NatureNeurosciencePeer-reviewed journal article
An Atlas of Gene Regulatory Elements in Adult Mouse Cerebrum
Yang Eric LiiD, Sebastian PreißliD, Xiaomeng HouiD, Ziyang ZhangiD, Kai ZhangiD, Yunjiang QiuiD, Olivier PoirioniD, Bin LiiD, Joshua ChiouiD, Hanqing LiuiD, António Pinto‐DuarteiD, Naoki KuboiD, Xiaoyu YangiD, Rongxin FangiD, Xinxin WangiD, Jee Yun HaniD, Jacinta D. LuceroiD, Yiming YaniD, Michael MilleriD, Samantha KuaniD, David U. GorkiniD, Kyle J. GaultoniD, Yin SheniD, Michael F. NunniD, Eran A. MukameliD, , Bing ReniD
Abstract
The mammalian cerebrum performs high level sensory, motor control and cognitive functions through highly specialized cortical networks and subcortical nuclei. Recent surveys of mouse and human brains with single cell transcriptomics1-3 and high-throughput imaging technologies4,5 have uncovered hundreds of neuronal cell types and a variety of non-neuronal cell types distributed in different brain regions, but the cell-type-specific transcriptional regulatory programs responsible for the unique identity and function of each brain cell type have yet to be elucidated. Here, we probe the accessible chromatin in >800,000 individual nuclei from 45 regions spanning the adult mouse isocortex, olfactory bulb, hippocampus and cerebral nuclei, and use the resulting data to define 491,818 candidate cis regulatory DNA elements in 160 distinct sub-types. We link a significant fraction of them to putative target genes expressed in diverse cerebral cell types and uncover transcriptional regulators involved in a broad spectrum of molecular and cellular pathways in different neuronal and glial cell populations. Our results provide a foundation for comprehensive analysis of gene regulatory programs of the mammalian brain and assist in the interpretation of non-coding risk variants associated with various neurological disease and traits in humans. To facilitate the dissemination of information, we have set up a web portal (http://catlas.org/mousebrain).
- organism: mouse
- assay: ATAC-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 185
- FWCI
- 8.55
- Field percentile
- top 1%
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