Recently, the LungMAP consortium produced a LungMAP CellCards 10, a rigorous catalog of lung cells based on a community-wide effort that synthesizes current functional and single-cell data from human and mouse lungs into a comprehensive and practical cellular census of lung cells. Therefore, single-cell references with comprehensive cell types, functionally validated cell identities, and standardized nomenclature are much needed by the research community to optimize automated cell type annotation and facilitate data integration, sharing, and collaboration.Ī growing number of community-wide efforts have been devoted to the development of common cell type nomenclature, including cell type ontologies of the Human Cell Atlas 8 and mammalian brain 9. The lack of common cell type nomenclatures and guidelines for single cell transcriptomic studies also creates substantial technical challenges for data integration and comparison. Common issues related with the use of a published scRNA-seq dataset as a reference for supervised classification of user-supplied datasets include the lack of comprehension (missing cell types), inclusion of speculative cell types/states that have not been functionally validated, technology specific-biases in the reference or query, and insufficient power to represent the repertoire of common healthy lung cell types. With the increasing number of published scRNA-seq datasets and the release of large-scale cell atlases, advanced computational tools 5, 6, 7 have been developed using annotated datasets to predict cell identities in new datasets. Accurate cell type identification is a necessary step in single-cell data analysis that usually requires time-consuming processes to optimize computational parameters followed by manual inspection that requires domain expertise. Single-cell RNA-seq (scRNA-seq) analysis is being widely applied in biomedical research, enabling the study of complex organs, such as the lung, at unprecedented scale and resolution, and transforming our understanding of organ development and disease 1, 2, 3, 4. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. ![]() Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Nature Communications volume 14, Article number: 4566 ( 2023)Īccurate cell type identification is a key and rate-limiting step in single-cell data analysis. Guided construction of single cell reference for human and mouse lung
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