Extraction and GC/MS Analysis of the Human Blood Plasma Metabolome

Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of expe...

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Veröffentlicht in:Analytical chemistry (Washington) 2005-12, Vol.77 (24), p.8086-8094
Hauptverfasser: A, Jiye, Trygg, Johan, Gullberg, Jonas, Johansson, Annika I, Jonsson, Pär, Antti, Henrik, Marklund, Stefan L, Moritz, Thomas
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Sprache:eng
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Zusammenfassung:Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of experiment), for analyzing the human blood plasma metabolome by GC/MS. The protocol was optimized by evaluating the data for more than 500 resolved peaks using multivariate statistical tools including principal component analysis and partial least-squares projections to latent structures (PLS). The performance of five organic solvents (methanol, ethanol, acetonitrile, acetone, chloroform), singly and in combination, was investigated to optimize the LMC extraction. PLS analysis demonstrated that methanol extraction was particularly efficient and highly reproducible. The extraction and derivatization conditions were also optimized. Quantitative data for 32 endogenous compounds showed good precision and linearity. In addition, the determined amounts of eight selected compounds agreed well with analyses by independent methods in accredited laboratories, and most of the compounds could be detected at absolute levels of ∼0.1 pmol injected, corresponding to plasma concentrations between 0.1 and 1 μM. The results suggest that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.
ISSN:0003-2700
1520-6882
DOI:10.1021/ac051211v