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Automated Reproducible Titania-Based Phosphopeptide Enrichment Strategy for Label-Free Quantitative Phosphoproteomics

机译:自动化的可重复的基于二氧化钛的磷酸肽富集策略用于无标签的定量磷酸化蛋白质组学

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摘要

An automated phosphopeptide enrichment strategy is described using titanium dioxide (TiO2)-packed, fused silica capillaries for use with liquid chromatography (LC)-mass spectrometry (MS)/MS-based, label-free proteomics workflows. To correlate an optimum peptide:TiO2 loading ratio between different particle types, the ratio of phenyl phosphate-binding capacities was used. The optimum loading for the column was then verified through replicate enrichments of a range of quantities of digested rat brain tissue cell lysate. Fractions were taken during sample loading, multiple wash steps, and the elution steps and analyzed by LC-MS/MS to gauge the efficiency and reproducibility of the enrichment. Greater than 96% of the total phosphopeptides were detected in the elution fractions, indicating efficient trapping of the phosphopeptides on the first pass of enrichment. The quantitative reproducibility of the automated setup was also improved greatly with phosphopeptide intensities from replicate enrichments exhibiting a median coefficient of variation (CV) of 5.8%, and 80% of the identified phosphopeptides had CVs below 11.1%, while maintaining >85% specificity. By providing this high degree of analytical reproducibility, this method allows for label-free phosphoproteomics over large sample sets with complex experimental designs (multiple biological conditions, multiple biological replicates, multiple time-points, etc.), including large-scale clinical cohorts.
机译:描述了一种自动磷酸肽富集策略,该策略使用装有二氧化钛(TiO2)的熔融石英毛细管与液相色谱(LC)-质谱(MS)/ MS,无标记蛋白质组学工作流程配合使用。为了关联不同颗粒类型之间的最佳肽:TiO2负载比,使用了磷酸苯酯结合能力之比。然后通过重复富集一定数量的消化的大鼠脑组织细胞裂解液来验证色谱柱的最佳负载量。在样品上样,多个洗涤步骤和洗脱步骤中提取馏分,并通过LC-MS / MS分析以评估富集的效率和重现性。在洗脱级分中检测到超过96%的总磷酸肽,表明在富集的第一遍有效捕获了磷酸肽。由于重复富集的磷酸肽强度显示出5.8%的中位变异系数(CV),并且80%的鉴定出的磷酸肽的CV低于11.1%,同时仍保持> 85%的特异性,因此自动化设置的定量重现性也得到了极大提高。通过提供高度的分析重现性,该方法可在具有复杂实验设计(多种生物学条件,多种生物学重复,多个时间点等)的大型样品组上实现无标记的磷酸化蛋白质组学,包括大规模的临床队列研究。

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