Nonlinear Regression Improves Accuracy of Characterization of Multiplexed Mass Spectrometric Assays
Nonlinear Regression Improves Accuracy of Characterization of Multiplexed Mass Spectrometric Assays
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DOI：
10.1074/mcp.ra117.000322
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发表时间：
2018-05-01
影响因子：
7
通讯作者：
Vitek, Olga
中科院分区：
文献类型：
--
作者：
Galitzine, Cyril;Egertson, Jarrett D.;Vitek, Olga
在基于定量质谱的蛋白质组学中，对分析表征的需求是普遍存在的。在许多测定特性中，空白限 (LOB) 和检测限 (LOD) 是两个特别有用的品质因数。 LOB 和 LOD 是通过重复量化已知肽浓度的样品中观察到的肽强度并得出强度与浓度响应曲线来确定的。最常见的是，加权线性或逻辑曲线适合强度-浓度响应，并根据拟合估计 LOB 和 LOD。在这里，我们认为这些方法不准确地表征了观察到的强度在低浓度时趋于稳定的测定，这是多重系统中的常见情况。本手稿说明了这些方法的缺陷，并提出了一种基于非线性回归的替代方法来克服这些不准确性。我们使用计算机模拟并使用在数据独立采集（DIA）、平行反应监测（PRM）和选择反应监测（SRM）模式下获得的11个实验数据集评估了所提出方法的性能。当强度在低浓度处趋于稳定时，非线性模型会向上更改 LOB/LOD 的估计值，在某些数据集中会向上更改 20-40%。在没有低浓度强度趋于平稳的情况下，通过非线性统计模型获得的 LOB/LOD 估计值与加权线性回归的估计值相同。我们在基于 R 的开源软件 MSstats 中实现了非线性回归方法，并提倡将其普遍用于基于质谱分析的表征。
The need for assay characterization is ubiquitous in quantitative mass spectrometry-based proteomics. Among many assay characteristics, the limit of blank (LOB) and limit of detection (LOD) are two particularly useful figures of merit. LOB and LOD are determined by repeatedly quantifying the observed intensities of peptides in samples with known peptide concentrations and deriving an intensity versus concentration response curve. Most commonly, a weighted linear or logistic curve is fit to the intensity-concentration response, and LOB and LOD are estimated from the fit. Here we argue that these methods inaccurately characterize assays where observed intensities level off at low concentrations, which is a common situation in multiplexed systems. This manuscript illustrates the deficiencies of these methods, and proposes an alternative approach based on nonlinear regression that overcomes these inaccuracies. We evaluated the performance of the proposed method using computer simulations and using eleven experimental data sets acquired in Data-Independent Acquisition (DIA), Parallel Reaction Monitoring (PRM), and Selected Reaction Monitoring (SRM) mode. When the intensity levels off at low concentrations, the nonlinear model changes the estimates of LOB/LOD upwards, in some data sets by 20-40%. In absence of a low concentration intensity leveling off, the estimates of LOB/LOD obtained with nonlinear statistical modeling were identical to those of weighted linear regression. We implemented the nonlinear regression approach in the open-source R-based software MSstats, and advocate its general use for characterization of mass spectrometry-based assays.