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.
@article{arxiv.2212.06505,
title = {Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics},
author = {Katherine Benjamin and Aneesha Bhandari and Zhouchun Shang and Yanan Xing and Yanru An and Nannan Zhang and Yong Hou and Ulrike Tillmann and Katherine R. Bull and Heather A. Harrington},
journal= {arXiv preprint arXiv:2212.06505},
year = {2022}
}