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

Area of Science:

• Comparative genomics
• Pulmonary research
• Single-cell transcriptomics

Background:

• Single-cell RNA sequencing (scRNA-seq) offers unprecedented resolution for biological process analysis.
• Analyzing heterogeneous cell transcriptomes in parallel is crucial for lung research, involving diverse resident and recruited cells vital for organ function.

Purpose of the Study:

• To compare single-cell lung transcriptomes across six species (human, monkey, pig, hamster, rat, mouse).
• To develop and apply a workflow for interspecies data integration and analysis.
• To identify species-specific transcriptomic signatures for improved respiratory research models.

Main Methods:

• Utilized RNA velocity and ligand-receptor co-expression analysis for intercellular communication.
• Integrated publicly available and newly generated scRNA-seq datasets.
• Applied unified gene nomenclature, cell-specific clustering, and marker gene identification.

Main Results:

• Successfully demonstrated a workflow for interspecies data integration.
• Identified distinct cell populations and marker genes within each species' lung tissue.
• Established a foundation for comparative transcriptomic analysis across diverse mammalian lungs.

Conclusions:

• Integrative analysis of multi-species lung scRNA-seq data can uncover species-specific transcriptomic signatures.
• This approach facilitates the selection of suitable animal models for studying both healthy and diseased lung conditions.
• Highlights the power of combining diverse datasets for advancing respiratory research and translational medicine.