Single-cell analysis of chromatin accessibility in the adult mouse brain

Songpeng Zu, Yang Eric Li, Kangli Wang, Ethan J. Armand, Sainath Mamde, Maria Luisa Amaral, Yuelai Wang, Andre Chu, Yang Xie, Michael Miller, Jie Xu, Zhaoning Wang, Kai Zhang, Bojing Jia, Xiaomeng Hou, Lin Lin, Qian Yang, Seoyeon Lee, Bin Li, Samantha Kuan, Hanqing Liu, Jingtian Zhou, Antonio Pinto-Duarte, Jacinta Lucero, Julia Osteen, Michael Nunn, Kimberly A. Smith, Bosiljka Tasic, Zizhen Yao, Hongkui Zeng, Zihan Wang, Jingbo Shang, M. Margarita Behrens, Joseph R. Ecker, Allen Wang, Sebastian Preissl and Bing Ren ()
Additional contact information
Songpeng Zu: University of California San Diego, School of Medicine
Yang Eric Li: University of California San Diego, School of Medicine
Kangli Wang: University of California San Diego, School of Medicine
Ethan J. Armand: University of California San Diego, School of Medicine
Sainath Mamde: University of California San Diego, School of Medicine
Maria Luisa Amaral: University of California San Diego, School of Medicine
Yuelai Wang: University of California San Diego, School of Medicine
Andre Chu: University of California San Diego, School of Medicine
Yang Xie: University of California San Diego, School of Medicine
Michael Miller: University of California San Diego, School of Medicine
Jie Xu: University of California San Diego, School of Medicine
Zhaoning Wang: University of California San Diego, School of Medicine
Kai Zhang: University of California San Diego, School of Medicine
Bojing Jia: University of California San Diego, School of Medicine
Xiaomeng Hou: University of California San Diego, School of Medicine
Lin Lin: University of California San Diego, School of Medicine
Qian Yang: University of California San Diego, School of Medicine
Seoyeon Lee: University of California San Diego, School of Medicine
Bin Li: University of California San Diego, School of Medicine
Samantha Kuan: University of California San Diego, School of Medicine
Hanqing Liu: The Salk Institute for Biological Studies
Jingtian Zhou: The Salk Institute for Biological Studies
Antonio Pinto-Duarte: The Salk Institute for Biological Studies
Jacinta Lucero: The Salk Institute for Biological Studies
Julia Osteen: The Salk Institute for Biological Studies
Michael Nunn: The Salk Institute for Biological Studies
Kimberly A. Smith: Allen Institute for Brain Science
Bosiljka Tasic: Allen Institute for Brain Science
Zizhen Yao: Allen Institute for Brain Science
Hongkui Zeng: Allen Institute for Brain Science
Zihan Wang: University of California San Diego
Jingbo Shang: University of California San Diego
M. Margarita Behrens: The Salk Institute for Biological Studies
Joseph R. Ecker: The Salk Institute for Biological Studies
Allen Wang: University of California San Diego, School of Medicine
Sebastian Preissl: University of California San Diego, School of Medicine
Bing Ren: University of California San Diego, School of Medicine

Nature, 2023, vol. 624, issue 7991, 378-389

Abstract: Abstract Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1–4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs—specifically, those identified from a subset of cortical excitatory neurons—are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.

Date: 2023
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DOI: 10.1038/s41586-023-06824-9

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