Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics

TL;DR

This paper introduces TopACT, a topological method that classifies and locates individual cell types in subcellular spatial transcriptomics data, unifying gene expression and tissue organization across scales.

Contribution

TopACT is a novel topological approach that identifies and localizes individual cells in nanoscale spatial transcriptomics without prior cell boundary knowledge.

Findings

Successfully predicts immune cell ring structures in kidney glomeruli

Validates predictions with immunofluorescent imaging

Unifies subcellular and tissue-scale biological data

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.