The recent advent of single cell RNA-sequencing and other high-throughput technologies has led to an explosion of cell type definitions across URLan systems. Consortia like the BRAIN Initiative Cell Census Network (BICCN) and the Human Cell Atlas (HCA) have begun to standardize and centralize the intake of data and associated metadata from these projects; however, the naming URLanization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies derived from overlapping datasets . To facilitate cross-dataset alignment and comparison, we created a working nomenclature convention for matching and tracking cell types across studies . The convention is qualitatively similar to how gene transcripts are tracked across different versions of GENCODE genome builds, allowing comparison of matched types with a common reference or any other taxonomy. It augments but does not change existing cell type names provided in original publications and can be directly applied to data from new or published studies. We applied this nomenclature to a series of published cell type data from the Allen Institute, highlighting its extensibility to classifications defined using multiple quantifiable modalities. Finally, we propose conventions for assigning accurate yet flexible cell types names in the mammalian cortex as a step towards a much larger community-wide effort URLanize multi-source, data-driven information related to cell types. The convention presented here can be easily applied to cell type taxonomies from URLan system URLanism.
The advancement of single cell RNA-sequencing technologies has led to an explosion of cell type definitions across URLans URLanisms. While standards for data and metadata intake are URLanization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies . To facilitate cross-dataset comparison, the Allen Institute created the Common Cell type Nomenclature (CCN) for matching and tracking cell types across studies that is qualitatively similar to gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from multiple quantifiable modalities. The CCN facilitates assigning accurate yet flexible cell type names in the mammalian cortex as a step towards community-wide efforts URLanize multi-source, data-driven information related to cell type taxonomies from URLanism.
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URLan systems. Consortia like the BRAIN Initiative Cell Census Network (BICCN) and the Human Cell Atlas (HCA) have begun to standardize and centralize the intake of data and associated metadata from these projects; however, the naming
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derived from overlapping datasets
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how gene transcripts are tracked across different versions of GENCODE genome builds, allowing comparison of matched types with a common reference or any other taxonomy. It augments but does not change existing cell type names provided in original publications and can be directly applied to data from new or published studies. We applied this nomenclature to a series of published cell type data from the Allen Institute, highlighting its extensibility to classifications defined using
gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from
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