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A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex

Author

Listed:
  • Zizhen Yao

    (Allen Institute for Brain Science)

  • Hanqing Liu

    (The Salk Institute for Biological Studies)

  • Fangming Xie

    (University of California, San Diego)

  • Stephan Fischer

    (Cold Spring Harbor Laboratory)

  • Ricky S. Adkins

    (University of Maryland School of Medicine)

  • Andrew I. Aldridge

    (The Salk Institute for Biological Studies)

  • Seth A. Ament

    (University of Maryland School of Medicine)

  • Anna Bartlett

    (The Salk Institute for Biological Studies)

  • M. Margarita Behrens

    (The Salk Institute for Biological Studies)

  • Koen Berge

    (University of California, Berkeley
    Computer Science and Statistics, Ghent University)

  • Darren Bertagnolli

    (Allen Institute for Brain Science)

  • Hector Roux Bézieux

    (School of Public Health, University of California, Berkeley)

  • Tommaso Biancalani

    (Broad Institute of MIT and Harvard)

  • A. Sina Booeshaghi

    (California Institute of Technology)

  • Héctor Corrada Bravo

    (University of Maryland, College Park)

  • Tamara Casper

    (Allen Institute for Brain Science)

  • Carlo Colantuoni

    (Johns Hopkins School of Medicine, Department of Neurology
    Johns Hopkins School of Medicine, Department of Neuroscience
    University of Maryland School of Medicine, Institute for Genome Sciences)

  • Jonathan Crabtree

    (University of Maryland School of Medicine)

  • Heather Creasy

    (University of Maryland School of Medicine)

  • Kirsten Crichton

    (Allen Institute for Brain Science)

  • Megan Crow

    (Cold Spring Harbor Laboratory)

  • Nick Dee

    (Allen Institute for Brain Science)

  • Elizabeth L. Dougherty

    (Broad Institute of MIT and Harvard)

  • Wayne I. Doyle

    (University of California, San Diego)

  • Sandrine Dudoit

    (University of California, Berkeley)

  • Rongxin Fang

    (University of California, San Diego)

  • Victor Felix

    (University of Maryland School of Medicine)

  • Olivia Fong

    (Allen Institute for Brain Science)

  • Michelle Giglio

    (University of Maryland School of Medicine)

  • Jeff Goldy

    (Allen Institute for Brain Science)

  • Mike Hawrylycz

    (Allen Institute for Brain Science)

  • Brian R. Herb

    (University of Maryland School of Medicine)

  • Ronna Hertzano

    (University of Maryland School of Medicine
    University of Maryland School of Medicine)

  • Xiaomeng Hou

    (University of California, San Diego School of Medicine)

  • Qiwen Hu

    (Harvard Medical School)

  • Jayaram Kancherla

    (University of Maryland, College Park)

  • Matthew Kroll

    (Allen Institute for Brain Science)

  • Kanan Lathia

    (Allen Institute for Brain Science)

  • Yang Eric Li

    (Ludwig Institute for Cancer Research)

  • Jacinta D. Lucero

    (The Salk Institute for Biological Studies)

  • Chongyuan Luo

    (The Salk Institute for Biological Studies
    University of California, Los Angeles
    The Salk Institute for Biological Studies)

  • Anup Mahurkar

    (University of Maryland School of Medicine)

  • Delissa McMillen

    (Allen Institute for Brain Science)

  • Naeem M. Nadaf

    (Broad Institute of MIT and Harvard)

  • Joseph R. Nery

    (The Salk Institute for Biological Studies)

  • Thuc Nghi Nguyen

    (Allen Institute for Brain Science)

  • Sheng-Yong Niu

    (The Salk Institute for Biological Studies)

  • Vasilis Ntranos

    (University of California, San Francisco)

  • Joshua Orvis

    (University of Maryland School of Medicine)

  • Julia K. Osteen

    (The Salk Institute for Biological Studies)

  • Thanh Pham

    (Allen Institute for Brain Science)

  • Antonio Pinto-Duarte

    (The Salk Institute for Biological Studies)

  • Olivier Poirion

    (University of California, San Diego School of Medicine)

  • Sebastian Preissl

    (University of California, San Diego School of Medicine)

  • Elizabeth Purdom

    (University of California, Berkeley)

  • Christine Rimorin

    (Allen Institute for Brain Science)

  • Davide Risso

    (University of Padova)

  • Angeline C. Rivkin

    (The Salk Institute for Biological Studies)

  • Kimberly Smith

    (Allen Institute for Brain Science)

  • Kelly Street

    (Dana-Farber Cancer Institute)

  • Josef Sulc

    (Allen Institute for Brain Science)

  • Valentine Svensson

    (California Institute of Technology)

  • Michael Tieu

    (Allen Institute for Brain Science)

  • Amy Torkelson

    (Allen Institute for Brain Science)

