Stansak Kendra L, Baum Luke D, Ghosh Sumana, Thapa Punam, Vanga Vineel, Walters Bradley J
Department of Otolaryngology - Head and Neck Surgery, University of Mississippi Medical Center, Jackson, Mississippi, United States of America.
bioRxiv. 2024 Feb 15:2024.01.30.578047. doi: 10.1101/2024.01.30.578047.
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). PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. Inner ear sensory epithelia including cochleae (P4) and utricles (E17.5) from mice were immunostained for βII-spectrin and imaged on a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear angle measurements for accuracy using a 1-5 agreement scale. For utricle samples, we directly compared PCPA derived measurements against manually derived angle measurements using concordance correlation coefficients (CCC) and Bland-Altman limits of agreement. Finally, PCPA was tested against a variety of images copied from publications examining PCP in various tissues and across various species.
PCPA 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 ideally accurate planar cell polarity measurements for over 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to >98%. When manual angle measurements for E17.5 utricles were compared, PCPA's measurements fell within -9 to +10 degrees of manually obtained mean angle measures with 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. 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.
在发育过程中，细胞平面会形成复杂的组织和器官。这些组织的正常功能严重依赖于细胞间和细胞内的正确空间定向，这一特征称为平面细胞极性（PCP）。为了研究影响平面细胞极性的遗传和环境因素，研究人员通常必须手动测量细胞定向，这是一项耗时的工作。
为了实现细胞计数和平面细胞极性数据收集的自动化，我们开发了一个名为PCP自动计数（PCPA）的Fiji/ImageJ插件。PCPA分析二值图像，并识别包含浸润黑色像素“洞穴”的白色像素“块”。对来自小鼠的内耳感觉上皮，包括耳蜗（P4）和椭圆囊（E17.5）进行βII-血影蛋白免疫染色，并在共聚焦显微镜下成像。使用现有的Fiji功能对图像进行预处理，以增强对比度、生成二值图像并减少噪声。一名研究人员使用1-5的一致性量表对PCPA耳蜗角度测量的准确性进行评分。对于椭圆囊样本，我们使用一致性相关系数（CCC）和布兰德-奥特曼一致性界限，将PCPA得出的测量值与手动得出的角度测量值直接进行比较。最后，使用从研究各种组织和物种中PCP的出版物中复制的各种图像对PCPA进行测试。
PCPA能够识别并计数样本集中99.81%的耳蜗毛细胞（n = 11541个毛细胞），并能够为超过96%的毛细胞获得理想准确的平面细胞极性测量值。当允许与“完美”测量值有<10°的偏差时，PCPA的准确性提高到>98%。当比较E17.5椭圆囊的手动角度测量值时，PCPA的测量值落在手动获得的平均角度测量值的-9至+10度范围内，CCC为0.999。对果蝇小眼、小鼠室管膜细胞和小鼠放射状祖细胞的示例图像进行定性检查，结果显示PCPA在各种染色、组织类型和物种中都具有很高的准确性。总体而言，数据表明PCPA插件套件是用于自动收集细胞计数和PCP角度测量的强大而准确的工具。
