PCD Detect: enhancing ciliary features through image averaging and classification.

Primary ciliary dyskinesia (PCD) is an inherited disorder of the motile cilia. Early accurate diagnosis is important to help prevent lung damage in childhood and to preserve lung function. Confirmation of a diagnosis traditionally relied on assessment of ciliary ultrastructure by transmission electron microscopy (TEM); however, >50 known PCD genes have made the identification of biallelic mutations a viable alternative to confirm diagnosis. TEM and genotyping lack sensitivity, and research to improve accuracy of both is required. TEM can be challenging when a subtle or partial ciliary defect is present or affected cilia structures are difficult to identify due to poor contrast. Here, we demonstrate software to enhance TEM ciliary images and reduce background by averaging ciliary features. This includes an option to classify features into groups based on their appearance, to generate multiple averages when a nonhomogeneous abnormality is present. We validated this software on images taken from subjects with well-characterized PCD caused by variants in the outer dynein arm (ODA) heavy chain gene . Examining more difficult to diagnose cases, we detected ) regionally restricted absence of the ODAs away from the ciliary base, in a subject carrying mutations in ; ) loss of the typically poorly contrasted inner dynein arms; and ) sporadic absence of part of the central pair complex in subjects carrying mutations in , including one case with an unverified genetic diagnosis. We show that this easy-to-use software can assist in detailing relationships between genotype and ultrastructural phenotype, improving diagnosis of PCD.