PCP Auto Count: A Novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting

Abstract

AbstractBackgroundDuring development, planes of cells give rise to complex tissues and organs. The proper functioning of some cell types is critically dependent on proper inter- and intracellular spatial orientation in regards to the overall tissue, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity investigators must manually measure cell orientations, which is a time-consuming endeavor.MethodologyTo automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called Planar Cell Polarity Auto Count (PCPA) to analyze binary images and identify “chunks” of white pixels that contain “caves” of infiltrated black pixels. Cochleae (P4) and utricles (E17.5) from wildtype mice were immunostained for βII-Spectrin and imaged on a confocal microscope. Images were preprocessed using existing ImageJ functionality to enhance contrast, make binary, and reduce noise. An investigator rated P4 cochlear angle measurements produced by PCPA from P4 cochlea for accuracy using a 1-5 agreement scale. For E17.5 cochlear samples, we compared mean angle measurements per utricular region derived using PCPA against manually derived angle measurements. Finally, PCPA was tested against a variety of images copied from publications examining PCP in various tissues and across various species.ResultsPCPA was able to recognize and count 99.81% of cochlear hair cells (n = 1,1541 hair cells) in a sample set, and was able to obtain accurate planar cell polarity measurements for over 96% of hair cells. When allowing for a <10° deviation from human rater performance PCPA was able to accurately measure over 98% of cochlear hair cells. When manual angle measurements were compared to PCPA’s measurements for E17.5 utricles, PCPA’s mean measurements fell within -9 to +10 degrees of manually obtained mean angle measures. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Altogether, the data suggest that the PCPA plugin suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.