Shoemark, A;
Pinto, AL;
Patel, MP;
Daudvohra, F;
Hogg, C;
Mitchison, HM;
Burgoyne, T;
(2020)
PCD Detect: enhancing ciliary features through image averaging and classification.
American Journal of Physiology: Lung Cellular and Molecular Physiology
, 319
(6)
L1048-L1060.
10.1152/ajplung.00264.2020.
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Abstract
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 >40 known PCD genes has made the identification of bi-allelic 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 generation multiple averages when a nonhomogeneous abnormality is present. We validated this software on images taken from subjects with well characterised PCD caused by variants in the outer dynein arm (ODA) heavy chain gene DNAH5. Examining more difficult to diagnose cases, we detected (i) regionally restricted absence of the ODAs away from the ciliary base, in a subject carrying mutations in DNAH9; (ii) loss of the typically poorly contrasted inner dynein arms; (iii) sporadic absence of part of the central pair complex in subjects carrying mutations in HYDIN, including one case with an unverified genetic diagnosis. We show this easy to use software can assist in detailing relationships between genotype and ultrastructural phenotype and diagnosing PCD by TEM.
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