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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. 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.}
@article{item_3164698, title = {{A community-based transcriptomics classification and nomenclature of neocortical cell types}}, journal = {{Nature Neuroscience}}, 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.}}, volume = {23}, number = {12}, pages = {1456--1468}, publisher = {Nature America Inc.}, address = {New York, NY}, year = {2020}, slug = {item_3164698}, author = {Yuste, R and Hawrylycz, M and Aalling, N and Arendt, D and Armananzas, R and Ascoli, G and Bielza, C and Bokharaie, VS and Bergmann, T and Bystron, I and Capogna, M and Chang, Y and Clemens, A and de Kock, C and DeFelipe, J and Dos Santos, S and Dunville, K and Feldmeyer, D and Fi{\'a}th, R and Fishell, G and Foggetti, A and Gao, X and Ghaderi, P and G{\"u}nt{\"u}rk{\"u}n, O and Hall, VJ and Helmstaedter, M and Herculano-Houzel, S and Hilscher, M and Hirase, H and Hjerling-Leffler, J and Hodge, R and Huang, ZJ and Huda, R and Juan, Y and Khodosevich, K and Kiehn, O and Koch, H and Kuebler, E and K{\"u}hnemund, M and Larra{\textasciitilde n}aga, P and Lelieveldt, D and Louth, EL and Lui, J and Mansvelder, H and Marin, O and Mart{\'\i}nez-Trujillo, J and Moradi, H and Goriounova, N and Mohapatra, A and Nedergaard, M and N{\v{e}}mec, P and Ofer, N and Pfisterer, U and Pontes, S and Redmond, W and Rossier, J and Sanes, J and Scheuermann, R and Serrano Saiz, E and Somogyi, P and Tam{\'a}s, G and Tolias, A and Tosches, M and Turrero Garcia, M and Aguilar-Valles, A and Munguba, H and Wozny, C and Wuttke, T and Yong, L and Zeng, H and Lein, ES} }