Automated reconstruction of dendritic and axonal arbors reveals molecular correlates of neuroanatomy

Abstract

AbstractNeuronal 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.