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

Research output: Contribution to journalJournal articleResearchpeer-review

  • Rafael Yuste
  • Michael Hawrylycz
  • 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 Fiáth
  • Gordon James Fishell
  • Angelica Foggetti
  • Xuefan Gao
  • Parviz Ghaderi
  • Natalia A. Goriounova
  • Onur Güntürkün
  • Kenta Hagihara
  • Moritz Helmstaedter
  • Suzana Herculano
  • Markus M. Hilscher
  • Jens Hjerling-leffler
  • Rebecca Hodge
  • Josh Huang
  • Rafiq Huda
  • 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
  • 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
  • Hermany Munguba Vieira
  • Christian Wozny
  • Thomas V. Wuttke
  • Liu Yong
  • Juan Yuan
  • Hongkui Zeng
  • Ed Lein
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
Pages (from-to)1456-1468
Number of pages13
Publication statusPublished - 2020

Number of downloads are based on statistics from Google Scholar and www.ku.dk

No data available

ID: 247388258