Decoding the epithelial-stromal interactome in allergic rhinitis through single-cell multiomics integration.

Abstract

Allergic rhinitis (AR) is a prevalent chronic condition, yet the cellular and molecular changes associated with its pathogenesis remain incompletely understood. We sought to construct a comprehensive cellular atlas of the nasal mucosa in AR and nonallergic rhinitis and elucidate disease-associated transcriptional and epigenetic alterations. We performed single-cell RNA sequencing and single-cell ATAC sequencing on nasal mucosa samples from 39 subjects (25 AR, 14 nonallergic rhinitis). Differentially expressed gene analysis, differentially accessible peaks analysis, cell-cell communication, trajectory inference, and gene regulatory network reconstruction were applied. A deep learning framework was developed to integrate multiomics data for disease prediction. We profiled 1,024,146 cells, constructing a comprehensive nasal mucosa atlas. The AR epithelium exhibited aberrant differentiation with suppressed maturation of basal and club cells, while fibroblasts displayed inflammatory activation and matrix remodeling signatures. Epithelial-stromal cross talk was enhanced in the AR group. Cell subset-specific epigenetic alterations were also observed. Single-cell multiomics for AR integrative analysis, or scMARIA, can simultaneously predict AR risk and clinically relevant disease parameters, and prioritize putative cell type-specific regulatory linkages. This multiomics study establishes a comprehensive molecular framework of the nasal mucosa, revealing dysregulated epithelial-stromal interactions and gene regulatory networks that are correlated with AR status.