Monitoring of system conditioning after blank injections in untargeted UPLC-MS metabolomic analysis.
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SUBMITTER: Martinez-Sena T
PROVIDER: S-EPMC6614502 | biostudies-literature | 2019 Jul
REPOSITORIES: biostudies-literature
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Monitoring of system conditioning after blank injections in untargeted UPLC-MS metabolomic analysis.
Martínez-Sena Teresa T Luongo Giovanna G Sanjuan-Herráez Daniel D Castell José V JV Vento Máximo M Quintás Guillermo G Kuligowski Julia J
Scientific reports 20190708 1
Ultra-performance liquid chromatography - mass spectrometry (UPLC-MS) is widely used for untargeted metabolomics in biomedical research. To optimize the quality and precision of UPLC-MS metabolomic analysis, evaluation of blank samples for the elimination of background features is required. Although blanks are usually run either at the beginning or at the end of a sequence of samples, a systematic analysis of their effect of the instrument performance has not been properly documented. Using the ...[more]
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Project description:Ultra-performance liquid chromatography - mass spectrometry (UPLC-MS) is widely used for untargeted metabolomics in biomedical research. To optimize the quality and precision of UPLC-MS metabolomic analysis, evaluation of blank samples for the elimination of background features is required. Although blanks are usually run either at the beginning or at the end of a sequence of samples, a systematic analysis of their effect of the instrument performance has not been properly documented. Using the analysis of two common bio-fluids (plasma and urine), we describe how the injection of blank samples within a sequence of samples may affect both the chromatographic and MS detection performance depending on several factors, including the sample matrix and the physicochemical properties of the metabolites of interest. The analysis of blanks and post-blank conditioning samples using t-tests, PCA and guided-PCA provides useful information for the elimination of background UPLC-MS features, the identification of column carry over and the selection of the number of samples required to achieve a stable performance.
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