Expedited Assessment of Environment-induced Respiratory Ciliopathies Leveraging Motile Apical-out Airway Organoids

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 computational
analysis.

Grant Number: 5F31HL176100-02
NIH Institute/Center: NIH
Principal Investigator: Dhruv Bhattaram