A pulmonologist's guide to perform and analyse cross-species single lung cell transcriptomics. (27th July 2022)

• Record Type: Journal Article
• Authors: Pennitz, Peter; Kirsten, Holger; Friedrich, Vincent D.; Wyler, Emanuel; Goekeri, Cengiz; Obermayer, Benedikt; Heinz, Gitta A.; Mashreghi, Mir-Farzin; Büttner, Maren; Trimpert, Jakob; Landthaler, Markus; Suttorp, Norbert; Hocke, Andreas C.; Hippenstiel, Stefan; Tönnies, Mario; Scholz, Markus; Kuebler, Wolfgang M.; Witzenrath, Martin; Hoenzke, Katja; Nouailles, Geraldine
• Is Part Of: European respiratory review. Volume 31:Number 165(2022)
• Publication Date: 2022-07-27
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiratory Tract Diseases; Respiratory organs -- Diseases; Respiratory organs -- Diseases -- Treatment; Electronic journals; Periodical; Periodicals; Periodicals; 616.2
• DOI: 10.1183/16000617.0056-2022
• Languages: English
• ISSNs: 0905-9180

Abstract

Single-cell ribonucleic acid sequencing is becoming widely employed to study biological processes at a novel resolution depth. 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 We compared the single-cell transcriptomes from publicly available and unpublished datasets of the lungs in six different species: human ( Homo sapiens ), African green monkey ( Chlorocebus sabaeus ), pig ( Sus domesticus ), hamster ( Mesocricetus auratus ), rat ( Rattus norvegicus ) and mouse ( Mus musculus ) by employing RNA velocity and intercellular communication based on ligand–receptor 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. The COVID-19 pandemic led to an increase in publicly available single-cell RNA sequencing data. This review provides an up-to-date framework and readily adoptable tools to measure such data in lungs and compare it with existing data across species. https://bit.ly/3wHCoHe