bioRxivNeurosciencePreprint
Dendritome Mapping Unveils Spatial Organization of Striatal D1/D2-Neuron Morphology
Chang Sin ParkiD, Ming YaniD, Muye ZhuiD, Masood A. AkramiD, Nicholas N. FosteriD, Andrew Bennecke, Christopher Choi, Karl Marrett, Keivan MoradiiD, Jason Y. ZhangiD, Gabrielle Magat, Sumit NandaiD, Raymond VacaiD, Kathleen Wijaya, Jedrick Regala Zablan, Sebastian LeeiD, Cassidy Song, Mary Jasmine Lara, Madeline LouieiD, Jason CongiD, Yongsoo KimiD, Giorgio A. AscoliiD, Peter LangfelderiD, Daniel Jacob TwardiD, Hong‐Wei DongiD, X. William YangiD
- Citations
- 1
Abstract
Morphology is a cardinal feature of a neuron and mediates its functions, but profiling neuronal morphologies at scale remains a formidable challenge. Here we describe a generalizable pipeline for large-scale brainwide profiling of dendritic morphology of genetically-defined single neurons in the mouse brain. We generated a dataset of 3,762 3D-reconstructed and reference-atlas mapped striatal D1- and D2-medium spiny neurons (MSNs). Integrative morphometric analyses reveal distinct impacts of D1/D2 and anatomical locations on MSN morphology. To analyze striatal regional features of MSN dendrites without prior anatomical constraints, we assigned MSNs to a lattice of cubic boxes in the reference brain atlas, and summarized morphometric representation ("eigen-morph") for each box and clustered boxes with shared morphometry. This analysis reveals 6 modules with characteristic dendritic features and spanning contiguous striatal territories, each receiving distinct corticostriatal inputs. Finally, we found aging confers robust and concordant dendritic length and branching defects in D1/D2-MSNs, while Huntingtons disease (HD) mice exhibit MSN-subtype and striatal regional specific pathology. Together, our study demonstrates a systems-biology approach to profile dendritic morphology of genetically-defined single-neurons; and defines novel striatal D1/D2-MSN morphological territories and aging- or HD-associated pathologies.
- organism: mouse
- assay: spatial transcriptomics
- research focus: clustering
- research focus: dimensionality reduction
- approach: statistical model
- approach: machine learning (classical)
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