Airway clearance of pathogens and particulates relies on motile cilia. Impaired cilia motility can lead to reduction in lung function, lung transplant, or death in some cases. More than 50 proteins regulating cilia motility are linked to primary ciliary dyskinesia (PCD), a heterogeneous, mainly recessive genetic lung disease. Accurate PCD molecular diagnosis is essential for identifying therapeutic targets and for initiating therapies that can stabilize lung function, thereby reducing socioeconomic impact of the disease. To date, PCD diagnosis has mainly relied on nonquantitative methods that have limited sensitivity or require a priori knowledge of the genes involved. Here, we developed a quantitative super-resolution microscopy workflow: (i) to increase sensitivity and throughput, (ii) to detect structural defects in PCD patients’ cells, and (iii) to quantify motility defects caused by yet to be found PCD genes. Toward these goals, we built a localization map of PCD proteins by three-dimensional structured illumination microscopy and implemented quantitative image analysis and machine learning to detect protein mislocalization, we analyzed axonemal structure by stochastic optical reconstruction microscopy, and we developed a high-throughput method for detecting motile cilia uncoordination by rotational polarity. Together, our data show that super-resolution methods are powerful tools for improving diagnosis of motile ciliopathies.

病原体和微粒的气道清除依赖于运动性纤毛。在某些情况下，纤毛运动能力受损可导致肺功能下降，肺移植或死亡。超过50种调节纤毛运动性的蛋白质与原发性睫状运动障碍（PCD）相关，后者是一种异质性，主要是隐性遗传性肺疾病。准确的PCD分子诊断对于确定治疗目标和启动可以稳定肺功能从而降低疾病的社会经济影响的治疗至关重要。迄今为止，PCD诊断主要依靠灵敏度有限或需要先验了解所涉及基因的非定量方法。在这里，我们开发了定量的超分辨率显微镜工作流程：（i）提高灵敏度和通量，（ii）检测PCD患者细胞中的结构缺陷，（iii）量化由尚未发现的PCD基因引起的运动缺陷。为了实现这些目标，我们通过三维结构化照明显微镜建立了PCD蛋白质的定位图，并进行了定量图像分析和机器学习以检测蛋白质的错误定位，并通过随机光学重建显微镜分析了轴突的结构，并开发了一种高通量方法用于通过旋转极性检测运动性纤毛不协调。总之，我们的数据表明，超分辨率方法是改善运动型纤毛病诊断的有力工具。我们通过三维结构化照明显微镜建立了PCD蛋白质的定位图，并进行了定量图像分析和机器学习来检测蛋白质的错误定位，我们通过随机光学重建显微镜分析了轴突的结构，并开发了一种高通量的运动性纤毛检测方法旋转极性不协调。总之，我们的数据表明，超分辨率方法是改善运动型纤毛病诊断的有力工具。我们通过三维结构化照明显微镜建立了PCD蛋白质的定位图，并进行了定量图像分析和机器学习来检测蛋白质的错误定位，我们通过随机光学重建显微镜分析了轴突的结构，并开发了一种高通量的运动性纤毛检测方法旋转极性不协调。总之，我们的数据表明，超分辨率方法是改善运动型纤毛病诊断的有力工具。