%0 Journal Article %1 Pennitz2022-iu %A Pennitz, Peter %A Kirsten, Holger %A Friedrich, Vincent D %A Wyler, Emanuel %A Goekeri, Cengiz %A Obermayer, Benedikt %A Heinz, Gitta A %A Mashreghi, Mir-Farzin %A Büttner, Maren %A Trimpert, Jakob %A Landthaler, Markus %A Suttorp, Norbert %A Hocke, Andreas C %A Hippenstiel, Stefan %A Tönnies, Mario %A Scholz, Markus %A Kuebler, Wolfgang M %A Witzenrath, Martin %A Hoenzke, Katja %A Nouailles, Geraldine %D 2022 %I European Respiratory Society (ERS) %J Eur. Respir. Rev. %K %N 165 %P 220056 %T A pulmonologist's guide to perform and analyse cross-species single lung cell transcriptomics %V 31 %X 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 We compared the single-cell transcriptomes from publicly available and unpublished datasets of the lungs in six different species: human (Homo sapiens), African green monkey (Chlorocebus sabaeus), pig (Sus domesticus), hamster (Mesocricetus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.

@article{Pennitz2022-iu, 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 We compared the single-cell transcriptomes from publicly available and unpublished datasets of the lungs in six different species: human (Homo sapiens), African green monkey (Chlorocebus sabaeus), pig (Sus domesticus), hamster (Mesocricetus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.}, added-at = {2024-09-10T11:56:37.000+0200}, author = {Pennitz, Peter and Kirsten, Holger and Friedrich, Vincent D and Wyler, Emanuel and Goekeri, Cengiz and Obermayer, Benedikt and Heinz, Gitta A and Mashreghi, Mir-Farzin and B{\"u}ttner, Maren and Trimpert, Jakob and Landthaler, Markus and Suttorp, Norbert and Hocke, Andreas C and Hippenstiel, Stefan and T{\"o}nnies, Mario and Scholz, Markus and Kuebler, Wolfgang M and Witzenrath, Martin and Hoenzke, Katja and Nouailles, Geraldine}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/217f1f4948ab7b7367cdbed2ba96457ea/scadsfct}, copyright = {http://creativecommons.org/licenses/by-nc/4.0/}, interhash = {9bce80bb731662046fd03925e2eba392}, intrahash = {17f1f4948ab7b7367cdbed2ba96457ea}, journal = {Eur. Respir. Rev.}, keywords = {}, language = {en}, month = sep, number = 165, pages = 220056, publisher = {European Respiratory Society (ERS)}, timestamp = {2024-09-10T15:15:57.000+0200}, title = {A pulmonologist's guide to perform and analyse cross-species single lung cell transcriptomics}, volume = 31, year = 2022 }