimodal Data

Cell phenotypes in multicellular systems are regulated by intrinsic factors (such as gene expression regulation) and extrinsic factors (such as intercellular interactions). We have demonstrated the tight connection between intrinsic epigenetic regulations and cell fate determination in mouse IVF and SCNT embryo development (Nature 2016, Nat. Cell Biol. 2018, Cell Stem Cell 2018, 2022, Cell Res. 2022). Currently, we are developing generative virtual cell and tissue models pretrained on large-scale single-cell spatial multimodal datasets. These models aim to uncover the collaborative effects of gene regulation, cellular crosstalk, and other environmental factors, such as metabolites and mechanical influences, on predicting cell and tissue phenotypes. We have developed SELINA (Cell Rep. Methods. 2023), which utilizes a multi-adversarial domain adaptation network to automatically annotate cell types using a large-scale pretrained human scRNA-seq reference. Our goal is to leverage generative AI models to gain deeper insights into the molecular mechanisms driving cell and tissue phenotypes, further guiding and reshaping this transformation process.

III. Connection of Cell Phenotypes to Individual Phenotypes in Complex Disease Microenvironments

Cancer and aging-associated diseases result from an imbalance in cell and tissue phenotypes, shifting away from healthy states. Our team combines AI-driven virtual cell and tissue models with experimental validations to identify the drivers of disease-related cell and tissue phenotypes in tumors and aging-related diseases. We have established a comprehensive single-cell RNA sequencing data resource, TISCH (Nucleic Acids Res. 2021, 2023), for analyzing gene expression and cellular composition in the tumor microenvironment. We have also constructed a cell phenotype atlas for all cancer types, TabulaTIME (Nat. Cancer 2025), and discovered a widely prevalent profibrotic ecotype that regulates tumor immunity. Our team is currently collaborating closely with oncologists and immunologists to investigate the mechanisms of tumor microenvironment evolution and immune therapy resistance in different cancer types (Cell 2024; Nat. Genet. 2025; Cancer Immunol. Res. 2023; Genome Med. 2023; EMBO J. 2023).