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Updated: Jun 13, 2025

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通过联合推断细胞身份和突触连接性来解码神经元连接

Himanshu Pawankumar Gupta¹, Anthony W Azevedo², Yu-C-Hieh David Chen³

¹Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.

bioRxiv : the preprint server for biology

|March 17, 2025

概括

研究人员绘制了Drosophila腿部运动神经元中的基因表达图,以了解神经回路如何发展. 一个名为ConnectionMiner的新工具将基因活动与神经线路联系起来,揭示了电机电路组件的关键基因.

科学领域:

神经科学是一个神经科学.
发展生物学发展生物学
计算生物学计算生物学

背景情况:

运动行为依赖于从前运动神经元 (preMN) 电路接收输入的运动神经元 (MN).
了解这些运动回路的发育组合是神经科学的一个关键挑战.

研究的目的:

研究Drosophila melanogaster中控制腿部运动的运动回路的发展背后的分子机制.
识别与神经连接和电路组合相关的基因表达模式.

主要方法:

为跨越发育时间点的腿部MN生成单细胞RNA测序 (scRNAseq) 数据集.
开发了ConnectionMiner,这是一个集成scRNAseq数据与电子显微镜连接组的计算工具.
使用ConnectionMiner来完善细胞类型的注释,并识别与突触连接相关的基因表达特征.

主要成果:

鉴定了转录因子 (TF) 和细胞粘附分子 (CAM) 作为MN分子多样性的驱动因素.
ConnectionMiner成功地注释了MNs和preMNs,并将基因表达与突触连接强度联系起来.
发现了可能参与编排前MN-MN电路组装的候选基因组合.

结论:

基因表达特征,特别是TF和CAM,对于运动神经元多样性和电路形成至关重要.
连接Miner是一个强大的工具,用于整合转录和连接的数据,以了解神经电路的发展.
这项研究提供了一个转录框架,以了解Drosophila的运动电路是如何从preMNs组装到MNs的.

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