Data correction strategy for metabolomics analysis using gas chromatography-mass spectrometry
Data correction strategy for metabolomics analysis using gas chromatography-mass spectrometry
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DOI：
10.1016/j.ymben.2006.08.001
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发表时间：
2007-01-01
影响因子：
8.4
通讯作者：
Klapa, Maria I.
中科院分区：
文献类型：
--
作者：
Kanani, Harin H.;Klapa, Maria I.
气相色谱-质谱代谢组学需要对原始样品进行衍生化。因此，必须考虑可能扭曲原始代谢物浓度和衍生峰面积曲线之间一对一比例关系的系统偏差。第一种类型的偏差仅改变样品中两个分布图之间的比例常数值，并通过使用内标进行校正。然而，第二种类型可能会扭曲一对一的关系，并且还会将样品中两个分布之间的比例常数改变为每种代谢物的不同折叠程度。代谢组学谱应该纠正这些偏差，因为仅由化学动力学引起的变化就可以被赋予生物学意义。本文提出了第一个简化的数据校正和验证策略，该策略不会损害代谢组分析的高通量性质。此背景还允许对 15 个目前未知的含 (NH2) 基团的化合物的衍生峰进行化学注释。 (C) 2006 Elsevier Inc. 保留所有权利。
Gas chromatography-mass spectrometry metabolomics requires the original sample's derivatization. Therefore, systematic biases that might distort the one-to-one proportional relationship between the original metabolite concentration and derivative peak area profiles have to be considered. The first type of such biases change only the value of the proportionality constant between the two profiles among samples and are corrected by the use of an internal standard. The second type, however, might distort the one-to-one relationship and also change the proportionality constant between the two profiles among samples to a different fold-extent for each metabolite. Metabolomic profiles should be corrected from these biases, because changes due only to chemical kinetics could be assigned biological significance. This paper presents the first streamlined data correction and validation strategy that does not jeopardize the high-throughput nature of metabolomic analysis. This context allowed also for the chemical annotation of 15 currently unknown derivative peaks of (NH2)-group containing compounds. (C) 2006 Elsevier Inc. All rights reserved.