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- Broeckling, Corey D., et al.
(författare)
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Assigning precursor-product ion relationships in indiscriminant MS/MS data from non-targeted metabolite profiling studies
- 2013
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 9:1, s. 33-43
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Tidskriftsartikel (refereegranskat)abstract
- Tandem mass spectrometry using precursor ion selection (MS/MS) is an invaluable tool for structural elucidation of small molecules. In non-targeted metabolite profiling studies, instrument duty cycle limitations and experimental costs have driven efforts towards alternate approaches. Recently, researchers have begun to explore methods for collecting indiscriminant MS/MS (idMS/MS) data in which the fragmentation process does not involve precursor ion isolation. While this approach has many advantages, importantly speed, sensitivity and coverage, confident assignment of precursor-product ion relationships is challenging, which has inhibited broad adoption of the technique. Here, we present an approach that uses open source software to improve the assignment of precursor-product relationships in idMS/MS data by appending a dataset-wide correlational analysis to existing tools. The utility of the approach was demonstrated using a dataset of standard compounds spiked into a malt-barley background, as well as unspiked human serum. The workflow was able to recreate idMS/MS spectra which are highly similar to standard MS/MS spectra of authentic standards, even in the presence of a complex matrix background. The application of this approach has the potential to generate high quality idMS/MS spectra for each detectable molecular feature, which will streamline the identification process for non-targeted metabolite profiling studies.
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| 2. |
- Kumar, Jitender, et al.
(författare)
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Influence of Biological and Technical Covariates on Non-targeted Metabolite Profiling in a Large-scale Epidemiological Study
- 2013
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Ingår i: Current Metabolomics. - 2213-235X. ; 1:3, s. 220-226-
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Tidskriftsartikel (refereegranskat)abstract
- Non-targeted metabolite profiling using ultra performance liquid chromatography-mass spectrometry (UPLCMS) was performed as part of a large-scale epidemiological study involving biobanked serum samples. The influence of both biological (age and body mass index) and technical (season of sample collection, fasting time, handling time, and storage time) covariates on the analysis was assessed. Statistical models including different sets of these covariates were compared and the results illustrate that variation in which covariates were included did not have an appreciable effect on the number or composition of biologically significant metabolite features associated with body mass index or age. Furthermore, when all covariates were included in the model, there was little overlap of metabolite features significantly associated with the different covariates. Thus, the results of this study illustrate that while some of the observed quantitative variance of metabolite features can be explained by biological and technical covariates, the use of non-targeted metabolite profiling of serum by UPLC-MS is valid for studies of biological outcomes in biobanked clinical samples from large-scale studies. - See more at: http://www.eurekaselect.com/115259/article#sthash.BOvtwWe7.dpuf
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