GSE264334 Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [Xenium]

Contributors : Katherine Benjamin ; Aneesha Bhandari ; Jessica D Kepple ; Rui Qi ; Zhouchun Shang ; Yanan Xing ; Yanru An ; Nannan Zhang ; Yong Hou ; Tanya L Crockford ; Oliver MacCallion ; Fadi Issa ; Joanna Hester ; Ulrike Tillmann ; Heather A Harrington ; Katherine R BullSeries Type : OtherOrganism : Homo sapiensSpatial 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. In extant mouse brain data, TopACT locates previously undetectable macrophages. 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 multiplex imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene ...
Source: GEO: Gene Expression Omnibus - Category: Genetics & Stem Cells Tags: Other Homo sapiens Source Type: research