Guidelines for single cell RNA sequencing analysis of eosinophils

Eosinophils are challenging to profile by single cell RNA sequencing (scRNA-seq) approaches due to their fragile nature and the abundance of RNases and cytotoxic enzymes stored in cytoplasmic granules, which can compromise RNA integrity upon stress. Although recent technical advances have improved eosinophil recovery, their transcriptomes remain intrinsically sparse, particularly in mature cells, resulting in low gene detection and high dropout rates that can bias standard preprocessing and quality-control steps. Here, we integrated multiple publicly available eosinophil scRNA-seq datasets from our laboratory and other groups, and performed comparative analyses across platforms, tissues, and species. We show that eosinophils consistently display among the lowest transcriptome coverage, emphasizing the need for eosinophil-adapted analytical strategies. To enable reliable eosinophil annotation despite high dropout rates, we curated a dedicated eosinophil marker-gene panel derived from cross-dataset differential expression signatures. We further demonstrate that intron-inclusive genome alignment markedly increases eosinophil gene and transcript detection compared with exon-only alignment. Finally, we identify genotype-dependent programs: Il5-transgenic eosinophils exhibit a less mature profile, whereas wild-type eosinophils show stronger host-defense-associated signatures. Together, these results provide a practical framework for eosinophil-focused scRNA-seq analysis that improves eosinophil recovery, annotation, and biological interpretation in complex datasets.