Transcriptomic correlates of state modulation in GABAergic interneurons: A cross-species analysis

Abstract

GABAergic inhibitory interneurons comprise many subtypes that differ in their molecular, anatomical and functional properties. In mouse visual cortex, they also differ in their modulation with an animal’s behavioural state, and this state modulation can be predicted from the first principal component (PC) of the gene expression matrix. Here, we ask whether this link between transcriptome and state-dependent processing generalises across species. To this end, we analysed seven single-cell and single-nucleus RNA sequencing datasets from mouse, human, songbird, and turtle forebrains. Despite homology at the level of cell types, we found clear differences between transcriptomic PCs, with greater dissimilarities between evolutionarily distant species. These dissim­ilarities arise from two factors: divergence in gene expression within homologous cell types and divergence in cell type abundance. We also compare the expression of cholinergic receptors, which are thought to causally link transcriptome and state modulation. Several cholinergic receptors predictive of state modulation in mouse interneurons are differentially expressed between species. Circuit modelling and mathematical analyses suggest conditions under which these expression differences could translate into functional differences.Significance StatementThe brain is a complex network of many different cell types. A particularly diverse group of cells is that of inhibitory interneurons, named for their suppressive effect. These interneurons change their activity depending on an animal’s behavioural state—at least in mice. Here, we investigate if this finding generalises to other species by comparing gene expression patterns of human, turtle, and zebra finch interneurons. Despite sharing an evolutionary past, we find that only human and mouse interneurons have similar gene expression patterns associated with state modulation. A mathematical model suggests which expression differences in individual cells translate into functional differences at the network level.