Statistical Model to Analyze Quantitative Proteomics Data Obtained by 18O/16O Labeling and Linear Ion Trap Mass Spectrometry APPLICATION TO THE STUDY OF VASCULAR ENDOTHELIAL GROWTH FACTOR-INDUCED ANGIOGENESIS IN ENDOTHELIAL CELLS
Statistical Model to Analyze Quantitative Proteomics Data Obtained by 18O/16O Labeling and Linear Ion Trap Mass Spectrometry APPLICATION TO THE STUDY OF VASCULAR ENDOTHELIAL GROWTH FACTOR-INDUCED ANGIOGENESIS IN ENDOTHELIAL CELLS
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DOI：
10.1074/mcp.m800260-mcp200
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发表时间：
2009-05-01
影响因子：
7
通讯作者：
Vazquez, Jesus
中科院分区：
文献类型：
--
作者：
Jorge, Inmaculada;Navarro, Pedro;Vazquez, Jesus
用于通过稳定同位素标记分析蛋白质表达变化的统计模型仍然很差，特别是对于通过O-16/O-18标记获得的数据。除了大规模的测试实验来验证零假设是缺乏的。虽然血管内皮生长因子（VEGF）促进内皮细胞的生物学作用的机制的研究是相当感兴趣的，定量蛋白质组学研究在这个问题上是稀缺的，并已进行后，暴露细胞的因素很长一段时间。在这项工作中，我们提出了最大的定量蛋白质组学研究，迄今为止的短期影响，血管内皮生长因子对人脐静脉内皮细胞的O-18/O-16标记。发现基于正态性和方差齐性的当前统计模型不适合描述在对这些细胞进行的大规模测试实验中的零假设，从而产生假表达变化。开发了一个随机效应模型，包括光谱拟合、扫描、肽和蛋白质水平的四个不同方差来源。使用新模型，在三个大规模实验中，扫描和肽水平的离群值的数量可以忽略不计，并且在测试实验中，在超过1000种蛋白质中仅观察到一种假蛋白质表达变化。新模型允许检测VEGF刺激4小时和8小时后蛋白质表达的显着变化。在4小时观察到的变化的一致性通过较小规模的复制品证实，并通过一些蛋白质的Western印迹分析进一步验证。大多数观察到的变化以前没有描述过，并且与随着血管生成反应的演变而随时间动态变化的蛋白质表达模式一致。有了这个统计模型的O-18标记方法出现作为一个非常有前途的和强大的替代进行定量蛋白质组学研究的深度几千蛋白质。Molecular & Cellular Proteomics 8：1130-1149，2009.
Statistical models for the analysis of protein expression changes by stable isotope labeling are still poorly developed, particularly for data obtained by O-16/O-18 labeling. Besides large scale test experiments to validate the null hypothesis are lacking. Although the study of mechanisms underlying biological actions promoted by vascular endothelial growth factor (VEGF) on endothelial cells is of considerable interest, quantitative proteomics studies on this subject are scarce and have been performed after exposing cells to the factor for long periods of time. In this work we present the largest quantitative proteomics study to date on the short term effects of VEGF on human umbilical vein endothelial cells by O-18/O-16 labeling. Current statistical models based on normality and variance homogeneity were found unsuitable to describe the null hypothesis in a large scale test experiment performed on these cells, producing false expression changes. A random effects model was developed including four different sources of variance at the spectrum-fitting, scan, peptide, and protein levels. With the new model the number of outliers at scan and peptide levels was negligible in three large scale experiments, and only one false protein expression change was observed in the test experiment among more than 1000 proteins. The new model allowed the detection of significant protein expression changes upon VEGF stimulation for 4 and 8 h. The consistency of the changes observed at 4 h was confirmed by a replica at a smaller scale and further validated by Western blot analysis of some proteins. Most of the observed changes have not been described previously and are consistent with a pattern of protein expression that dynamically changes over time following the evolution of the angiogenic response. With this statistical model the O-18 labeling approach emerges as a very promising and robust alternative to perform quantitative proteomics studies at a depth of several thousand proteins. Molecular & Cellular Proteomics 8: 1130-1149, 2009.