National Institute of Statistical Sciences, Research Triangle Park, North Carolina, United States of America.
PLoS One. 2011 Jan 26;6(1):e14590. doi: 10.1371/journal.pone.0014590.
In the Addona et al. paper (Nature Biotechnology 2009), a large-scale multi-site study was performed to quantify Multiple Reaction Monitoring (MRM) measurements of proteins spiked in human plasma. The unlabeled signature peptides derived from the seven target proteins were measured at nine different concentration levels, and their isotopic counterparts were served as the internal standards.
METHODOLOGY/PRINCIPAL FINDINGS: In this paper, the sources of variation are analyzed by decomposing the variance into parts attributable to specific experimental factors: technical replicates, sites, peptides, transitions within each peptide, and higher-order interaction terms based on carefully built mixed effects models. The factors of peptides and transitions are shown to be major contributors to the variance of the measurements considering heavy (isotopic) peptides alone. For the light ((12)C) peptides alone, in addition to these factors, the factor of study*peptide also contributes significantly to the variance of the measurements. Heterogeneous peptide component models as well as influence analysis identify the outlier peptides in the study, which are then excluded from the analysis. Using a log-log scale transformation and subtracting the heavy/isotopic peptide [internal standard] measurement from the peptide measurements (i.e., taking the logarithm of the peak area ratio in the original scale establishes that), the MRM measurements are overall consistent across laboratories following the same standard operating procedures, and the variance components related to sites, transitions and higher-order interaction terms involving sites have greatly reduced impact. Thus the heavy peptides have been effective in reducing apparent inter-site variability. In addition, the estimates of intercepts and slopes of the calibration curves are calculated for the sub-studies.
CONCLUSIONS/SIGNIFICANCE: The MRM measurements are overall consistent across laboratories following the same standard operating procedures, and heavy peptides can be used as an effective internal standard for reducing apparent inter-site variability. Mixed effects modeling is a valuable tool in mass spectrometry-based proteomics research.
在 Addona 等人的论文（《自然生物技术》2009 年）中，进行了一项大规模的多站点研究，以定量测量人血浆中添加的蛋白质的多重反应监测（MRM）。来自七个目标蛋白质的未标记特征肽在九个不同浓度水平下进行了测量，它们的同位素对应物被用作内标。
方法/主要发现：在本文中，通过将方差分解为归因于特定实验因素的部分来分析变异源：技术重复、地点、肽、每个肽内的跃迁以及基于精心构建的混合效应模型的高阶交互项。考虑到重（同位素）肽单独存在，肽和跃迁因子被证明是测量方差的主要贡献者。对于轻（12C）肽单独存在，除了这些因素外，研究*肽因子也显著影响了测量的方差。异质肽成分模型和影响分析确定了研究中的异常肽，然后将其从分析中排除。使用对数-对数比例转换，并从肽测量值中减去重/同位素肽[内标]测量值（即，在原始比例下取峰面积比的对数确定），MRM 测量值在遵循相同标准操作程序的实验室之间总体上是一致的，并且与地点相关的地点、跃迁和高阶交互项的方差分量的影响大大降低。因此，重肽有效地降低了明显的站点间变异性。此外，还为子研究计算了校准曲线的截距和斜率的估计值。
结论/意义：遵循相同标准操作程序的实验室之间的 MRM 测量值总体上是一致的，重肽可以作为减少明显站点间变异性的有效内标。混合效应模型是基于质谱的蛋白质组学研究中的一种有价值的工具。
