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Phenotypic variation of transcriptomic cell types in mouse motor cortex

Author

Listed:
  • Federico Scala

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Dmitry Kobak

    (University of Tübingen)

  • Matteo Bernabucci

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Yves Bernaerts

    (University of Tübingen
    International Max Planck Research School for Intelligent Systems)

  • Cathryn René Cadwell

    (University of California San Francisco)

  • Jesus Ramon Castro

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Leonard Hartmanis

    (Karolinska Institutet)

  • Xiaolong Jiang

    (Baylor College of Medicine
    Baylor College of Medicine
    Jan and Dan Duncan Neurological Research Institute)

  • Sophie Laturnus

    (University of Tübingen)

  • Elanine Miranda

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Shalaka Mulherkar

    (Baylor College of Medicine)

  • Zheng Huan Tan

    (Baylor College of Medicine
    Baylor College of Medicine)

  • Zizhen Yao

    (Allen Institute for Brain Science)

  • Hongkui Zeng

    (Allen Institute for Brain Science)

  • Rickard Sandberg

    (Karolinska Institutet)

  • Philipp Berens

    (University of Tübingen
    University of Tübingen
    University of Tübingen
    University of Tübingen)

  • Andreas S. Tolias

    (Baylor College of Medicine
    Baylor College of Medicine)

Abstract

Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties1,2. Most existing neural taxonomies are based on either transcriptomic3,4 or morpho-electric5,6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells7. Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (those expressing Vip, Pvalb, Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neuronal types in the neocortex do not always form discrete entities. Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.

Suggested Citation

  • Federico Scala & Dmitry Kobak & Matteo Bernabucci & Yves Bernaerts & Cathryn René Cadwell & Jesus Ramon Castro & Leonard Hartmanis & Xiaolong Jiang & Sophie Laturnus & Elanine Miranda & Shalaka Mulher, 2021. "Phenotypic variation of transcriptomic cell types in mouse motor cortex," Nature, Nature, vol. 598(7879), pages 144-150, October.
  • Handle: RePEc:nat:nature:v:598:y:2021:i:7879:d:10.1038_s41586-020-2907-3
    DOI: 10.1038/s41586-020-2907-3
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    Citations

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    Cited by:

    1. Olga Gliko & Matt Mallory & Rachel Dalley & Rohan Gala & James Gornet & Hongkui Zeng & Staci A. Sorensen & Uygar Sümbül, 2024. "High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy," Nature Communications, Nature, vol. 15(1), pages 1-13, 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. Kiya W. Govek & Patrick Nicodemus & Yuxuan Lin & Jake Crawford & Artur B. Saturnino & Hannah Cui & Kristi Zoga & Michael P. Hart & Pablo G. Camara, 2023. "CAJAL enables analysis and integration of single-cell morphological data using metric geometry," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Oren Amsalem & Hidehiko Inagaki & Jianing Yu & Karel Svoboda & Ran Darshan, 2024. "Sub-threshold neuronal activity and the dynamical regime of cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, 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. 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.
    7. 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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