Physiology and Pharmacology Publications


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


Rafael Yuste, Columbia University in the City of New York
Michael Hawrylycz, Allen Institute for Brain Science
Nadia Aalling, Københavns Universitet
Argel Aguilar-Valles, Carleton University
Detlev Arendt, European Molecular Biology Laboratory Heidelberg
Ruben Armananzas Arnedillo, George Mason University, Fairfax Campus
Giorgio A. Ascoli, George Mason University, Fairfax Campus
Concha Bielza, Universidad Politécnica de Madrid
Vahid Bokharaie, Max Planck Institute
Tobias Borgtoft Bergmann, Københavns Universitet
Irina Bystron, University of Oxford
Marco Capogna, Aarhus Universitet
Yoonjeung Chang, Harvard Medical School
Ann Clemens, The University of Edinburgh
Christiaan P.J. de Kock, Vrije Universiteit Amsterdam
Javier DeFelipe, CSIC - Instituto Cajal (IC)
Sandra Esmeralda Dos Santos, Vanderbilt University
Keagan Dunville, Scuola Normale Superiore di Pisa
Dirk Feldmeyer, Institute of Neuroscience and Medicine
Richárd Fiáth, Research Centre for Natural Sciences
Gordon James Fishell, Harvard Medical School
Angelica Foggetti, Christian-Albrechts-Universität zu Kiel
Xuefan Gao, European Molecular Biology Laboratory Hamburg
Parviz Ghaderi, Ecole Polytechnique Fédérale de Lausanne
Natalia A. Goriounova, Vrije Universiteit Amsterdam
Onur Güntürkün, Ruhr-Universitat Bochum
Kenta Hagihara, Friedrich Miescher Institute for Biomedical Research
Vanessa Jane Hall, Københavns Universitet
Moritz Helmstaedter, Max Planck Institute for Brain Research
Suzana Herculano, Vanderbilt University
Markus M. Hilscher, Karolinska Institutet
Hajime Hirase, RIKEN Center for Brain Science

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Nature Neuroscience





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© 2020, The Author(s). 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.

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