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

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          Abstract

          To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological features of neurons and glia, while productive, have not resulted in a unified taxonomy of neural cell types. The recent development of single-cell transcriptomics has enabled, for the first time, systematic high-throughput profiling of large numbers of cortical cells and the generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data have revealed the existence of clear clusters, many of which correspond to cell types defined by traditional criteria, and which are conserved across cortical areas and species. To capitalize on these innovations and advance the field, we, the Copenhagen Convention Group, propose the community adopts a transcriptome-based taxonomy of the cell types in the adult mammalian neocortex. This core classification should be ontological, hierarchical and use a standardized nomenclature. It should be configured to flexibly incorporate new data from multiple approaches, developmental stages and a growing number of species, enabling improvement and revision of the classification. This community-based strategy could serve as a common foundation for future detailed analysis and reverse engineering of cortical circuits and serve as an example for cell type classification in other parts of the nervous system and other organs.

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            Developmental diversification of cortical inhibitory interneurons

            Summary Diverse subsets of cortical interneurons play vital roles in higher-order brain functions. To investigate how this diversity is generated, we used single cell RNA-seq to profile the transcriptomes of murine cells collected along a developmental timecourse. Heterogeneity within mitotic progenitors in the ganglionic eminences is driven by a highly conserved maturation trajectory, alongside eminence-specific transcription factor expression that seeds the emergence of later diversity. Upon becoming postmitotic, progenitors diverge and differentiate into transcriptionally distinct states, including an interneuron precursor state. By integrating datasets across developmental timepoints, we identified shared sources of transcriptomic heterogeneity between adult interneurons and their precursors, revealing the embryonic emergence of interneuron cardinal subtypes. Our analysis revealed that the ASD-associated transcription factor Mef2c delineates early Pvalb-precursors, and is essential for their development. These findings shed new light on the molecular diversification of early inhibitory precursors, and identify gene modules that may influence the specification of human subtypes.
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              Cell ontology in an age of data-driven cell classification

              Background Data-driven cell classification is becoming common and is now being implemented on a massive scale by projects such as the Human Cell Atlas. The scale of these efforts poses a challenge. How can the results be made searchable and accessible to biologists in general? How can they be related back to the rich classical knowledge of cell-types, anatomy and development? How will data from the various types of single cell analysis be made cross-searchable? Structured annotation with ontology terms provides a potential solution to these problems. In turn, there is great potential for using the outputs of data-driven cell classification to structure ontologies and integrate them with data-driven cell query systems. Results Focusing on examples from the mouse retina and Drosophila olfactory system, I present worked examples illustrating how formalization of cell ontologies can enhance querying of data-driven cell-classifications and how ontologies can be extended by integrating the outputs of data-driven cell classifications. Conclusions Annotation with ontology terms can play an important role in making data driven classifications searchable and query-able, but fulfilling this potential requires standardized formal patterns for structuring ontologies and annotations and for linking ontologies to the outputs of data-driven classification.
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                Author and article information

                Journal
                06 September 2019
                Article
                1909.03083
                b220aa2c-e46c-4887-bbd2-ed452ac863fa

                http://creativecommons.org/licenses/by/4.0/

                History
                Custom metadata
                21 pages, 3 figures
                q-bio.GN q-bio.NC

                Neurosciences,Genetics
                Neurosciences, Genetics

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