This study takes on the problem of bridging transcriptional data to neuronal phenotype and function by using publicly available datasets characterizing distinct neuronal populations based on gene expression, electrophysiology and morphology. In addition, a non-published PatchSeq dataset of Pvalb-cre positive cells in CA1 was used, which is the dataset submitted here. Taken together, these datasets were used to identify cross-cell type correlations between these data modalities. Detected correlations were classified as class-driven if they could be explained by differences between excitatory and inhibitory cell classes, or non-class driven if they could be explained by gradient like phenotypic differences within cell classes. Some genes whose relationships to electrophysiological or morphological properties were found to to be specific to either excitatory or inhibitory cell types. The Patch Seq data specifically allowed simultaneous single-cell characterization of gene expression and electrophysiology, showing that the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. SOURCE: Sten LinnarssonMolecular Neurobiology Karolinska Institutet

Pluto Bioinformatics

GSE130950: RNA-seq data of PatchSeq dataset from Pvalb-Cre positive interneurons in the mouse hippocamus CA1 region