A comparative atlas of single-cell chromatin accessibility in the human brain

• 成果类型: Article
• 署名作者: Li, Yang Eric; Preissl, Sebastian; Miller, Michael; Johnson, Nicholas D.; Wang, Zihan; Jiao, Henry; Zhu, Chenxu; Wang, Zhaoning; Xie, Yang; Poirion, Olivier; Kern, Colin; Pinto-Duarte, Antonio; Tian, Wei; Siletti, Kimberly; Emerson, Nora; Osteen, Julia; Lucero, Jacinta; Lin, Lin; Yang, Qian; Zhu, Quan; Zemke, Nathan; Espinoza, Sarah; Yanny, Anna Marie; Nyhus, Julie; Dee, Nick; Casper, Tamara; Shapovalova, Nadiya; Hirschstein, Daniel; Hodge, Rebecca D.; Linnarsson, Sten; Bakken, Trygve; Levi, Boaz; Keene, C. Dirk; Shang, Jingbo; Lein, Ed; Wang, Allen; Behrens, M. Margarita; Ecker, Joseph R.; Ren, Bing
• 署名单位: University of California System; University of California San Diego; University of California System; University of California San Diego; Salk Institute; University of California System; University of California San Diego; Karolinska Institutet; Allen Institute for Brain Science; University of Washington; University of Washington Seattle; Howard Hughes Medical Institute; Salk Institute; Washington University (WUSTL); University of Freiburg
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-10648
• DOI: 10.1126/science.adf7044
• 发表日期: 2023-10-13
• 页码: 180-+
• 关键词: r-package; transposable elements; generation; microglia; HEALTH; STATES

摘要

Recent advances in single-cell transcriptomics have illuminated the diverse neuronal and glial cell types within the human brain. However, the regulatory programs governing cell identity and function remain unclear. Using a single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq), we explored open chromatin landscapes across 1.1 million cells in 42 brain regions from three adults. Integrating this data unveiled 107 distinct cell types and their specific utilization of 544,735 candidate cis-regulatory DNA elements (cCREs) in the human genome. Nearly a third of the cCREs demonstrated conservation and chromatin accessibility in the mouse brain cells. We reveal strong links between specific brain cell types and neuropsychiatric disorders including schizophrenia, bipolar disorder, Alzheimer's disease (AD), and major depression, and have developed deep learning models to predict the regulatory roles of noncoding risk variants in these disorders.