Mass Spectrometry-Based Metabolomics: Accelerating the Characterization of Discriminating Signals by Combining Statistical Correlations and Ultrahigh Resolution

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• 作者：Erwan Werner ; Vincent Croixmarie ; Thierry Umbdenstock ; Eric Ezan ; Pierre Chaminade ; Jean-Claude Tabet ; Christophe Junot
• 刊名：Analytical Chemistry
• 出版年：2008
• 出版时间：July 1, 2008
• 年：2008
• 卷：80
• 期：13
• 页码：4918 - 4932
• 全文大小：2485K
• 年卷期：v.80,no.13(July 1, 2008)
• ISSN：1520-6882

文摘

A strategy combining autocorrelation matrices and ultrahigh resolution mass spectrometry (MS) was developed to optimize the characterization of discriminating ions highlighted by metabolomics. As an example, urine samples from rats treated with phenobarbital (PB) were analyzed by ultrahigh-pressure chromatography with two different eluting conditions coupled to time-of-flight mass spectrometric detection in both the positive and negative electrospray ionization modes. Multivariate data analyses were performed to highlight discriminating variables from several thousand detected signals: a few hundred signals were found to be affected by PB, whereas a few tenths of them were linked to its metabolism. Autocorrelation matrices were then applied to eliminate adduct and fragment ions. Finally, the characterization of the ions of interest was performed with ultrahigh-resolution mass spectrometry and sequential MSⁿ experiments, by using a LC-LTQ-Orbitrap system. The use of different eluting conditions was shown to drastically impact on the chromatographic retention and ionization of compounds, thus providing a way to obtain more exhaustive metabolic fingerprints, whereas autocorrelation matrices allowed one to focus the identification work on the most relevant ions. By using such an approach, 14 PB metabolites were characterized in rat urines, some of which have not been reported in the literature.