High-Throughput Assessment of Respiratory Cilia Beating Defects Using Computer Vision on Rotating Airway Organoids
Abstract
This proposal addresses the FY20 PRMRP Topic Area of “Respiratory Health” and aims to develop and validate a novel mini-lung tissue model and associated computational tools for automated, personalized evaluation of lung injury, and therefore promote improved treatment development and disease management. Due to excessive exposure to air pollution (such as engine waste, smoke, and dust), military service members and veterans suffer higher risk of developing chronic lung diseases. In particular, a critical type of structures being injured in patients’ lung are the cilia lining the respiratory tracts. There are approximately three billion clila in the human lung, which are tiny brush-like structures with less than a tenth the width of a human hair. Cilia beat in a coordinated way to move mucus towards the mouth, therefore, cilia beating defects have detrimental consequences leading to difficulty in mucus clearance and breathing. The goal of this project is to establish a novel mini-lung tissue model to evaluate cilia beating defects resulting from air pollution-induced lung injury. One of the key challenges in using existing lung tissue models for lung injury assessment is that they have an “outside-in” conformation with the lung surface that should be facing external environment abnormally hidden inside the tissue and becoming inaccessible to experimental pollutants. To address this limitation, a novel technology will be developed to effectively reverse the orientation of mini-lung tissue models to the physiological “outside-out” conformation. In this way, the exterior surface in the engineered tissue can be readily accessed by air pollutants, which closely mimics the situation in real human lung. Interestingly, the cilia that beat on the exterior surface of the “outside-out” mini-lung serve as thousands of “paddles” that propel the mini-lung tissue to spin in three dimensions. Taking advantage of this unique phenomena, the evaluation of the fast beating speed of tiny cilia will be converted to the measurement of the 10 times slower spinning speed of the much bigger (500 times) mini-lung tissues. This will be done using automated, computer-assisted video analysis, and will not only reduce the equipment requirement but also improve the efficiency of cilia function analysis. The two key hypotheses of this project are that (1) the spinning speed of the “outside-out” mini-lung tissues can be used to predict cilia beating speed, and that (2) the “outside-out” mini-lung tissue can fully mimic pollution-induced injury of the real human lung. These two hypotheses will be examined in the two specific research aims. In Aim 1, computer vision methods will be developed to accelerate cilia beating analysis by measuring organoid spinning. In Aim 2, pollution particle-induced lung injury will be non-invasively modeled and assessed in the novel “outside-out” mini-lung tissue. Successful accomplishment of this project will have significant impacts on the clinical care of military and civilian patients suffering from lung injury. In the short term, technologies developed from this project will pave the way for evaluation and diagnosis of lung injury with unprecedented efficiency, as the proposed mini-lung tissue production approach can be scaled to produce nearly thirty thousand mini-tissues at the same time. In the long term, this project will enable personalized lung injury evaluation, treatment development, and disease management, as the proposed mini-lung tissue can be engineered from patient-specific cells that can be conveniently obtained from patients using simple, minimally invasive medical procedures, such as endobronchial brushing. In summary, this project addresses two key Areas of Encouragement of the “Respiratory Health” Topic Area by 1) developing improved methods for evaluating lung injury due to air pollution; and 2) facilitating research on the causes and treatment of chronic lung diseases by enabling personaliz
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110183
Entities
People
- Xi Ren
Organizations
- Carnegie Mellon University
- United States Army