Huanhuan Wei

Topic: Multi-omics Analytics for Personalized Medicine

Project Summary: First, we will develop a framework that integrates spatial transcriptomics, single-cell RNA-seq, single-cell ATAC-seq, high-resolution imaging, and single-cell targeted protein data to identify tissue microenvironments. By utilizing network-based variable selection and regression of cell morphology, we will aggregate selected features using cell adjacency matrices to cluster tissue areas into microenvironments. This multi-modal integration promises to uncover new microenvironment characteristics for targeted therapeutics. Second, we will focus on identifying disease progression-associated changes in tissue microenvironments. Using known biomarker genes, we will differentiate microenvironments and assess disease severity and progression. We will analyze changes in cell compositions, expression profiles, gene regulatory networks, and cell-cell communication networks. Deconvolved spatial transcriptomics and causal network approaches will aid in constructing gene regulatory networks, while Connectome and graph attention network methods will establish cell-cell communication networks. Correlations with disease progression will be examined independently and combined using neural networks to gain a comprehensive understanding for precise therapeutic development.