跨多种方式解剖皮层gaba能细胞的细胞多样性:神经元分类学的一个转折点。

Faculty reviews Pub Date : 2022-05-11 eCollection Date: 2022-01-01 DOI:10.12703/r-01-000009
Paola Arlotta, Fei Chen, Simona Lodato, Troy W Margrie, Tomasz J Nowakowski, Thoru Pederson, Beatriz Rico
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引用次数: 0

摘要

破解大脑的复杂性需要了解其神经元回路的结构、功能和发展。基于特定特征/行为对神经元进行分组的神经元分类已经成为以系统和可重复的方式进一步分析不同亚型的必要条件。一个综合的分类框架,考虑到多种定义和定量特征,将为推断属于同一神经元类型的细胞的广义规则提供参考,并最终预测细胞行为,即使没有实验测量。神经系统细胞类型分类的技术在可扩展性和分辨率方面正在迅速发展。虽然这些方法描述了神经元形态、电生理和基因表达方面惊人的多样性,但在很大程度上仍然缺乏不同分析模式之间一致性的可靠度量,从而导致统一的分类。Gouwens等人以大脑皮层的gabaergy神经元为研究对象,首创了基于同时分析转录网络、记录固有电生理特性和重建同一细胞的3D形态的综合细胞类型分类方法。他们的全面和高质量的数据提供了一个新的框架来阐明什么可能被认为是“神经元细胞类型”。
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Dissecting cellular diversity of cortical GABAergic cells across multiple modalities: A turning point in neuronal taxonomy.

Decoding the complexity of the brain requires an understanding of the architecture, function, and development of its neuronal circuits. Neuronal classifications that group neurons based on specific features/behaviors have become essential to further analyze the different subtypes in a systematic and reproducible way. A comprehensive taxonomic framework, accounting for multiple defining and quantitative features, will provide the reference to infer generalized rules for cells ascribed to the same neuronal type, and eventually predict cellular behaviors, even in the absence of experimental measures. Technologies that enable cell-type classification in the nervous system are rapidly evolving in scalability and resolution. While these approaches depict astonishing diversity in neuronal morphology, electrophysiology, and gene expression, a robust metric of the coherence between different profiling modalities leading to a unified classification is still largely missing. Focusing on GABAergic neurons of the cerebral cortex, Gouwens et al.1 pioneered the first integrated cell-type classification based on the simultaneous analysis of the transcriptional networks, the recording of intrinsic electrophysiological properties, and the reconstruction of 3D morphologies of the same cell. Their comprehensive and high-quality data provide a new framework to shed light on what may be considered a "neuronal cell type."

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