Abstract
Motile cilia generate fluid flows that are essential for normal development and physiology. Cilia display diverse beating waveforms, and while pronounced defects are strongly associated with motile ciliopathies, subtler alterations also influence disease manifestations. Finer quantification of ciliary dynamics is therefore critical for understanding ciliopathies, but the heterogeneity of cilia beating makes accurate and robust characterization challenging. Here, we present CiliaIO, a machine learning-based tool for quantification of motile cilia morphodynamics. Using this platform, we discovered subtle but highly significant regional differences in ciliary waveforms in the zebrafish spinal cord. To demonstrate the tool's efficacy, we used CiliaIO to capture and quantify subtle ciliary defects in a novel bbs2 allele that causes a late-onset scoliosis phenotype. These results provide a workflow for additional fine-scale analyses of ciliary morphodynamics that will be important for understanding motile ciliopathy.