Matrix Effects in GC-MS Profiling of Common Metabolites after Trimethylsilyl Derivatization.

Autor: Tarakhovskaya E; Department of Plant Physiology and Biochemistry, Faculty of Biology, St. Petersburg State University, 199034 St. Petersburg, Russia.; Vavilov Institute of General Genetics RAS, St. Petersburg Branch, 199034 St. Petersburg, Russia., Marcillo A; Mass Spectrometry Research Group, Faculty of Chemistry and Mineralogy, Leipzig University, 04103 Leipzig, Germany.; Institute of Energy and Climate Research (IEK-8), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany., Davis C; Mass Spectrometry Research Group, Faculty of Chemistry and Mineralogy, Leipzig University, 04103 Leipzig, Germany.; Waters GmbH, 1130 Vienna, Austria., Milkovska-Stamenova S; Bioanalytics Research Group, Faculty of Chemistry and Mineralogy, Leipzig University, 04103 Leipzig, Germany.; AP Diagnostics GmbH, 04103 Leipzig, Germany., Hutschenreuther A; Mass Spectrometry Research Group, Faculty of Chemistry and Mineralogy, Leipzig University, 04103 Leipzig, Germany., Birkemeyer C; Mass Spectrometry Research Group, Faculty of Chemistry and Mineralogy, Leipzig University, 04103 Leipzig, Germany.; German Center for Integrative Biodiversity Research (iDiv) Halle-Leipzig-Jena, 04103 Leipzig, Germany.
Jazyk: angličtina
Zdroj: Molecules (Basel, Switzerland) [Molecules] 2023 Mar 15; Vol. 28 (6). Date of Electronic Publication: 2023 Mar 15.
DOI: 10.3390/molecules28062653
Abstrakt: Metabolite profiling using gas chromatography coupled to mass spectrometry (GC-MS) is one of the most frequently applied and standardized methods in research projects using metabolomics to analyze complex samples. However, more than 20 years after the introduction of non-targeted approaches using GC-MS, there are still unsolved challenges to accurate quantification in such investigations. One particularly difficult aspect in this respect is the occurrence of sample-dependent matrix effects. In this project, we used model compound mixtures of different compositions to simplify the study of the complex interactions between common constituents of biological samples in more detail and subjected those to a frequently applied derivatization protocol for GC-MS analysis, namely trimethylsilylation. We found matrix effects as signal suppression and enhancement of carbohydrates and organic acids not to exceed a factor of ~2, while amino acids can be more affected. Our results suggest that the main reason for our observations may be an incomplete transfer of carbohydrate and organic acid derivatives during the injection process and compound interaction at the start of the separation process. The observed effects were reduced at higher target compound concentrations and by using a more suitable injection-liner geometry.
Databáze: MEDLINE
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