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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Olga Gliko & Matt Mallory & Rachel Dalley & Rohan Gala & James Gornet & Hongkui Zeng & Staci A. Sorensen & Uygar Sümbül, 2024. "High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50728-9
    DOI: 10.1038/s41467-024-50728-9
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    References listed on IDEAS

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