Maidana Daniel E, Tsoka Pavlina, Tian Bo, Dib Bernard, Matsumoto Hidetaka, Kataoka Keiko, Lin Haijiang, Miller Joan W, Vavvas Demetrios G
Invest Ophthalmol Vis Sci. 2015 Oct;56(11):6701-8. doi: 10.1167/iovs.15-17599.
TUNEL assay is widely used to evaluate cell death. Quantification of TUNEL-positive (TUNEL+) cells in tissue sections is usually performed manually, ideally by two masked observers. This process is time consuming, prone to measurement errors, and not entirely reproducible. In this paper, we describe an automated quantification approach to address these difficulties.
We developed an ImageJ macro to quantitate cell death by TUNEL assay in retinal cross-section images. The script was coded using IJ1 programming language. To validate this tool, we selected a dataset of TUNEL assay digital images, calculated layer area and cell count manually (done by two observers), and compared measurements between observers and macro results.
The automated macro segmented outer nuclear layer (ONL) and inner nuclear layer (INL) successfully. Automated TUNEL+ cell counts were in-between counts of inexperienced and experienced observers. The intraobserver coefficient of variation (COV) ranged from 13.09% to 25.20%. The COV between both observers was 51.11 ± 25.83% for the ONL and 56.07 ± 24.03% for the INL. Comparing observers' results with macro results, COV was 23.37 ± 15.97% for the ONL and 23.44 ± 18.56% for the INL.
We developed and validated an ImageJ macro that can be used as an accurate and precise quantitative tool for retina researchers to achieve repeatable, unbiased, fast, and accurate cell death quantitation. We believe that this standardized measurement tool could be advantageous to compare results across different research groups, as it is freely available as open source.
TUNEL 检测广泛用于评估细胞死亡。组织切片中 TUNEL 阳性（TUNEL+）细胞的定量通常由人工进行，理想情况下由两名不知情的观察者完成。这个过程耗时、容易出现测量误差且不完全可重复。在本文中，我们描述了一种自动化定量方法来解决这些难题。
我们开发了一个 ImageJ 宏程序，用于在视网膜横断面图像中通过 TUNEL 检测对细胞死亡进行定量。该脚本使用 IJ1 编程语言编写。为了验证这个工具，我们选择了一组 TUNEL 检测数字图像数据集，手动计算层面积和细胞计数（由两名观察者完成），并比较观察者之间的测量结果和宏程序的结果。
自动化宏程序成功分割了外核层（ONL）和内核层（INL）。自动化 TUNEL+细胞计数介于经验不足和经验丰富的观察者的计数之间。观察者内部变异系数（COV）范围为 13.09%至 25.20%。对于 ONL，两名观察者之间的 COV 为 51.11±25.83%，对于 INL 为 56.07±24.03%。将观察者的结果与宏程序的结果进行比较，ONL 的 COV 为 23.37±15.97%，INL 的 COV 为 23.44±18.56%。
我们开发并验证了一个 ImageJ 宏程序，它可以作为视网膜研究人员进行准确、精确的定量工具，以实现可重复、无偏差、快速且准确的细胞死亡定量。我们相信，这个标准化的测量工具对于比较不同研究组的结果可能是有利的，因为它作为开源软件可免费获取。
