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

Peter PennitzHolger KirstenVincent D FriedrichEmanuel WylerCengiz GoekeriBenedikt ObermayerGitta A HeinzMir-Farzin MashreghiMaren BüttnerJakob TrimpertMarkus LandthalerNorbert SuttorpAndreas C HockeStefan HippenstielMario TönniesMarkus ScholzWolfgang M KueblerMartin WitzenrathKatja HoenzkeGeraldine Nouailles
Published in: European respiratory review : an official journal of the European Respiratory Society (2022)
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 We compared the single-cell transcriptomes from publicly available and unpublished datasets of the lungs in six different species: human ( Homo sapiens ), African green monkey ( Chlorocebus sabaeus ), pig ( Sus domesticus ), hamster (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.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • endothelial cells
  • machine learning
  • poor prognosis
  • cell therapy
  • patient safety
  • genetic diversity
  • signaling pathway
  • quality improvement
  • cell death
  • genome wide identification