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

Single-cell RNA-sequencing (scRNA-seq) is a powerful technique for describing cell states. Unfortunately, identifying the spatial arrangement of these states 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 a fluorescent dye to establish a gradient along the radial axis of 3D disease models. Classification of sample layers based on dye accessibility permits dissociated and sorted cells to be retrospectively characterized by transcriptomic and regional identity. Using SEEP, we analyze spheroid, organoid, and in vivo tumor 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. Spatially segmented single-cell RNA sequencing was performed using the SEEP method and the inDrop protocol as detailed in the manuscript related to this work