A protocol to analyze single-cell RNA-seq data from Mycobacterium tuberculosis-infected mice lung

Area of Science:

• Immunology
• Genomics
• Computational Biology

Background:

• Analyzing single-cell RNA sequencing (scRNA-seq) data from lung cells presents challenges due to complex cell subtypes and biological variability.
• Understanding lung lymphocyte populations is crucial for studying host responses to infections like tuberculosis.

Purpose of the Study:

• To provide a detailed protocol for analyzing scRNA-seq data from mouse lung lymphocyte populations.
• To facilitate the in-depth assessment of cellular responses in both healthy and Mycobacterium tuberculosis-infected mice.

Main Methods:

• Downloading and integrating processed scRNA-seq datasets from different experimental conditions.
• Performing cluster analysis to identify distinct cell populations.
• Identifying specific lymphoid cell subtypes, conducting differential expression analysis, and performing pathway enrichment analysis.

Main Results:

• The protocol enables comprehensive characterization of lung lymphocyte populations.
• It allows for the identification of distinct cell subtypes and their functional states.
• The methods facilitate the discovery of molecular pathways altered during Mycobacterium tuberculosis infection.

Conclusions:

• This protocol offers a standardized approach for analyzing lung lymphocyte scRNA-seq data.
• It is valuable for researchers investigating immune responses in lung diseases and infections.
• The methodology supports detailed comparative analyses between different physiological or pathological states.