电池类型
表观遗传学
生物
转录组
计算生物学
人口
初级运动皮层
运动皮层
细胞
神经科学
基因
基因表达
遗传学
DNA甲基化
医学
刺激
环境卫生
作者
Zizhen Yao,Hanqing Liu,Fangming Xie,Stephan Fischer,Ricky S. Adkins,Andrew I. Aldridge,Seth A. Ament,Anna Bartlett,M. Margarita Behrens,Koen Van den Berge,Darren Bertagnolli,Hector Roux de Bézieux,Tommaso Biancalani,A. Sina Booeshaghi,Héctor Corrada Bravo,Tamara Casper,Carlo Colantuoni,Jonathan Crabtree,Heather H. Creasy,Kirsten Crichton
出处
期刊:Nature
[Nature Portfolio]
日期:2021-10-06
卷期号:598 (7879): 103-110
被引量:383
标识
DOI:10.1038/s41586-021-03500-8
摘要
Abstract Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.
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