Single-cell RNA-sequencing (scRNA-seq) is a powerful technique for describing cell states. Unfortunately, identifying the spatial arrangement ofthesestates in tissues remains challenging.Here, we describe SEgmentation by Exogenous Perfusion (SEEP), a rapid and integrated method to link positional location to transcriptional identity within three-dimensional (3D) disease models. The method utilizes the steady-state diffusion kinetics of afluorescentdye to establish a gradient along the radial axis of 3D disease models. Classification ofsample layers based on dye accessibility permits dissociated and sorted cells to beretrospectivelycharacterized by transcriptomic and regional identity. Using SEEP, we analyze spheroid, organoid, andin vivotumor models of high grade serous ovarian cancer (HGSOC). The results validate long-standing beliefs regarding the relationship between cell state and position while also revealing new concepts on how the spatially unique microenvironment of individual cells within tumors influences cell identity. SOURCE: David Morse National Institutes of Health

Pluto Bioinformatics

GSE157299: Positional influence on cellular transcriptional identity revealed through spatially segmented single-cell transcriptomics