In this work, we sought to analyze cellular diversity across many brain regions in order to investigate shared and region-specific patterns in cellular composition and gene expression. We overcame several challenges. First, dissociating adult brain into healthy, representative cell suspensions is difficult; many scRNA-seq studies have thus used younger mice in which developmental programs are comingled with the expression differences that underlie functional specializations. Here we developed techniques, borrowing ideas from preparations for electrophysiological recordings, that allowed adult brain tissue to be dissociated into intact cell bodies while representing all major cell classes. Second, scRNA-seq data are simultaneously shaped by cellular categories, continuously varying gradients, and technical artifacts; cell clusters derived from scRNA-seq often reflect unknown combinations of these effects. We developed analytical methods to separate biological and technical influences on single-cell data, enabling a more transparent understanding of the relationships driving cellular classifications.
