Perisomatic ultrastructure efficiently classifies cells in mouse cortex

doi:10.1038/s41586-024-07765-7

2025-04-09

Leila Elabbady

Sharmishtaa Seshamani

Shang Mu

Gayathri Mahalingam

Casey M Schneider-Mizell

Nuno Maçarico da Costa

DOI：10.1038/s41586-024-07765-7

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哺乳动物新皮质包含一组高度多样的细胞类型。这些细胞类型已通过多种分子、电生理和形态学方法1, 2, 3-4进行了系统映射。每种模态为探究细胞类型特化所依赖的生物学过程的变异提供了新的视角。细胞尺度的电子显微镜提供了密集的超微结构检查，以及对脑细胞亚细胞组织的无偏视角，包括其突触连接和纳米级形态。在包含数万神经元的数据中，其中大部分重建不完整，识别细胞类型成为分析中的一个明确挑战5。为应对这一挑战，本文通过电子显微镜获得的定量特征，对一立方毫米皮质内所有细胞的胞体区域进行了系统调查。该分析表明，胞体周边区域足以用于识别细胞类型，包括主要基于其连接模式定义的类型。随后，我们描述了这种分类如何促进细胞类型特异性连接特征的表征，以及在数据集中定位具有罕见连接模式的细胞。

Keyword:

VISUAL-CORTEX

MOTOR

RAT

CAT

RECONSTRUCTION

INTERNEURONS

INHIBITION

CIRCUITS

NEURONS

期刊

Nature

IF:

48.5

论文数:

1.8W

被引数:

96.5W

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暂无机构信息

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