Identification of spatially associated subpopulations by combining scRNA-seq and sequential fluorescence in situ hybridization data

How intrinsic gene-regulatory networks interact with a cell’s spatial environment to define its identity remains poorly understood. Here we present an approach to distinguish intrinsic and extrinsic effects on global gene expression by integrating analysis of sequencing-based and imaging-based single-cell transcriptomic profiles, using cross-platform cell-type mapping combined with a hidden Markov random field model. We apply this approach to dissect the cell-type and spatial-domain-associated heterogeneity within the mouse visual cortex region. Our analysis identifies distinct spatially associated, cell-type-independent signatures in the glutamatergic and astrocyte cell compartments. Using these signatures to analyze single-cell RNAseq data, we identify previously unknown spatially associated subpopulations, which are validated by comparison with anatomical structure and Allen Brain Atlas images.