Alghanem Bandar, Nikitin Frédéric, Stricker Thomas, Duchoslav Eva, Luban Jeremy, Strambio-De-Castillia Caterina, Muller Markus, Lisacek Frédérique, Varesio Emmanuel, Hopfgartner Gérard
Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, Geneva, Switzerland.
King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
Rapid Commun Mass Spectrom. 2017 May 15;31(9):753-761. doi: 10.1002/rcm.7839.
In peptide quantification by liquid chromatography/mass spectrometry (LC/MS), the optimization of multiple reaction monitoring (MRM) parameters is essential for sensitive detection. We have compared different approaches to build MRM assays, based either on flow injection analysis (FIA) of isotopically labelled peptides, or on the knowledge and the prediction of the best settings for MRM transitions and collision energies (CE). In this context, we introduce MRMOptimizer, an open-source software tool that processes spectra and assists the user in selecting transitions in the FIA workflow.
MS/MS spectral libraries with CE voltages from 10 to 70 V are automatically acquired in FIA mode for isotopically labelled peptides. Then MRMOptimizer determines the optimal MRM settings for each peptide. To assess the quantitative performance of our approach, 155 peptides, representing 84 proteins, were analysed by LC/MRM-MS and the peak areas were compared between: (A) the MRMOptimizer-based workflow, (B1) the SRMAtlas transitions set used 'as-is'; (B2) the same SRMAtlas set with CE parameters optimized by Skyline.
51% of the three most intense transitions per peptide were shown to be common to both A and B1/B2 methods, and displayed similar sensitivity and peak area distributions. The peak areas obtained with MRMOptimizer for transitions sharing either the precursor ion charge state or the fragment ions with the SRMAtlas set at unique transitions were increased 1.8- to 2.3-fold. The gain in sensitivity using MRMOptimizer for transitions with different precursor ion charge state and fragment ions (8% of the total), reaches a ~ 11-fold increase.
Isotopically labelled peptides can be used to optimize MRM transitions more efficiently in FIA than by searching databases. The MRMOptimizer software is MS independent and enables the post-acquisition selection of MRM parameters. Coefficients of variation for optimal CE values are lower than those obtained with the SRMAtlas approach (B2) and one additional peptide was detected. Copyright © 2017 John Wiley & Sons, Ltd.
在通过液相色谱/质谱联用（LC/MS）进行肽定量分析时，优化多反应监测（MRM）参数对于灵敏检测至关重要。我们比较了不同的构建MRM分析方法，一种基于同位素标记肽的流动注射分析（FIA），另一种基于对MRM跃迁和碰撞能量（CE）最佳设置的了解和预测。在此背景下，我们引入了MRMOptimizer，这是一款开源软件工具，可处理光谱并协助用户在FIA工作流程中选择跃迁。
在FIA模式下自动获取同位素标记肽的CE电压为10至70V的MS/MS光谱库。然后MRMOptimizer为每种肽确定最佳MRM设置。为了评估我们方法的定量性能，通过LC/MRM-MS分析了代表84种蛋白质的155种肽，并比较了以下之间的峰面积：（A）基于MRMOptimizer的工作流程，（B1）直接使用的SRMAtlas跃迁集；（B2）通过Skyline优化CE参数的相同SRMAtlas集。
每种肽的三个最强跃迁中有51%显示在A方法与B1/B2方法中是相同的，并且显示出相似的灵敏度和峰面积分布。对于与SRMAtlas集在唯一跃迁处共享前体离子电荷状态或碎片离子的跃迁，使用MRMOptimizer获得的峰面积增加了1.8至2.3倍。对于具有不同前体离子电荷状态和碎片离子的跃迁（占总数的8%），使用MRMOptimizer的灵敏度提高达到约11倍。
与搜索数据库相比，同位素标记肽可用于在FIA中更有效地优化MRM跃迁。MRMOptimizer软件独立于质谱仪，能够在采集后选择MRM参数。最佳CE值的变异系数低于使用SRMAtlas方法（B2）获得的变异系数，并且检测到了另外一种肽。版权所有©2017约翰威立父子有限公司。
