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The ability to analyse transcriptomes of multiple heterogeneous cell types in parallel is especially valuable for cell-focused lung research where a variety of resident and recruited cells are essential for maintaining organ functionality. We compared the single-cell transcriptomes from publicly available and unpublished datasets of the lungs in six different species: human (<jats:italic>Homo sapiens<\/jats:italic>), African green monkey (<jats:italic>Chlorocebus sabaeus<\/jats:italic>), pig (<jats:italic>Sus domesticus<\/jats:italic>), hamster (<jats:italic>Mesocricetus auratus<\/jats:italic>), rat (<jats:italic>Rattus norvegicus<\/jats:italic>) and mouse (<jats:italic>Mus musculus<\/jats:italic>) by employing RNA velocity and intercellular communication based on ligand\u2013receptor co-expression, among other techniques. Specifically, we demonstrated a workflow for interspecies data integration, applied a single unified gene nomenclature, performed cell-specific clustering and identified marker genes for each species. Overall, integrative approaches combining newly sequenced as well as publicly available datasets could help identify species-specific transcriptomic signatures in both healthy and diseased lung tissue and select appropriate models for future respiratory research.<\/jats:p>","DOI":"10.1183\/16000617.0056-2022","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T05:51:23Z","timestamp":1658987483000},"page":"220056","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["A pulmonologist's guide to perform and analyse cross-species single lung cell 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