Multi-modal spatial omics represents a paradigm shift in understanding complex biological systems by integrating diverse omics modalities within their native tissue contexts. Different spatial omics techniques capture distinct molecular information, including transcriptomics, proteomics, metabolomics, chromatin accessibility, and histone modifications. Together, these modalities provide a comprehensive view of cellular and tissue functions, which is crucial for unraveling the molecular mechanisms of diseases. The commercialization of spatial omics technologies by companies such as 10x Genomics, NanoString, and Vizgen has made them widely accessible, leading to their rapid adoption in many laboratories. Each platform offers at least one spatially resolved omics modality, often paired with high-resolution hematoxylin and eosin (H&E) images, enabling simultaneous examination of molecular and morphological data from the same tissue slice. Integrating spatial omics modalities with tissue morphology is essential for achieving a more holistic and precise understanding of tissue architecture and function at both cellular and molecular levels. One focus of my lab is to develop state-of-the-art machine learning analytical tools to help researchers better mine the rich information in spatial multi-omics data.

Related publications: MISO, METI, TESLA, SpaGCN.

In collaboration with: Michael Epstein, Jingjing Yang.