Automated quantification of ciliary beat frequency and area as functional endpoints in ALI-differentiated human airway epithelial models.

The mucociliary system of the respiratory tract is a critical first-line defense that clears inhaled particles through coordinated ciliary motion. To investigate key events involved in respiratory disease pathogenesis and inhalation toxicity, physiologically relevant and functionally validated in vitro airway models are required. Although three-dimensional (3D) airway epithelial cultures differentiated under air-liquid interface (ALI) conditions recapitulate native airway architecture, robust quantitative evaluation of ciliary function beyond structural characterization remains limited. In this study, we developed and validated an automated Python-based analysis pipeline for objective quantification of ciliary activity, integrating temporal (ciliary beat frequency, CBF) and spatial (ciliary beat area, CBA) parameters in primary human small airway epithelial cells (hSAEC) and bronchial airway epithelial cells (hAECB) cultured under ALI conditions. The system enables whole-field, high-throughput, and user-independent analysis of high-speed video microscopy data, overcoming limitations of manual or ROI-based approaches. Biological relevance was demonstrated by strong concordance between automated ciliary metrics and established differentiation markers, including transepithelial electrical resistance, histological assessment, and immunocytochemical analysis. Time-resolved analysis revealed cell type-dependent ciliogenesis, with significant CBA expansion and CBF stabilization after 28 days of ALI differentiation. Overall, this study presents a scalable and quantitative framework for functional evaluation of airway epithelial differentiation, disease modeling.