GSE126836
Single-cell multi-omic integration compares and contrasts features of brain cell identity
To flexibly model single-cell datasets, we developed LIGER, an algorithm and software package that delineates shared and dataset-specific features of cell identity.  We applied it to four diverse and challenging analyses of human and mouse brain cells, the first two of which include newly generated single-cell transcriptome datasets for mouse and human regions. First, we determined region-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis (BNST). Second, we analyzed expression in the human substantia nigra (SN), integrating cell types across donors, and relating cell types to those in the mouse (using mouse SN data from Saunders et al., 2018 (GEO: GSE116470)). The main cell types identified include neurons, astrocytes, microglia, oligodendrocytes, polydendrocytes, and endothelial cells. Third, we jointly leveraged in situ and single-cell expression data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we integrated mouse cortical single-cell RNA-seq and DNA methylation profiles, revealing mechanisms of cell-type-specific gene regulation. Integrative analyses using LIGER promise to accelerate investigations of cell-type definition, gene regulation, and disease states. Overall design: Single nuclei were extracted and isolated from 1mm biopsy punches from the BNST of 15 mouse brains (8 male and 7 female replicates), and sequenced using the 10X Chromium system (V3). In total, 204,737 BNST nuclei were recovered. Single nuclei were extracted and isolated from manually dissected samples of the SN of seven de-identified postmortem human donors, and sequenced using the 10X chromium system (V2). In total, 44,274 SN nuclei were recovered.