A community-based transcriptomics classification and nomenclature of neocortical cell types

Rafael Yuste*, Michael Hawrylycz, Nadia Aalling, Argel Aguilar-Valles, Detlev Arendt, Ruben Armananzas Arnedillo, Giorgio A. Ascoli, Concha Bielza, Vahid Bokharaie, Tobias Borgtoft Bergmann, Irina Bystron, Marco Capogna, Yoonjeung Chang, Ann Clemens, Christiaan P. J. de Kock, Javier DeFelipe, Sandra Esmeralda Dos Santos, Keagan Dunville, Dirk Feldmeyer, Richárd FiáthGordon James Fishell, Angelica Foggetti, Xuefan Gao, Parviz Ghaderi, Natalia A. Goriounova, Onur Güntürkün, Kenta Hagihara, Vanessa Jane Hall, Moritz Helmstaedter, Suzana Herculano, Markus M. Hilscher, Hajime Hirase, Jens Hjerling-Leffler, Rebecca Hodge, Josh Huang, Rafiq Huda, Konstantin Khodosevich, Ole Kiehn, Henner Koch, Eric S. Kuebler, Malte Kühnemund, Pedro Larrañaga, Boudewijn Lelieveldt, Emma Louise Louth, Jan H. Lui, Huibert D. Mansvelder, Oscar Marin, Julio Martinez-Trujillo, Homeira Moradi Chameh, Alok Nath Mohapatra, Hermany Munguba, Maiken Nedergaard, Pavel Němec, Netanel Ofer, Ulrich Gottfried Pfisterer, Samuel Pontes, William Redmond, Jean Rossier, Joshua R. Sanes, Richard Scheuermann, Esther Serrano-Saiz, Jochen F. Steiger, Peter Somogyi, Gábor Tamás, Andreas Savas Tolias, Maria Antonietta Tosches, Miguel Turrero García, Christian Wozny, Thomas V. Wuttke, Yong Liu, Juan Yuan, Hongkui Zeng, Ed Lein

*Corresponding author for this work

    Research output: Contribution to journalComment/opinionpeer-review

    139 Citations (Scopus)
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    To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

    Original languageEnglish
    Pages (from-to)1456-1468
    Number of pages13
    JournalNature Neuroscience
    Issue number12
    Publication statusPublished - Dec 2020

    Bibliographical note

    Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Author and Publisher corrections exist for this article, and have been included in the published pdf.
    The Author correction can be found at doi: 10.1038/s41593-020-00779-0
    and the Publisher correction can be found at doi: 10.1038/s41593-020-0685-8


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