Background Airway hyperresponsiveness (AHR) is a clinically important feature of chronic pediatric inflammatory airway disease, but its cellular architecture remains incompletely resolved. Because bulk transcriptomic approaches can mask signals from rare or state‐specific cell populations, single‐cell RNA sequencing (scRNA‐seq) offers a useful framework for examining epithelial and immune cell diversity in hyperresponsive airways. Methods Publicly available scRNA‐seq data (GEO: GSM8722221) from bronchial brushing specimens of pediatric AHR patients (10x Genomics Chromium platform) were processed with Cell Ranger, quality‐filtered, and analyzed using PCA (15 PCs), UMAP/tSNE embedding, and Leiden clustering (resolution = 0. 8). Cell types were annotated by canonical markers and validated via Seurat label transfer. Pseudotime trajectories were inferred with Monocle (DDRTree) and validated by scVelo, Slingshot, and PAGA. Macrophage subpopulations were resolved at resolution = 1. 2 and profiled by hdWGCNA. GO and KEGG enrichment used clusterProfiler (Benjamini‐Hochberg; adjusted p <0. 05). Network pharmacology integrated TCM BTMAN 2. 0, Swiss Target Prediction, SEA, and PPI topology to identify targets and herbal compounds from Xiao Qing Long Tang. Results Quality control supported the analytical suitability of the dataset (mitochondrial fraction vs. nCountRNA r = ‐0. 31; inter‐dataset r = 0. 96). Actb, Gapdh, Tuba1b, Hmga2, Vim, Col1a1, Zeb1, Snai2, Twist1, and Mmp9 were among the top variable genes, and the first 15 PCs were retained after JackStraw testing. UMAP and tSNE resolved eight airway mucosal cell populations with concordant separation. Cell‐type‐specific expression of TSLP, IL33, CCL26, S100A8, POSTN, CLCA1, FCGR3B, TNFRSF8, FOXJ1, and SCGB1A1 was consistent with known inflammatory, remodeling, and mucociliary programs. High‐resolution macrophage analysis identified 23 putative subpopulations, which were interpreted cautiously as computationally defined states. Enrichment analysis highlighted cytoplasmic translation, ribosome biogenesis, oxidative phosphorylation, and related mitochondrial signatures. Network pharmacology identified VEGFA, PTGS2, and ESR1 among prioritized targets, yielding 83 shared candidates between disease‐associated and compound‐associated target sets. Conclusions This study provides a pediatric AHR single‐cell atlas and an integrated, hypothesis‐generating view of epithelial‐immune heterogeneity, macrophage state diversity, and candidate therapeutic networks. The findings should be interpreted as computational evidence that requires validation in healthy pediatric controls, independent cohorts, and functional experiments.
Yang et al. (Thu,) studied this question.