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. 2006 May 15;78(10):3289-95.
doi: 10.1021/ac060245f.

Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum

Affiliations

Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum

Anders Nordström et al. Anal Chem. .

Abstract

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.

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Figures

Figure 1
Figure 1
Workflow scheme, the acquired data is converted to CDF format. The CDF files are subsequently processed using XCMS. The output from XCMS is data aligned data which can be viewed as picture files and also as a result matrix where samples (observations) are making up the columns and the features (variables) constitutes the rows. The features are (in this case 2711) are normalized and sorted according to p-value obtained through a t-test. Features that display significant difference (p<0.05) between sample class A and B are subject to re-integration in the raw data. The re-integrated areas are again normalized and sorted according to p-value, and compounds with p<0.05 constitutes the final table of metabolites that are different between sample A and B.
Figure 2
Figure 2
A. Signal to noise ratio (S/N) for the spiked compounds. Black and white bars are S/N measured with UPLC and HPLC respectively. Error bars are showing standard deviation (n=5). B. Extracted ion chromatograms (EIC) and spectra for selected spiked compounds.
Figure 3
Figure 3
Effect of normalization the top graph display areas for feature M430T608 (Mifepristone) as integrated by XCMS. Letters A and B and the numbers corresponds to the injection replicates of respective spiking mixture. The bottom graph show the normalized area values for the same feature.

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