School of Oceanography, University of Washington , Seattle, Washington, United States.
Anal Chem. 2018 Jan 16;90(2):1363-1369. doi: 10.1021/acs.analchem.7b04400. Epub 2018 Jan 3.
The goal of metabolomics is to measure the entire range of small organic molecules in biological samples. In liquid chromatography-mass spectrometry-based metabolomics, formidable analytical challenges remain in removing the nonbiological factors that affect chromatographic peak areas. These factors include sample matrix-induced ion suppression, chromatographic quality, and analytical drift. The combination of these factors is referred to as obscuring variation. Some metabolomics samples can exhibit intense obscuring variation due to matrix-induced ion suppression, rendering large amounts of data unreliable and difficult to interpret. Existing normalization techniques have limited applicability to these sample types. Here we present a data normalization method to minimize the effects of obscuring variation. We normalize peak areas using a batch-specific normalization process, which matches measured metabolites with isotope-labeled internal standards that behave similarly during the analysis. This method, called best-matched internal standard (B-MIS) normalization, can be applied to targeted or untargeted metabolomics data sets and yields relative concentrations. We evaluate and demonstrate the utility of B-MIS normalization using marine environmental samples and laboratory grown cultures of phytoplankton. In untargeted analyses, B-MIS normalization allowed for inclusion of mass features in downstream analyses that would have been considered unreliable without normalization due to obscuring variation. B-MIS normalization for targeted or untargeted metabolomics is freely available at https://github.com/IngallsLabUW/B-MIS-normalization .
代谢组学的目标是测量生物样本中所有小分子有机化合物。在基于液相色谱-质谱的代谢组学中，在去除影响色谱峰面积的非生物因素方面仍然存在艰巨的分析挑战。这些因素包括样品基质诱导的离子抑制、色谱质量和分析漂移。这些因素的组合被称为“掩盖性变异”。一些代谢组学样本由于基质诱导的离子抑制会表现出强烈的掩盖性变异，使得大量数据不可靠且难以解释。现有的归一化技术对这些样本类型的适用性有限。在这里，我们提出了一种数据归一化方法来最小化掩盖性变异的影响。我们使用批次特异性归一化过程对峰面积进行归一化，该过程将测量的代谢物与在分析过程中表现相似的同位素标记内部标准相匹配。这种方法称为“最佳匹配内部标准（B-MIS）归一化”，可应用于靶向或非靶向代谢组学数据集，并得出相对浓度。我们使用海洋环境样本和实验室培养的浮游植物来评估和演示 B-MIS 归一化的实用性。在非靶向分析中，B-MIS 归一化允许将质量特征纳入下游分析，否则由于掩盖性变异，这些质量特征在没有归一化的情况下将被认为是不可靠的。B-MIS 归一化用于靶向或非靶向代谢组学，可在 https://github.com/IngallsLabUW/B-MIS-normalization 上免费获得。
