A transcriptomic axis predicts state modulation of cortical interneurons

Stéphane Bugeon (), Joshua Duffield, Mario Dipoppa, Anne Ritoux, Isabelle Prankerd, Dimitris Nicoloutsopoulos, David Orme, Maxwell Shinn, Han Peng, Hamish Forrest, Aiste Viduolyte, Charu Bai Reddy, Yoh Isogai, Matteo Carandini and Kenneth D. Harris ()
Additional contact information
Stéphane Bugeon: University College London
Joshua Duffield: University College London
Mario Dipoppa: University College London
Anne Ritoux: University College London
Isabelle Prankerd: University College London
Dimitris Nicoloutsopoulos: University College London
David Orme: University College London
Maxwell Shinn: University College London
Han Peng: University of Oxford
Hamish Forrest: University College London
Aiste Viduolyte: University College London
Charu Bai Reddy: University College London
Yoh Isogai: University College London
Matteo Carandini: University College London
Kenneth D. Harris: University College London

Nature, 2022, vol. 607, issue 7918, 330-338

Abstract: Abstract Transcriptomics has revealed that cortical inhibitory neurons exhibit a great diversity of fine molecular subtypes1–6, but it is not known whether these subtypes have correspondingly diverse patterns of activity in the living brain. Here we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, which are organized by a single factor: position along the main axis of transcriptomic variation. We combined in vivo two-photon calcium imaging of mouse V1 with a transcriptomic method to identify mRNA for 72 selected genes in ex vivo slices. We classified inhibitory neurons imaged in layers 1–3 into a three-level hierarchy of 5 subclasses, 11 types and 35 subtypes using previously defined transcriptomic clusters3. Responses to visual stimuli differed significantly only between subclasses, with cells in the Sncg subclass uniformly suppressed, and cells in the other subclasses predominantly excited. Modulation by brain state differed at all hierarchical levels but could be largely predicted from the first transcriptomic principal component, which also predicted correlations with simultaneously recorded cells. Inhibitory subtypes that fired more in resting, oscillatory brain states had a smaller fraction of their axonal projections in layer 1, narrower spikes, lower input resistance and weaker adaptation as determined in vitro7, and expressed more inhibitory cholinergic receptors. Subtypes that fired more during arousal had the opposite properties. Thus, a simple principle may largely explain how diverse inhibitory V1 subtypes shape state-dependent cortical processing.

Date: 2022
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DOI: 10.1038/s41586-022-04915-7

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