ABSTRACT Exposure to airborne pollutants and harmful chemicals, along with smoking, can lead to a wide range of respiratory diseases, including chronic obstructive pulmonary disease (COPD), bronchiectasis, and asthma; COPD is the third leading cause of death worldwide, with disease counts still on the rise. These diverse respiratory disorders have a pathological hallmark in common: cilia beating defects (ciliopathies), which lead to impaired removal of foreign particulates via the mucociliary escalator, airway obstruction, and increased mortality. Despite the importance, traditional methods for assessing cilia function are equipment-demanding and time- consuming, due to cilia’s nano-scale size and high beating frequency. To overcome this hurdle, this proposal will combine advanced engineering and computational analysis of apical-out airway organoids (AOAOs) to generate physiologically relevant quantitative metrics for modeling mucociliary dysfunction in the human airway. The AOAO exhibits novel behavior that translates the nano-scale cilia beating into micro-scale cilia-powered organoid locomotion, dramatically improving spatiotemporal resolution and enabling cilia functional analysis using computer vision to deliver unprecedented throughput without the need for specialized equipment. Furthermore, the AOAO enables non-invasive pollutant introduction directly to the physiologic, outward-facing apical epithelial surface. The central hypotheses of this project are that the AOAO locomotion correlates with and predicts cilia function and that its apical-out epithelial polarity will allow close mimicry of in vivo injury response dynamics induced by environmental pollution. To test these hypotheses and, thereby, attain the overall objective, the following specific aims will be pursued. Aim 1 will deliver computational tools for rapid ciliopathy diagnosis using point-tracking algorithms to extract AOAO locomotion metrics to correlate with core aspects of cilia function (density, beating frequency, and coordination). Accuracy and accessibility to later users will be further enhanced by utilizing machine learning algorithms to provide automation and high-level feature extraction. Aim 2 will further assess this experimental and computational pipeline for evaluating mucociliary dysfunction by exposing AOAOs to Diesel Particulate Matter (DPM), a model pollutant and major respiratory health threat with close relevance to real-world pollution exposure. AOAOs will be evaluated through computer vision and single-cell transcriptomic analysis to assess the theragnostic utility of the platform for recapitulating native airway-pollutant interactions. The rationale for the proposed research is that a stem cell-based, high-throughput model of respiratory injury will enable accelerated and personalized therapeutic development and clinical management. Concurrent with the pursuit of this research, this project will facilitate the PI’s mastery over organoid engineering and...