To guide analysts to select the right tool and parameters in differential gene expression analysis of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline (https://github.com/interactivereport/scRNAseq_DE) for single cell analysis will enable scientists to better understand cell-type specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations of filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.

Platelet-activating factor (PAF) is a phospholipid-derived inflammatory mediator, triggering various inflammatory conditions, including eosinophil activation and recruitment. Despite the unclear mechanism underlying the formation and maintenance of nasal polyp in chronic rhinosinusitis with nasal polyps (CRSwNP), there is strongly indication of its association with type 2 inflammation. This study aimed to quantify PAF-associated gene expression, examining enzymes involved in PAF synthesis (lysophosphatidylcholine acyltransferase 1 (LPCAT1), LPCAT2, LPCAT3, and LPCAT4), and PAF degradation (PAF acetylhydrolases 1 B2 (PAFAH1B2), PAFAH1B3, and PAF acetylhydrolases 2 (PAFAH2)), and PAF receptor (PTAFR). Transcriptomic analysis encompassed CRSwNP, including eosinophilic CRS (ECRS) (n=9), nonECRS (n=8), and a subgroup of ECRS with aspirin exacerbated respiratory disease (Asp) (n=3) based on the Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis (JESREC) criteria. Gene expression profiles from nasal polyps in CRSwNP patients and the uncinate process in normal subjects (controls) (n=6) were analyzed using bulk RNA barcoding and sequencing (BRB-seq). Hierarchal analysis, utilizing the average linkage method with iDEP1.1, clarified the relationship between Differentially Expressed Genes (DEG) expressions and cluster formation. Two clusters (cluster 1 (n=11) and cluster 2 (n=9)) of CRSwNP divided by hierarchal analysis showed distinct gene expression profiles in the specific pathway of “cytokine-cytokine receptor interaction” in KEGG pathway and upregulated type 2 inflammatory gene expression in cluster 2. In CRSwNP subclassification of ECRS, nonECRS, and Ctrl, PTAFR was only upregulated in ECRS and nonECRS. In cluster analysis, cluster 1 showed significant downregulation of LPCAT2 and upregulation of PTAFR expression, while cluster 2 showed significant upregulation of LPCAT1, PAFAH1B2, and PTAFR and downregulation of PAFAH2 expression. These findings suggest that cluster 2, characterized by severe type 2 inflammatory gene expression, manifest a prominent PAF-associated pathophysiology. Understanding this high PAF-associated pathophysiology in cluster 2 could provide valuable insights into the treatment and management of CRSwNP.