Human nasal epithelial cells as an integrated platform for biomarker discovery, functional airway studies, and patient-specific stem cell generation

Abstract

Abstract Studying epithelial dysfunction in respiratory diseases requires physiologically relevant human models that faithfully recapitulate airway structure and function. Primary human nasal epithelial cells (hNECs) represent an accessible and clinically validated system, widely used for mutation-specific drug theratyping. However, the broader potential of hNECs as an integrated experimental platform remains underexplored. Here, we establish a customized hNEC-based platform spanning ex vivo, in vitro, and stem cell-based applications, with direct relevance to precision medicine. Using freshly fixed nasal brushings, we developed a quantitative single-cell metric to assess Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) localization at the ionocyte plasma membrane, identifying a candidate ex vivo biomarker sensitive to disease status and pharmacological modulation in cystic fibrosis (CF). In parallel, transcriptomic profiling demonstrated that air-liquid interface-differentiated hNECs closely recapitulate the molecular features of native nasal epithelium. Functionally, differentiated hNECs reproduced key properties of airway surface liquid (ASL) physiology and its dysregulation under inflammatory and CF conditions. Finally, we show that basal hNECs provide a robust and accessible source for induced pluripotent stem cell (iPSC) generation. hNEC-derived iPSCs exhibited canonical pluripotency features, differentiated efficiently into germ layer derivatives and lung progenitors, and displayed transcriptomic profiles indistinguishable from iPSCs generated from other somatic sources, supporting their suitability for patient-specific disease modeling. Collectively, these findings position hNECs as an integrated, patient-specific platform linking ex vivo biomarker discovery, in vitro functional assays, and stem cell–based modeling, thereby expanding their utility for precision medicine.