NatureNeurosciencePeer-reviewed journal article
A transcriptomic axis predicts state modulation of cortical interneurons
Stéphane BugeoniD, Joshua A. M. DuffieldiD, Mario DipoppaiD, Anne RitouxiD, Isabelle PrankerdiD, Dimitris Nicoloutsopoulos, David OrmeiD, Maxwell ShinniD, Han Peng, Hamish ForrestiD, Aiste ViduolyteiD, Charu Bai ReddyiD, Yoh IsogaiiD, Matteo CarandiniiD, Kenneth D. HarrisiD
Abstract
Transcriptomics has revealed the exquisite diversity of cortical inhibitory neurons1-7, but it is not known whether these fine molecular subtypes have correspondingly diverse activity patterns in the living brain. Here, we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, but that this diversity is organized by a single factor: position along their main axis of transcriptomic variation. We combined in vivo 2-photon calcium imaging of mouse V1 with a novel transcriptomic method to identify mRNAs for 72 selected genes in ex vivo slices. We used transcriptomic clusters (t-types)4 to classify inhibitory neurons imaged in layers 1-3 using a three-level hierarchy of 5 Families, 11 Classes, and 35 t-types. Visual responses differed significantly only across Families, but modulation by brain state differed at all three hierarchical levels. Nevertheless, this diversity could be predicted from the first transcriptomic principal component, which predicted a cell types brain state modulation and correlations with simultaneously recorded cells. Inhibitory t-types with narrower spikes, lower input resistance, weaker adaptation, and less axon in layer 1 as determined in vitro8 fired more in resting, oscillatory brain states. Transcriptomic types with the opposite properties fired more during arousal. The former cells had more inhibitory cholinergic receptors, and the latter more excitatory receptors. Thus, despite the diversity of V1 inhibitory neurons, a simple principle determines how their joint activity shapes state-dependent cortical processing.
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 171
- FWCI
- 15.44
- Field percentile
- top 1%
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