Marco Capogna

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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperComment/debateResearchpeer-review

  • Rafael Yuste, Columbia University
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  • Michael Hawrylycz, Allen Institute for Brain Science
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  • Nadia Aalling, University of Copenhagen
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  • Argel Aguilar-Valles, Carleton University
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  • Detlev Arendt, European Molecular Biology Laboratory
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  • Ruben Armananzas Arnedillo, George Mason University
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  • Giorgio A. Ascoli, George Mason University
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  • Concha Bielza, Technical University of Madrid
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  • Vahid Bokharaie, Max Planck Institute
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  • Tobias Borgtoft Bergmann, University of Copenhagen
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  • Irina Bystron, University of Oxford
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  • Marco Capogna
  • Yoonjeung Chang, Harvard University
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  • Ann Clemens, University of Edinburgh
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  • Christiaan P.J. de Kock, Vrije Universiteit Amsterdam
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  • Javier DeFelipe, CSIC - Cajal Institute
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  • Sandra Esmeralda Dos Santos, Vanderbilt University
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  • Keagan Dunville, Scuola Normale Superiore di Pisa
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  • Dirk Feldmeyer, JARA-Brain Institute of Neuroscience and Medicine
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  • Richárd Fiáth, Research Centre for Natural Sciences
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  • Gordon James Fishell, Harvard University
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  • Angelica Foggetti, Kiel University
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  • Xuefan Gao, European Molecular Biology Laboratory
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  • Parviz Ghaderi, Swiss Federal Institute of Technology Lausanne
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  • Natalia A. Goriounova, Vrije Universiteit Amsterdam
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  • Onur Güntürkün, Ruhr University Bochum
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  • Kenta Hagihara, Novartis
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  • Vanessa Jane Hall, University of Copenhagen
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  • Moritz Helmstaedter, Max Planck Institute for Brain Research
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  • Suzana Herculano, Vanderbilt University
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  • Markus M. Hilscher, Karolinska Institutet
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  • Hajime Hirase, RIKEN
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  • Jens Hjerling-Leffler, Karolinska Institutet
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  • Rebecca Hodge, Allen Institute for Brain Science
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  • Josh Huang, Cold Spring Harbor Laboratory
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  • Rafiq Huda, Massachusetts Institute of Technology
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  • Konstantin Khodosevich, University of Copenhagen
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  • Ole Kiehn, University of Copenhagen
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  • Henner Koch, RWTH Aachen University
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  • Eric S. Kuebler, Western University
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  • Malte Kühnemund, CARTANA
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  • Pedro Larrañaga, Technical University of Madrid
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  • Boudewijn Lelieveldt, Leiden University
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  • Emma Louise Louth
  • Jan H. Lui, Stanford University
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  • Huibert D. Mansvelder, Vrije Universiteit Amsterdam
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  • Oscar Marin, King's College London
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  • Julio Martinez-Trujillo, Western University
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  • Homeira Moradi Chameh, Krembil Research Institute
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  • Alok Nath, University of Haifa
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  • Maiken Nedergaard, University of Rochester
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  • Pavel Němec, Charles University
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  • Netanel Ofer, Bar-Ilan University
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  • Ulrich Gottfried Pfisterer, University of Copenhagen
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  • Samuel Pontes, Columbia University
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  • William Redmond, Macquarie University
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  • Jean Rossier, Sarbonne University
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  • Joshua R. Sanes, Harvard University
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  • Richard Scheuermann, J. Craig Venter Institute
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  • Esther Serrano-Saiz, CSIC-UAM - Centre for Molecular Biology "Severo Ochoa"
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  • Jochen F. Steiger, University of Göttingen
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  • Peter Somogyi, University of Oxford
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  • Gábor Tamás, University of Szeged
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  • Andreas Savas Tolias, Baylor College of Medicine
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  • Maria Antonietta Tosches, Columbia University
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  • Miguel Turrero García, Harvard University
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  • Hermany Munguba Vieira, Karolinska Institutet
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  • Christian Wozny, University of Strathclyde
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  • Thomas V. Wuttke, RWTH Aachen University
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  • Liu Yong, New York University
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  • Juan Yuan, Karolinska Institutet
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  • Hongkui Zeng, Allen Institute for Brain Science
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  • Ed Lein, Allen Institute for Brain Science

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
JournalNature Neuroscience
Volume23
Issue12
Pages (from-to)1456-1468
Number of pages13
ISSN1097-6256
DOIs
Publication statusPublished - Dec 2020

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