Protocol for automated graph-based clustering of single-cell RNA-seq data with application in mouse intestinal stem cells

We present a protocol for isolating highly purified crypt epithelial cells from the mouse intestine for single-cell RNA sequencing (scRNA-seq). Optimized for the mouse jejunum, it can be adapted to all intestinal tracts, including the colon. The pipeline incorporates automated community detection of cell populations (ACDC), a time- and memory-efficient Python package for automated graph-based optimal clustering of large scRNA-seq datasets. We demonstrate its usage to identify cellular populations in an intestinal stem cell dataset and generate publication-ready figures. For complete details on the use and execution of this protocol, please refer to Malagola et al.1.