Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics

arXiv2212.06505MaRDI QIDQ6420388

Aneesha Bhandari, Katherine Benjamin, Katherine R. Bull, Yanan Xing, Yanru An, Yong Hou, Zhouchun Shang, Ulrike Tillmann, Heather A. Harrington, Nannan Zhang

Publication date: 13 December 2022

Abstract: Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with immunofluorescent imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.

Has companion code repository: https://github.com/katherine-benjamin/topact-paper

Mathematics Subject Classification ID

Persistent homology and applications, topological data analysis (55N31) Computational methods for problems pertaining to biology (92-08) Computational aspects of data analysis and big data (68T09) Topological data analysis (62R40)

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