Macedo Nayana Damiani, Buzin Aline Rodrigues, de Araujo Isabela Bastos Binotti Abreu, Nogueira Breno Valentim, de Andrade Tadeu Uggere, Endringer Denise Coutinho, Lenz Dominik
Masters Program in Pharmaceutical Sciences, University Vila Velha, Vila Velha, ES, Brazil.
Department of Morphology, Federal University of Espírito Santo, Vitória, ES, Brazil; Faculty of Medicine Carl Gustav Curav-Technical University Dresden, Dresden, Germany.
Tissue Cell. 2017 Feb;49(1):22-27. doi: 10.1016/j.tice.2016.12.006. Epub 2016 Dec 28.
The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation.
A total of 17 images of kidney histological slide samples from male Wistar rats were used. The slides were photographed using an Axio Zeiss Vert.A1 microscope with a 40x objective lens coupled with an Axio Cam MRC Zeiss camera and Zen 2012 software. The images were analyzed using CellProfiler (version 2.1.1) and CellProfiler Analyst open-source software.
Out of the 10,378 objects, 4970 (47,9%) were identified as TUNEL positive, and 5408 (52,1%) were identified as TUNEL negative. On average, the sensitivity and specificity values of the machine learning approach were 0.80 and 0.77, respectively.
Image cytometry provides a quantitative analytical alternative to the more traditional qualitative methods more commonly used in studies.
本研究提出一种自动化机器学习方法，用于根据DNA片段化对细胞死亡途径中的细胞进行定量分析。
共使用了17张来自雄性Wistar大鼠的肾脏组织学切片样本图像。这些切片使用配备40倍物镜的Axio Zeiss Vert.A1显微镜、Axio Cam MRC Zeiss相机和Zen 2012软件进行拍摄。使用CellProfiler（2.1.1版）和CellProfiler Analyst开源软件对图像进行分析。
在10378个对象中，4970个（47.9%）被鉴定为TUNEL阳性，5408个（52.1%）被鉴定为TUNEL阴性。机器学习方法的灵敏度和特异性值平均分别为0.80和0.77。
图像细胞术为研究中更常用传统定性方法提供了一种定量分析替代方法。
