Compositional Proteomics: Effects of Spatial Constraints on Protein Quantification Utilizing Isobaric Tags.,Journal of Proteome Research

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Compositional Proteomics: Effects of Spatial Constraints on Protein Quantification Utilizing Isobaric Tags.
Journal of Proteome Research ( IF 3.8 ) Pub Date : 2017-12-15 , DOI: 10.1021/acs.jproteome.7b00699
Jonathon J O'Brien ₁ , Jeremy D O'Connell ₁ , Joao A Paulo ₁ , Sanjukta Thakurta ₁ , Christopher M Rose ₁ , Michael P Weekes ₂ , Edward L Huttlin ₁ , Steven P Gygi ₁

Affiliation

Mass spectrometry (MS) has become an accessible tool for whole proteome quantitation with the ability to characterize protein expression across thousands of proteins within a single experiment. A subset of MS quantification methods (e.g., SILAC and label-free) monitor the relative intensity of intact peptides, where thousands of measurements can be made from a single mass spectrum. An alternative approach, isobaric labeling, enables precise quantification of multiple samples simultaneously through unique and sample specific mass reporter ions. Consequently, in a single scan, the quantitative signal comes from a limited number of spectral features (≤11). The signal observed for these features is constrained by automatic gain control, forcing codependence of concurrent signals. The study of constrained outcomes primarily belongs to the field of compositional data analysis. We show experimentally that isobaric tag proteomics data are inherently compositional and highlight the implications for data analysis and interpretation. We present a new statistical model and accompanying software that improves estimation accuracy and the ability to detect changes in protein abundance. Finally, we demonstrate a unique compositional effect on proteins with infinite changes. We conclude that many infinite changes will appear small and that the magnitude of these estimates is highly dependent on experimental design.

中文翻译：

组成蛋白质组学：空间限制对利用同量异位标签进行蛋白质定量的影响。

质谱 (MS) 已成为整个蛋白质组定量的一种可用工具，能够在一次实验中表征数千种蛋白质的蛋白质表达。 MS 定量方法的一个子集（例如 SILAC 和无标记）监测完整肽的相对强度，其中可以从单个质谱中进行数千次测量。另一种方法是同量异位标记，可以通过独特的样品特定质量报告离子同时对多个样品进行精确定量。因此，在单次扫描中，定量信号来自有限数量的光谱特征（≤11）。针对这些功能观察到的信号受到自动增益控制的限制，从而迫使并发信号相互依赖。受限结果的研究主要属于成分数据分析领域。我们通过实验证明同量异序标签蛋白质组数据本质上是组成的，并强调了数据分析和解释的含义。我们提出了一种新的统计模型和配套软件，可以提高估计准确性和检测蛋白质丰度变化的能力。最后，我们证明了对具有无限变化的蛋白质的独特组成效应。我们的结论是，许多无限的变化看起来很小，并且这些估计的幅度高度依赖于实验设计。

更新日期：2017-12-15

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