Visualization and detection of ciliary beating frequency in human airway organoids based on image correlated methods for drug screening.

Respiratory ciliary motion is critical for airway hygiene, as its directional beating clears pathogens and debris. Dysfunctional cilia lead to mucus buildup, infections, and worsened respiratory diseases. However, studying ciliary dynamics remains challenging due to technical limitations in imaging and analysis. To address this, human airway organoids expressing specific biomarkers were cultured, and ciliary motility was captured using a charge-coupled device camera. Wavelet transform-based image processing reduced background noise and enhanced cilia contours, enabling color visualization and automated motion analysis. An image-processing motion-tracking system was developed to quantify ciliary beating frequency (CBF), integrating optical stabilization, computational enhancement, and pattern recognition. This method provided spatiotemporal visualization, segmented frequency values, correlation coefficient curves, and dynamic analysis videos with real-time updates. Automated cilia recognition improved localization during movement, minimizing interference. Using this platform, Roflumilast and procaterol were identified as effective enhancers of ciliary beating. Transcriptomic analysis further revealed key regulatory networks governing ciliary structure and function. This framework combines mechanistic insights with therapeutic screening, offering a powerful tool for advancing respiratory research and clinical applications. By enabling precise, high-resolution ciliary motion analysis, this approach addresses longstanding challenges in the field and opens new avenues for targeted therapies in respiratory pathologies.