Automated reconstruction of dendritic and axonal arbors reveals molecular correlates of neuroanatomy
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 arbor reconstruction on a dataset of 802 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 many cellular properties used to define cell types. We uncover molecular correlates of laminar innervation on multiple molecularly defined neuronal subclasses and types. In particular, our results reveal molecular correlates of the variability in Layer 1 (L1) innervation even in a transcriptomically defined subpopulation of Martinotti cells in the adult mouse neocortex.
Additional Information
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. This version posted March 8, 2022. We wish to thank the Allen Institute for Brain Science founder, Paul G Allen, for his vision, encouragement and support. We thank Michael Hawrylycz and Claire Gamlin for helpful feedback on the manuscript. This work was supported by the National Institute Of Mental Health of the National Institutes of Health under award numbers 1U01MH114824-01 and 1RF1MH128778-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Data availability: Transcriptomic and morphological data supporting the findings of this study is available online at https://portal.brain-map.org/explore/classes/multimodal-characterization ("Neurons in Mouse Primary Visual Cortex"). Code availability: Code pertaining to this study as well as the trained neural network model for automated segmentation are available at https://github.com/ogliko/patchseq-autorecon and https://github.com/rhngla/topo-preserve-fastmarching. The authors have declared no competing interest.Attached Files
Submitted - 2022.03.07.482900v1.full.pdf
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Additional details
- Eprint ID
- 113899
- Resolver ID
- CaltechAUTHORS:20220314-687639400
- Paul G. Allen Family Foundation
- 1U01MH114824-01
- NIH
- 1RF1MH128778-01
- NIH
- Created
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2022-03-14Created from EPrint's datestamp field
- Updated
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2022-03-14Created from EPrint's last_modified field