Buzin Aline Rodrigues, Macedo Nayana Damiani, De Araujo Isabela Bastos Binotti Abreu, Nogueira Breno Valentim, de Andrade Tadeu Uggere, Endringer Denise Coutinho, Lenz Dominik
Pharmaceutical Sciences, University of Vila Velha, Brazil.
Department of Morphology, Federal University of Espirito Santo, Brazil; Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden (TUD), Dresden, Germany.
J Immunol Methods. 2017 May;444:47-50. doi: 10.1016/j.jim.2017.02.005. Epub 2017 Feb 14.
The objective of this study was the identification of the stain HIF-alpha using the Image Cytometry, and to help to count the positive cells (with HIF-alpha) and the negative cells (without HIF-alpha) from the same sample.
17 images of renal tissues from male rats of Winstar lineage; overall, there were 12.587 objects (cells) in the images for analysis. The acquired images were then analyzed through the free softwares CellProfiler (version 2.1.1) and CellProfiler Analyst (version 2.0). In the software CellProfiler Anlyst, there was a separation with the classes of the object, using a classifier, and the classes were: 1) class with HIF-alpha and 2) class without HIF-alpha.
With the data obtained through Score All, it was possible to calculate the percentage of cells that had HIF-alpha; out of 12.587 objects of the sample, 6.773 (54%) had HIF-alpha and 5.814 (46%) did not have HIF-alpha. Data of sensibility 0.90, specificity 0.84 and standard deviation 0.10 and 0.12.
The research shows that the free software CellProfiler, through the light microscope, was able to identify the stains, perform the machine's learning, and subsequently count and separate cells from distinct classes (with and without the stain of HIF-alpha).
本研究的目的是使用图像细胞术鉴定缺氧诱导因子α（HIF-α）染色，并帮助对同一样本中的阳性细胞（有HIF-α）和阴性细胞（无HIF-α）进行计数。
选取17张来自Wistar品系雄性大鼠的肾组织图像；总体而言，图像中共有12587个分析对象（细胞）。然后使用免费软件CellProfiler（版本2.1.1）和CellProfiler Analyst（版本2.0）对获取的图像进行分析。在CellProfiler Analyst软件中，使用分类器对对象类别进行分离，类别包括：1）有HIF-α的类别和2）无HIF-α的类别。
通过Score All获得的数据，可以计算出有HIF-α的细胞百分比；在样本的12587个对象中，6773个（54%）有HIF-α，5814个（46%）没有HIF-α。灵敏度数据为0.90，特异性为0.84，标准差为0.10和0.12。
研究表明，免费软件CellProfiler通过光学显微镜能够识别染色，进行机器学习，并随后对不同类别的细胞（有和无HIF-α染色）进行计数和分离。
