PT  - JOURNAL ARTICLE
AU  - Pennitz, Peter
AU  - Kirsten, Holger
AU  - Friedrich, Vincent D.
AU  - Wyler, Emanuel
AU  - Goekeri, Cengiz
AU  - Obermayer, Benedikt
AU  - Heinz, Gitta A.
AU  - Mashreghi, Mir-Farzin
AU  - Büttner, Maren
AU  - Trimpert, Jakob
AU  - Landthaler, Markus
AU  - Suttorp, Norbert
AU  - Hocke, Andreas C.
AU  - Hippenstiel, Stefan
AU  - Tönnies, Mario
AU  - Scholz, Markus
AU  - Kuebler, Wolfgang M.
AU  - Witzenrath, Martin
AU  - Hoenzke, Katja
AU  - Nouailles, Geraldine
TI  - A pulmonologist's guide to perform and analyse cross-species single lung cell transcriptomics
AID  - 10.1183/16000617.0056-2022
DP  - 2022 Sep 30
TA  - European Respiratory Review
PG  - 220056
VI  - 31
IP  - 165
4099  - http://err.ersjournals.com/content/31/165/220056.short
4100  - http://err.ersjournals.com/content/31/165/220056.full
SO  - EUROPEAN RESPIRATORY REVIEW2022 Sep 30; 31
AB  - 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 We compared the single-cell transcriptomes from publicly available and unpublished datasets of the lungs in six different species: human (Homo sapiens), African green monkey (Chlorocebus sabaeus), pig (Sus domesticus), hamster (Mesocricetus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