A Graphical Model to Determine the Subcellular Protein Location in Artificial Tissues.
A Graphical Model to Determine the Subcellular Protein Location in Artificial Tissues.
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DOI：
10.1109/isbi.2010.5490167
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发表时间：
2010-04
期刊：
影响因子：
--
通讯作者：
Murphy RF
中科院分区：
文献类型：
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作者：
Glory-Afshar E;Osuna-Highley E;Granger B;Murphy RF
定位蛋白质组学关注的是对蛋白质亚细胞定位的系统分析。为了对所有的蛋白质定位模式进行全面的分析，需要自动化的方法。为了将自动亚细胞定位模式分析方法扩展到组织的高分辨率图像，收集了多种蛋白质免疫染色的极化CaCo2细胞的3D共聚焦显微镜图像。开发了一种三色染色方案，允许对感兴趣的蛋白质以及DNA和肌动蛋白细胞骨架进行平行成像。该集合由描绘主要亚细胞模式的9种蛋白质中的每种蛋白质的11到21张图像组成。训练分类器识别分段细胞的亚细胞定位模式，准确率为89.2%。使用先验更新方法可以将准确度提高到99.6%。本研究证明了使用图形模型方法改善组织图像的模式分类的好处。
Location proteomics is concerned with the systematic analysis of the subcellular location of proteins. In order to perform comprehensive analysis of all protein location patterns, automated methods are needed. With the goal of extending automated subcellular location pattern analysis methods to high resolution images of tissues, 3D confocal microscope images of polarized CaCo2 cells immunostained for various proteins were collected. A three-color staining protocol was developed that permits parallel imaging of proteins of interest as well as DNA and the actin cytoskeleton. The collection is composed of 11 to 21 images for each of the 9 proteins that depict major subcellular patterns. A classifier was trained to recognize the subcellular location pattern of segmented cells with an accuracy of 89.2%. Using the Prior Updating method allowed improvement of this accuracy to 99.6%. This study demonstrates the benefit of using a graphical model approach for improving the pattern classification in tissue images.