Jean Fan

Date: 11/30/23

Speaker: Jean Fan, Assistant Professor of Biomedical Engineering at Johns Hopkins University

Title: Computational Methods for Comparative Spatial Omics Analysis

Abstract: Mammalian tissues are comprised of many molecularly and functionally distinct cell-types and cell-states organized into meso-scale structures and patterns to achieve intricate biological functions. Likewise, cells within tissues regulate thousands of interacting genes and other molecules to sense, respond to, and shape their tissue microenvironments. In turn, extrinsic signals from the local microenvironment impact cell state and cell-type specification. Recent advances in high-throughput spatial transcriptomics (ST) technologies now enable the identification and characterization of these cell-type and their molecular states in health versus disease while preserving the cell’s spatial context. Application of these ST technologies provides the opportunity to contribute to a more complete understanding of how cellular spatial organization relates to tissue function and how cellular spatial organization is altered in disease. New statistical approaches and scalable computational tools are needed to connect these molecular states and spatial-contextual differences. In this talk, I will provide an overview the latest ST technologies as well as associated computational analysis methods developed by my lab and their applications. I will highlight our development of STalign to align 2D spatially resolved transcriptomics datasets within and across technologies and to 3D common coordinate framework in order to make molecular and cell-type compositional comparisons at matched spatial locations across structurally similar tissues. I will present ongoing developments of CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, to quantitatively evaluate cell-type spatial relationships across different length scales to make cell-type relational comparisons. We anticipate that such statistical approaches and computational methods for analyzing spatially resolved transcriptomic data will offer the potential to identify and characterize spatial organizational differences and contribute to important fundamental biological insights regarding how cell-type spatial organization differs in healthy and diseased settings.


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