A multimodal cell census and atlas of the mammalian primary motor cortex

地图集(解剖学) 生物 初级运动皮层 人口普查 神经科学 计算生物学 运动皮层 地图学 地理 医学 解剖 环境卫生 人口 刺激
作者
BRAIN Initiative Cell Census Network (BICCN),BRAIN Initiative Cell Census Network (BICCN) Corresponding authors,Edward M. Callaway,Hong‐Wei Dong,Joseph R. Ecker,Michael Hawrylycz,Z. Josh Huang,Ed S. Lein,John Ngai,Pavel Osten,Bing Ren,Andreas S. Tolias,Owen White,Hongkui Zeng,Xiaowei Zhuang,BICCN contributing principal investigators,Giorgio A. Ascoli,M. Margarita Behrens,Jerold Chun,Guoping Feng
出处
期刊:Nature [Nature Portfolio]
卷期号:598 (7879): 86-102 被引量:564
标识
DOI:10.1038/s41586-021-03950-0
摘要

Abstract Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization 1–5 . First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

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