Publikationsdatenbank

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

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Pennitz, Peter

Kirsten, Holger

Friedrich, Vincent D.

Wyler, Emanuel

Goekeri, Cengiz

Obermayer, Benedikt

Heinz, Gitta A.

Mashreghi, Mir-Farzin

Büttner, Maren

Trimpert, Jakob (WE 5)

Landthaler, Markus

Suttorp, Norbert

Hocke, Andreas C.

Hippenstiel, Stefan

Tönnies, Mario

Scholz, Markus

Kuebler, Wolfgang M.

Witzenrath, Martin

Hoenzke, Katja

Nouailles, Geraldine

Quelle

European respiratory review

Bandzählung: 31

Heftzählung: 165

Seiten: Artikel 220056

ISSN: 1600-0617

Sprache

Englisch

Verweise

URL (Volltext): https://err.ersjournals.com/content/31/165/220056

DOI: 10.1183/16000617.0056-2022

Pubmed: 35896273

Kontakt

Institut für Virologie

Robert-von-Ostertag-Str. 7-13
14163 Berlin
+49 30 838 51833
virologie@vetmed.fu-berlin.de

Abstract / Zusammenfassung

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 We compared the single-cell transcriptomes from publicly available and unpublished datasets of the lungs in six different species: human (Homo sapiens), African green monkey (Chlorocebus sabaeus), pig (Sus domesticus), hamster (Mesocricetus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.