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Gottfried", "initials": "UG"}, {"family": "Pontes", "given": "Samuel", "initials": "S", "orcid": "0000-0002-4061-2365", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9aff30873ebf40179a15edab0031d19f.json"}}, {"family": "Redmond", "given": "William", "initials": "W", "orcid": "0000-0002-6250-1594", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f4a7763c683f4ddd986c3cceb3ee546a.json"}}, {"family": "Rossier", "given": "Jean", "initials": "J"}, {"family": "Sanes", "given": "Joshua R", "initials": "JR", "orcid": "0000-0001-8926-8836", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/1e69f10f8f7849168bea8aa89ccfb2b3.json"}}, {"family": "Scheuermann", "given": "Richard H", "initials": "RH", "orcid": "0000-0003-1355-892X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/35f14f67fd6d4bfbbc1af68c51dd7a28.json"}}, {"family": "Serrano-Saiz", "given": "Esther", 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"https://publications-affiliated.scilifelab.se/researcher/1af126dd218d4f61a12a477218307524.json"}}, {"family": "Wuttke", "given": "Thomas V", "initials": "TV", "orcid": "0000-0001-5655-8490", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c57ed174ace647a0acd30135c84a6ac4.json"}}, {"family": "Liu", "given": "Yong", "initials": "Y", "orcid": "0000-0002-0574-2877", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/231c5724b4a94545baa98beda2f4a0d9.json"}}, {"family": "Yuan", "given": "Juan", "initials": "J"}, {"family": "Zeng", "given": "Hongkui", "initials": "H", "orcid": "0000-0002-0326-5878", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cd9d8433d3ba4e8b83dda69a4355d02e.json"}}, {"family": "Lein", "given": "Ed", "initials": "E", "orcid": "0000-0001-9012-6552", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/667d53a701f24f339b9d8eec3eaaef1d.json"}}], "type": "journal article", "published": "2020-12-00", "journal": {"title": "Nat. Neurosci.", "issn": "1546-1726", "volume": "23", "issue": "12", "pages": "1456-1468", "issn-l": "1097-6256"}, "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.", "doi": "10.1038/s41593-020-0685-8", "pmid": "32839617", "labels": [], "xrefs": [{"db": "mid", "key": "NIHMS1627961"}, {"db": "pmc", "key": "PMC7683348"}, {"db": "pii", "key": "10.1038/s41593-020-0685-8"}], "notes": [], "created": "2026-09-23T11:44:30.116Z", "modified": "2026-09-23T11:44:31.236Z"}, {"entity": "publication", "iuid": "5035dbe7ef7f48f79e31ce93de46b5de", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/5035dbe7ef7f48f79e31ce93de46b5de.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/5035dbe7ef7f48f79e31ce93de46b5de"}}, "title": "Probabilistic cell typing enables fine mapping of closely related cell types in situ.", "authors": [{"family": "Qian", "given": "Xiaoyan", "initials": "X", "orcid": "0000-0001-7509-8071", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/37300f2654d74a5c9b81dd287d47768a.json"}}, {"family": "Harris", "given": "Kenneth D", "initials": "KD", "orcid": "0000-0002-5930-6456", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/07ebd4c89d40490d9d0302345b66fa3b.json"}}, {"family": "Hauling", "given": "Thomas", "initials": "T"}, {"family": "Nicoloutsopoulos", "given": "Dimitris", "initials": "D"}, {"family": "Mu\u00f1oz-Manchado", "given": "Ana B", "initials": "AB"}, {"family": "Skene", "given": "Nathan", "initials": "N", "orcid": "0000-0002-6807-3180", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2e9bf4dcea414273964e009ae9bf6c1d.json"}}, {"family": "Hjerling-Leffler", "given": "Jens", "initials": "J", "orcid": "0000-0002-4539-1776", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0bd7ee6587514ccfbfbee9d02bb508b6.json"}}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3311155f09a64b4f88d673da2a40627b.json"}}], "type": "journal article", "published": "2020-01-00", "journal": {"title": "Nat. Methods", "issn": "1548-7105", "volume": "17", "issue": "1", "pages": "101-106", "issn-l": "1548-7091"}, "abstract": "Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive previous work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues.", "doi": "10.1038/s41592-019-0631-4", "pmid": "31740815", "labels": [], "xrefs": [{"db": "mid", "key": "EMS84576"}, {"db": "pmc", "key": "PMC6949128"}, {"db": "pii", "key": "10.1038/s41592-019-0631-4"}], "notes": [], "created": "2026-09-23T06:39:00.920Z", "modified": "2026-09-23T06:39:01.046Z"}]}