DMS/NIGMS 1: Topological Study on Histological Images and Spatial Transcriptomics

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With the rapid advance of high-resolution transcriptomic profiling techniques, recent years have witnessed an increased interest in the study of the tissue microenvironment (TM) arising in cancer research and neuroscience, i.e., the collection of cells and structures in a tissue, such as neuron and glia in neural tissue or immune, stroma, and epithelial cells in tumors. The spatial configuration of these cells and structures plays pivotal roles in tissue function. Researchers have obtained high resolution transcriptomic and imaging data for TM study. The two types of information complement each other. High resolution transcriptomic profiling, such as single-cell RNA-seq (scRNA), provides cellular level molecular information, but does not carry local contextual information of cell, while histology image analysis provides detailed context, but does not provide corresponding cellular gene expression profiles. To combine them is challenging due to a lack of one-to-one correspondence between cells in transcriptomics and cells in histology images. This project intends to unify transcriptomics and bioimage informatics for a comprehensive study of TM, applying the advanced topological data analysis (TDA) methodology on the newly emerged spatial transcriptomic (ST) data. ST data provides localized spatial transcriptomics. TDA provides the foundation for studying rich contextual information in multi-omics. This project will produce a spatial-context-aware high-resolution mapping of TM transcriptomics. The outcome will be highly impactful. It will not only promote normal tissue level functionality characterization and mechanism study, but will also boost various types of diseases’ diagnosis, prognosis as well as their mechanistic studies. The PI/Co-PIs will create new topological approaches to extract rich contextual information from cells of multiple types in histology images. They will also propose new learning algorithms to integrate such topological information into localized ST scRNA data analysis for better differentiation of cells of different types and states, to build connection between spatial context and cell signaling gene activation, and to map transcriptomics information into whole slide image for visualization.