Integrated single-cell and transcriptional analysis of neutrophil migrasome-associated gene mechanisms in ARDS

The clinical presentation and inflammatory responses of acute respiratory distress syndrome (ARDS) had been previously described, yet the molecular mechanisms through which neutrophil migration occurs in such a condition were still unclear, hindering targeted therapeutic development. In the present study, we downloaded single-cell transcriptomic data of ARDS in the Gene Expression Omnibus (GEO) and calculated the migratory single-cell proportion on the basis of the single-sample Gene Set Enrichment Analysis (ssGSEA) approach. This method enabled us to find 1,268 differentially expressed genes which are connected with migrasomes concerning neutrophils in ARDS. Incorporating LASSO and SVM-RFE models we also identified important genes, notably COMT, PTPRC, and RPAP2. Functional enrichment analysis, cell communication analysis through Cellcall and other bioinformatics tools including GSEA, GSVA, immune infiltration analysis, motif analysis, non-coding RNA analysis, and drug prediction were utilized to understand the molecular mechanism and find potential therapeutic targets of ARDS. Moreover, we verified the single-cell expression of the key genes, studied how and at what time they are co-expressed in tandem with the migration-related genes, and did a pseudo-temporal analysis of neutrophils. These key genes were identified as being associated with many pathways and the infiltration of immune cells. Mechanically, these genes can affect the movement of the neutrophils and ARDS. To conclude, our results indicate that migrasome-related mechanisms in neutrophils is also closely linked to ARDS progression, and parameters found by us, including genes like COMT, PTPRC, RPAP2 might be potential targets in treating ARDS in future. • Defined a core set of neutrophil migrasome-related genes in ARDS through integrated single-cell and transcriptional analysis. • Pinpointed COMT, PTPRC, and RPAP2 as key genes via an integrated machine-learning pipeline (LASSO and SVM-RFE). • Revealed key genes’ links to pathways/immune cells, regulators, drugs. • Pseudotime analysis delineates neutrophil trajectory and dynamic expression of key genes.