A community-based transcriptomics classification and nomenclature of neocortical cell types
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. Si...
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| Published in: | Nature neuroscience Vol. 23; no. 12; pp. 1456 - 1468 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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New York
Nature Publishing Group US
01-12-2020
Nature Publishing Group |
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| Online Access: | Get full text |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Güntürkün, Onur Clemens, Ann Arendt, Detlev DeFelipe, Javier Martinez-Trujillo, Julio Chameh, Homeira Moradi Huda, Rafiq Kuebler, Eric S. Huang, Josh Němec, Pavel Rossier, Jean Dos Santos, Sandra Esmeralda Chang, YoonJeung Bokharaie, Vahid Tamás, Gábor Pfisterer, Ulrich Gottfried Fiáth, Richárd Ofer, Netanel Capogna, Marco Redmond, William Staiger, Jochen F. Zeng, Hongkui Wozny, Christian Tosches, Maria Antonietta Hodge, Rebecca Mansvelder, Huibert D. Bystron, Irina Aguilar-Valles, Argel Pontes, Samuel Liu, Yong Ascoli, Giorgio A. Wuttke, Thomas V. Hall, Vanessa Jane Marin, Oscar Hjerling-Leffler, Jens Hirase, Hajime Goriounova, Natalia A. Hawrylycz, Michael García, Miguel Turrero Lein, Ed Hilscher, Markus M. Lui, Jan H. Herculano-Houzel, Suzana Munguba, Hermany Fishell, Gordon James Aalling, Nadia Feldmeyer, Dirk Khodosevich, Konstantin Tolias, Andreas Savas Foggetti, Angelica Nedergaard, Maiken Kiehn, Ole Koch, Henner Gao, Xuefan Helmstaedter, Moritz Bielza, Concha de Kock, Christiaan P. J. Dunville, Keagan Mohapatra, Alok Nath Scheuermann, |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32839617$$D View this record in MEDLINE/PubMed http://kipublications.ki.se/Default.aspx?queryparsed=id:144501182$$DView record from Swedish Publication Index |
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| Title | A community-based transcriptomics classification and nomenclature of neocortical cell types |
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