Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics:: Quantitative analysis of endogenous and exogenous metabolites in human serum
Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics:: Quantitative analysis of endogenous and exogenous metabolites in human serum
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DOI：
10.1021/ac060245f
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发表时间：
2006-05-15
影响因子：
7.4
通讯作者：
Siuzdak, Gary
中科院分区：
文献类型：
--
作者：
Nordstrom, Anders;O'Maille, Grace;Siuzdak, Gary
一个非线性比对策略进行了检查，用于血清代谢物的定量分析。将10种化合物的相对浓度差异为20-100%的两种小分子混合物加入人血清中。使用UPLC和XCMS进行LC-MS数据比对的代谢组学方案可以在检测到的2700多个特征中容易地识别10个加标差异中的8个。通过对加标标准品的XCMS积分响应面积求平均值获得的单因子数据归一化增加了鉴别差异的数量。使用XCMS可以很好地保留原始数据结构，但重新整合原始数据中已识别的差异可以减少误报的数量。与HPLC相比，使用UPLC进行分离导致检测到的组分多20%。色谱分离的长度也被证明是一个关键参数的检测功能的数量。此外，与HPLC相比，UPLC显示出更好的保留时间重现性和加标化合物的信噪比，使该技术更适合非靶向代谢组学应用。
A nonlinear alignment strategy was examined for the quantitative analysis of serum metabolites. Two small-molecule mixtures with a difference in relative concentration of 20-100% for 10 of the compounds were added to human serum. The metabolomics protocol using UPLC and XCMS for LC-MS data alignment could readily identify 8 of 10 spiked differences among more than 2700 features detected. Normalization of data against a single factor obtained through averaging the XCMS integrated response areas of spiked standards increased the number of identified differences. The original data structure was well preserved using XCMS, but reintegration of identified differences in the original data reduced the number of false positives. Using UPLC for separation resulted in 20% more detected components compared to HPLC. The length of the chromatographic separation also proved to be a crucial parameter for a number of detected features. Moreover, UPLC displayed better retention time reproducibility and signal-to-noise ratios for spiked compounds over HPLC, making this technology more suitable for nontargeted metabolomics applications.