IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v598y2021i7879d10.1038_s41586-021-03705-x.html
   My bibliography  Save this article

Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH

Author

Listed:
  • Meng Zhang

    (Harvard University
    Harvard University
    Harvard University)

  • Stephen W. Eichhorn

    (Harvard University
    Harvard University
    Harvard University)

  • Brian Zingg

    (University of Southern California
    University of California)

  • Zizhen Yao

    (Allen Institute for Brain Science)

  • Kaelan Cotter

    (University of Southern California
    University of California)

  • Hongkui Zeng

    (Allen Institute for Brain Science)

  • Hongwei Dong

    (University of Southern California
    University of California)

  • Xiaowei Zhuang

    (Harvard University
    Harvard University
    Harvard University)

Abstract

A mammalian brain is composed of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing allows systematic identification of cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1,2. Single-cell epigenomic profiling3,4 further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation. Here we used a single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)5, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. We profiled approximately 300,000 cells in the mouse primary motor cortex and its adjacent areas, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory but also most inhibitory neuronal clusters adopted laminar organizations. Intratelencephalic neurons formed a largely continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Furthermore, we integrated MERFISH with retrograde labelling to probe projection targets of neurons of the mouse primary motor cortex and found that their cortical projections formed a complex network in which individual neuronal clusters project to multiple target regions and individual target regions receive inputs from multiple neuronal clusters.

Suggested Citation

  • Meng Zhang & Stephen W. Eichhorn & Brian Zingg & Zizhen Yao & Kaelan Cotter & Hongkui Zeng & Hongwei Dong & Xiaowei Zhuang, 2021. "Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH," Nature, Nature, vol. 598(7879), pages 137-143, October.
  • Handle: RePEc:nat:nature:v:598:y:2021:i:7879:d:10.1038_s41586-021-03705-x
    DOI: 10.1038/s41586-021-03705-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-021-03705-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/s41586-021-03705-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhiyuan Liu & Dafei Wu & Weiwei Zhai & Liang Ma, 2023. "SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Xinrui Zhou & Wan Yi Seow & Norbert Ha & Teh How Cheng & Lingfan Jiang & Jeeranan Boonruangkan & Jolene Jie Lin Goh & Shyam Prabhakar & Nigel Chou & Kok Hao Chen, 2024. "Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. 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.
    4. Yann Vanrobaeys & Utsav Mukherjee & Lucy Langmack & Stacy E. Beyer & Ethan Bahl & Li-Chun Lin & Jacob J. Michaelson & Ted Abel & Snehajyoti Chatterjee, 2023. "Mapping the spatial transcriptomic signature of the hippocampus during memory consolidation," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    5. 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.
    6. Jongsu Choi & Jin Li & Salma Ferdous & Qingnan Liang & Jeffrey R. Moffitt & Rui Chen, 2023. "Spatial organization of the mouse retina at single cell resolution by MERFISH," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    7. Zhiyuan Yuan, 2024. "MENDER: fast and scalable tissue structure identification in spatial omics data," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    8. Stefano Nardone & Roberto Luca & Antonino Zito & Nataliya Klymko & Dimitris Nicoloutsopoulos & Oren Amsalem & Cory Brannigan & Jon M. Resch & Christopher L. Jacobs & Deepti Pant & Molly Veregge & Hari, 2024. "A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    9. Nelson Johansen & Hongru Hu & Gerald Quon, 2023. "Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    10. Peter Androvic & Martina Schifferer & Katrin Perez Anderson & Ludovico Cantuti-Castelvetri & Hanyi Jiang & Hao Ji & Lu Liu & Garyfallia Gouna & Stefan A. Berghoff & Simon Besson-Girard & Johanna Knofe, 2023. "Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    11. 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.
    12. Wei Zhao & Kevin G. Johnston & Honglei Ren & Xiangmin Xu & Qing Nie, 2023. "Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    13. Hao Xu & Shuyan Wang & Minghao Fang & Songwen Luo & Chunpeng Chen & Siyuan Wan & Rirui Wang & Meifang Tang & Tian Xue & Bin Li & Jun Lin & Kun Qu, 2023. "SPACEL: deep learning-based characterization of spatial transcriptome architectures," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    14. Zhiyuan Yuan & Yisi Li & Minglei Shi & Fan Yang & Juntao Gao & Jianhua Yao & Michael Q. Zhang, 2022. "SOTIP is a versatile method for microenvironment modeling with spatial omics data," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    15. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:598:y:2021:i:7879:d:10.1038_s41586-021-03705-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.