ScienceNeurosciencePeer-reviewed journal article
A comparative atlas of single-cell chromatin accessibility in the human brain
Yang Eric LiiD, Sebastian PreißliD, Michael MilleriD, Nicholas D. JohnsoniD, Zihan WangiD, Henry Jiao, Chenxu ZhuiD, Zhaoning WangiD, Yang XieiD, Olivier PoirioniD, Colin KerniD, António Pinto‐DuarteiD, Wei TianiD, Kimberly SilettiiD, Nora EmersoniD, Julia K. OsteeniD, Jacinta D. LuceroiD, Lin LiniD, Qian YangiD, Quan ZhuiD, Nathan R. ZemkeiD, Sarah G. EspinozaiD, Anna Marie YannyiD, Julie K. NyhusiD, Nick DeeiD, , Bing ReniD
Abstract
The human brain contains an extraordinarily diverse set of neuronal and glial cell types. Recent advances in single cell transcriptomics have begun to delineate the cellular heterogeneity in different brain regions, but the transcriptional regulatory programs responsible for the identity and function of each brain cell type remain to be defined. Here, we carried out single nucleus ATAC-seq analysis to probe the open chromatin landscape from over 1.1 million cells in 42 brain regions of three neurotypical adult donors. Integrative analysis of the resulting data identified 107 distinct cell types and revealed the cell-type-specific usage of 544,735 candidate cis-regulatory DNA elements (cCREs) in the human genome. Nearly 1/3 of them displayed sequence conservation as well as chromatin accessibility in the mouse brain. On the other hand, nearly 40% cCREs were human specific, with chromatin accessibility associated with species-restricted gene expression. Interestingly, these human specific cCREs were enriched for distinct families of retrotransposable elements, which displayed cell-type-specific chromatin accessibility. We uncovered strong associations between specific brain cell types and neuropsychiatric disorders. We futher developed deep learning models to predict regulatory function of non-coding disease risk variants.
- organism: human
- assay: scATAC-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- approach: deep learning
- Citations
- 173
- FWCI
- 15.37
- Field percentile
- top 1%
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