Automated reconstruction of dendritic and axonal arbors reveals molecular correlates of neuroanatomy
Gliko, O.; Mallory, M.; Dalley, R.; Gala, R.; Gornet, J.; Zeng, H.; Sorensen, S.; Sumbul, U.
Show 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 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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Data-driven fine-grained region discovery in the mouse brain with transformers 97%
- Light microscopy based approach for mapping connectivity with molecular specificity 96%
- STAIG: Spatial Transcriptomics Analysis via Image-Aided Graph Contrastive Learning for Domain Exploration and Alignment-Free Integration 96%
Similar papers in this journal
Similar papers in this journal
- Single-neuron models linking electrophysiology, morphology and transcriptomics across cortical cell types 96%
- Stimulus information guides the emergence of behavior related signals in primary somatosensory cortex during learning 95%
- Perpetual step-like restructuring of hippocampal circuit dynamics 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.