AUTHOR=Stansak Kendra L. , Baum Luke D. , Ghosh Sumana , Thapa Punam , Vanga Vineel , Walters Bradley J.
TITLE=PCP auto count: a novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting
JOURNAL=Frontiers in Cell and Developmental Biology
VOLUME=12
YEAR=2024
URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2024.1394031
DOI=10.3389/fcell.2024.1394031
ISSN=2296-634X
ABSTRACT=Introdution: During development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity, investigators must often manually measure cell orientations, which is a time-consuming endeavor. To automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA).
Methods: PCPA analyzes binary images and identifies “chunks” of white pixels that contain “caves” of infiltrated black pixels. For validation, inner ear sensory epithelia including cochleae and utricles from mice were immunostained for βII-spectrin and imaged with a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear hair cell angle measurements for accuracy using a one to five agreement scale. For utricle samples, PCPA derived measurements were directly compared against manually derived angle measurements and the concordance correlation coefficient (CCC) and Bland-Altman limits of agreement were calculated. PCPA was also tested against previously published images examining PCP in various tissues and across various species suggesting fairly broad utility.
Results: PCPA was able to recognize and count 99.81% of cochlear hair cells, and was able to obtain ideally accurate planar cell polarity measurements for at least 96% of hair cells. When allowing for a <10° deviation from “perfect” measurements, PCPA’s accuracy increased to 98%–100% for all users and across all samples. When PCPA’s measurements were compared with manual angle measurements for E17.5 utricles there was negligible bias (<0.5°), and a CCC of 0.999. 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.
Discussion: Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.