Guidelines for single-cell RNA sequencing analysis of eosinophils.

Area of Science:

• Immunology
• Genomics
• Bioinformatics

Background:

• Eosinophils are challenging for single-cell RNA sequencing (scRNA-seq) due to RNA degradation from granule enzymes.
• Mature eosinophils exhibit sparse transcriptomes with low gene detection and high dropout rates, complicating analysis.

Purpose of the Study:

• To develop and validate an adapted analytical strategy for eosinophil scRNA-seq.
• To improve gene detection, annotation accuracy, and biological interpretation of eosinophil transcriptomes.

Main Methods:

• Integrated multiple public eosinophil scRNA-seq datasets for cross-platform, tissue, and species comparison.
• Curated a dedicated eosinophil marker-gene panel for reliable annotation.
• Utilized intron-inclusive genome alignment to enhance transcript detection.

Main Results:

• Eosinophils consistently show low transcriptome coverage across datasets.
• Intron-inclusive alignment significantly increased gene and transcript detection compared to exon-only alignment.
• Identified genotype-dependent transcriptional programs, with Il5-transgenic eosinophils showing a less mature profile than wild-type.

Conclusions:

• The developed framework enhances eosinophil recovery, annotation, and interpretation in scRNA-seq.
• Adapted strategies are crucial for overcoming technical challenges in eosinophil transcriptomic profiling.
• Findings provide a practical approach for robust eosinophil analysis in complex biological systems.