RESUMEN
Cilia density, distribution and beating frequency are important properties of airway epithelial tissues. These parameters are critical in diagnosing primary ciliary dyskinesia and examining in vitro models, including those derived from induced pluripotent stem cells. Video microscopy can be used to characterize these parameters, but most tools available at the moment are limited in the type of information they can provide, usually only describing the ciliary beat frequency of very small areas, while requiring human intervention and training for their use. We propose a novel and open-source method to fully characterize cilia beating frequency and motile cilia coverage in an automated fashion without user intervention. We demonstrate the ability to differentiate between different coverage densities, identifying even small patches of cilia in a larger field of view, and to fully characterize the cilia beating frequency of all moving areas. We also show that the method can be used to combine multiple fields of view to better describe a sample without relying on small pre-selected regions of interest. This is released with a simple graphical user interface for file handling, enabling a full analysis of individual fields of view in a few minutes on a typical personal computer.