High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy

Olga Gliko (), Matt Mallory, Rachel Dalley, Rohan Gala, James Gornet, Hongkui Zeng, Staci A. Sorensen and Uygar Sümbül ()
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Olga Gliko: Allen Institute
Matt Mallory: Allen Institute
Rachel Dalley: Allen Institute
Rohan Gala: Allen Institute
James Gornet: California Institute of Technology
Hongkui Zeng: Allen Institute
Staci A. Sorensen: Allen Institute
Uygar Sümbül: Allen Institute

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Neuronal anatomy is central to the organization and function of brain cell types. However, anatomical variability within apparently homogeneous populations of cells can obscure such insights. Here, we report large-scale automation of neuronal morphology reconstruction and analysis on a dataset of 813 inhibitory neurons characterized using the Patch-seq method, which enables measurement of multiple properties from individual neurons, including local morphology and transcriptional signature. We demonstrate that these automated reconstructions can be used in the same manner as manual reconstructions to understand the relationship between some, but not all, cellular properties used to define cell types. We uncover gene expression correlates of laminar innervation on multiple transcriptomically defined neuronal subclasses and types. In particular, our results reveal correlates of the variability in Layer 1 (L1) axonal innervation in a transcriptomically defined subpopulation of Martinotti cells in the adult mouse neocortex.

Date: 2024
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DOI: 10.1038/s41467-024-50728-9

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