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

• Published in: Nature neuroscience Vol. 23; no. 12; pp. 1456 - 1468
• Main Authors: Yuste, Rafael, Hawrylycz, Michael, Aalling, Nadia, Aguilar-Valles, Argel, Arendt, Detlev, Armañanzas, Ruben, Ascoli, Giorgio A., Bielza, Concha, Bokharaie, Vahid, Bergmann, Tobias Borgtoft, Bystron, Irina, Capogna, Marco, Chang, YoonJeung, Clemens, Ann, de Kock, Christiaan P. J., DeFelipe, Javier, Dos Santos, Sandra Esmeralda, Dunville, Keagan, Feldmeyer, Dirk, Fiáth, Richárd, Fishell, Gordon James, Foggetti, Angelica, Gao, Xuefan, Ghaderi, Parviz, Goriounova, Natalia A., Güntürkün, Onur, Hagihara, Kenta, Hall, Vanessa Jane, Helmstaedter, Moritz, Herculano-Houzel, Suzana, Hilscher, Markus M., Hirase, Hajime, Hjerling-Leffler, Jens, Hodge, Rebecca, Huang, Josh, Huda, Rafiq, Khodosevich, Konstantin, Kiehn, Ole, Koch, Henner, Kuebler, Eric S., Kühnemund, Malte, Larrañaga, Pedro, Lelieveldt, Boudewijn, Louth, Emma Louise, Lui, Jan H., Mansvelder, Huibert D., Marin, Oscar, Martinez-Trujillo, Julio, Chameh, Homeira Moradi, Mohapatra, Alok Nath, Munguba, Hermany, Nedergaard, Maiken, Němec, Pavel, Ofer, Netanel, Pfisterer, Ulrich Gottfried, Pontes, Samuel, Redmond, William, Rossier, Jean, Sanes, Joshua R., Scheuermann, Richard H., Serrano-Saiz, Esther, Staiger, Jochen F., Somogyi, Peter, Tamás, Gábor, Tolias, Andreas Savas, Tosches, Maria Antonietta, García, Miguel Turrero, Wozny, Christian, Wuttke, Thomas V., Liu, Yong, Yuan, Juan, Zeng, Hongkui, Lein, Ed
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.12.2020; Nature Publishing Group
• Subjects: 38/39; 631/378/2583; 631/378/3920; Agglomeration; Animal Genetics and Genomics; Animals; Behavioral Sciences; Biological diversity; Biological Techniques; Biomedical and Life Sciences; Biomedicine; Brain research; Cells - classification; Cerebral cortex; Circuits; Classification; Comment; Computational Biology; Data management; Developmental stages; Gene expression; Genetic aspects; Genetic transcription; Glial cells; Humans; Knowledge bases (artificial intelligence); Neocortex; Neocortex - cytology; Neural circuitry; Neurobiology; Neuroglia - classification; Neuronal-glial interactions; Neurons - classification; Neurosciences; Observations; Physiology; Single-Cell Analysis; Species classification; Statistical analysis; Taxonomy; Terminology as Topic; Transcriptome; United States
• ISSN: 1097-6256; 1546-1726; 1546-1726
• DOI: 10.1038/s41593-020-0685-8

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.