Abstract
BackgroundPrimary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. Typically, quantitative cilia analysis is done manually, which is very time-consuming. While some open-source and commercial image analysis software applications can segment input data, they still require the user to optimize many parameters, suffer from user bias, and often lack rigorous performance quality assessment (e.g., false positives and false negatives). Further, optimal parameter combinations vary in detection accuracy depending on cilia reporter, cell type, and imaging modality. A good automated solution would analyze images quickly, robustly, and adaptably—across different experimental data sets—without significantly compromising the accuracy of manual analysis.MethodsTo solve this problem, we developed a new software for automated cilia detection in cells (ACDC). The software operates through four main steps: image importation, pre-processing, detection auto-optimization, and analysis. From a data set, a representative image with manually selected cilia (i.e., Ground Truth) is used for detection auto-optimization based on four parameters: signal-to-noise ratio, length, directional score, and intensity standard deviation. Millions of parameter combinations are automatically evaluated and optimized according to an accuracy ‘F1’ score, based on the amount of false positives and false negatives. Afterwards, the optimized parameter combination is used for automated detection and analysis of the entire data set.ResultsThe ACDC software accurately and adaptably detected nuclei and primary cilia across different cell types (NIH3T3, RPE1), cilia reporters (AcTub, Smo-GFP, Arl13b), and image magnifications (60×, 40×). We found that false-positive and false-negative rates for Arl13b-stained cilia were 1–6%, yielding high F1 scores of 0.96–0.97 (max. = 1.00). The software detected significant differences in mean cilia length between control and cytochalasin D-treated cell populations and could monitor dynamic changes in cilia length from movie recordings. Automated analysis offered up to a 96-fold speed enhancement compared to manual analysis, requiring around 5 s/image, or nearly 18,000 cilia analyzed/hour.ConclusionThe ACDC software is a solution for robust automated analysis of microscopic images of ciliated cells. The software is extremely adaptable, accurate, and offers immense time-savings compared to traditional manual analysis.
Highlights
Primary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies
Software performance on real samples To assess the accuracy of the software’s analysis, we looked at three cilia reporters (AcTub, Smo-green fluorescent protein (GFP), Arl13b) across two different cell lines (NIH3T3, RPE1)
We showed that very high cell confluency can possibly obscure cilia frequency calculations, as the nuclei count for high-confluency images tended to possess more false negatives (FN rate = 12%) compared to low-confluency images (Additional file 5: Fig. S5)
Summary
Primary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. For in vitro cell culture studies, specimens are prepared so that primary cilia are lying flat along the coverslip. Acquiring these types of data requires analysis of microscopy images of ciliated cells. After image acquisition, images are manually analyzed using simple line measurement tools associated with the microscope software or a generic image analysis program such as ImageJ (http://rsbweb.nih.gov/ij/). Because this process can be time-consuming and susceptible to user bias, there has been a push for the development of practical, automated image analysis software over the past two decades. Accurate automated performance can even detect small changes that are too subtle, or too tedious, for the human visual system to assess [17]
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