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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.
摘要：
神经元解剖学是脑细胞类型的组织和功能的核心。然而,在明显同质的细胞群体中的解剖学变异性可以掩盖这种见解。这里,我们报告了使用Patch-seq方法表征的813个抑制性神经元数据集上的神经元形态重建和分析的大规模自动化，可以测量单个神经元的多种特性，包括局部形态学和转录签名。我们证明，这些自动重建可以以与手动重建相同的方式使用，以了解一些之间的关系，但不是全部,用于定义细胞类型的细胞属性。我们发现了多个转录上定义的神经元亚类和类型的层状神经支配的基因表达相关性。特别是,我们的结果揭示了成年小鼠新皮质中转录定义的Martinotti细胞亚群中第1层（L1）轴突神经支配的变异性。