  • Herman Tung

    (Allen Institute for Brain Science)

  • Eeshit Dhaval Vaishnav

    (Broad Institute of MIT and Harvard)

  • Charles R. Vanderburg

    (Broad Institute of MIT and Harvard)

  • Cindy Velthoven

    (Allen Institute for Brain Science)

  • Xinxin Wang

    (University of California, San Diego School of Medicine
    Washington University School of Medicine)

  • Owen R. White

    (University of Maryland School of Medicine)

  • Z. Josh Huang

    (Cold Spring Harbor Laboratory)

  • Peter V. Kharchenko

    (Harvard Medical School)

  • Lior Pachter

    (California Institute of Technology)

  • John Ngai

    (University of California, Berkeley)

  • Aviv Regev

    (Broad Institute of MIT and Harvard
    Department of Biology, MIT)

  • Bosiljka Tasic

    (Allen Institute for Brain Science)

  • Joshua D. Welch

    (University of Michigan)

  • Jesse Gillis

    (Cold Spring Harbor Laboratory)

  • Evan Z. Macosko

    (Broad Institute of MIT and Harvard)

  • Bing Ren

    (University of California, San Diego School of Medicine
    Ludwig Institute for Cancer Research)

  • Joseph R. Ecker

    (The Salk Institute for Biological Studies
    The Salk Institute for Biological Studies)

  • Hongkui Zeng

    (Allen Institute for Brain Science)

  • Eran A. Mukamel

    (University of California, San Diego)

Abstract

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Suggested Citation

  • Zizhen Yao & Hanqing Liu & Fangming Xie & Stephan Fischer & Ricky S. Adkins & Andrew I. Aldridge & Seth A. Ament & Anna Bartlett & M. Margarita Behrens & Koen Berge & Darren Bertagnolli & Hector Roux , 2021. "A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex," Nature, Nature, vol. 598(7879), pages 103-110, October.
  • Handle: RePEc:nat:nature:v:598:y:2021:i:7879:d:10.1038_s41586-021-03500-8
    DOI: 10.1038/s41586-021-03500-8
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    Citations

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    Cited by:

    1. Yichuan Cao & Xiamiao Zhao & Songming Tang & Qun Jiang & Sijie Li & Siyu Li & Shengquan Chen, 2024. "scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Jia-Ru Wei & Zhao-Zhe Hao & Chuan Xu & Mengyao Huang & Lei Tang & Nana Xu & Ruifeng Liu & Yuhui Shen & Sarah A. Teichmann & Zhichao Miao & Sheng Liu, 2022. "Identification of visual cortex cell types and species differences using single-cell RNA sequencing," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    3. Arezou Rahimi & Luis A. Vale-Silva & Maria Fälth Savitski & Jovan Tanevski & Julio Saez-Rodriguez, 2024. "DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Kian Kalhor & Chien-Ju Chen & Ho Suk Lee & Matthew Cai & Mahsa Nafisi & Richard Que & Carter R. Palmer & Yixu Yuan & Yida Zhang & Xuwen Li & Jinghui Song & Amanda Knoten & Blue B. Lake & Joseph P. Gau, 2024. "Mapping human tissues with highly multiplexed RNA in situ hybridization," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    5. Rongbo Shen & Lin Liu & Zihan Wu & Ying Zhang & Zhiyuan Yuan & Junfu Guo & Fan Yang & Chao Zhang & Bichao Chen & Wanwan Feng & Chao Liu & Jing Guo & Guozhen Fan & Yong Zhang & Yuxiang Li & Xun Xu & Ji, 2022. "Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    6. Ian Covert & Rohan Gala & Tim Wang & Karel Svoboda & Uygar Sümbül & Su-In Lee, 2023. "Predictive and robust gene selection for spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Ziqi Zhang & Haoran Sun & Ragunathan Mariappan & Xi Chen & Xinyu Chen & Mika S. Jain & Mirjana Efremova & Sarah A. Teichmann & Vaibhav Rajan & Xiuwei Zhang, 2023. "scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    8. Jules Samaran & Gabriel Peyré & Laura Cantini, 2024. "scConfluence: single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    9. Gennady Gorin & John J. Vastola & Meichen Fang & Lior Pachter, 2022. "Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    10. Ying Lei & Mengnan Cheng & Zihao Li & Zhenkun Zhuang & Liang Wu & Yunong sun & Lei Han & Zhihao Huang & Yuzhou Wang & Zifei Wang & Liqin Xu & Yue Yuan & Shang Liu & Taotao Pan & Jiarui Xie & Chuanyu L, 2022. "Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    11. Wendy Xueyi Wang & Julie L. Lefebvre, 2022. "Morphological pseudotime ordering and fate mapping reveal diversification of cerebellar inhibitory interneurons," Nature Communications, Nature, vol. 13(1), pages 1-21, December.

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