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Dithranol as a Matrix for Matrix Assisted Laser Desorption/Ionization Imaging on a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer
Published on: November 26, 2013
Quantitative detection of metabolites using matrix-assisted laser desorption/ionization mass spectrometry with
Seetharaman Vaidyanathan1, Royston Goodacre
1School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. S.Vaidyanathan@manchester.ac.uk
Abstract:
Quantitative detection of metabolites is a highly desirable feature in metabolome analyses. Recently, the successful detection of multiple metabolites using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in the negative ion mode employing 9-aminoacridine as the organic matrix was reported (Edwards JL, Kennedy RT. Anal. Chem. 2005; 77: 2201-2209). However, there is little information available on quantitative detection of multiple metabolites using MALDI-MS and in particular the influence changes in metabolite levels have on such detections. We investigated this aspect by spiking a synthetic metabolite cocktail (consisting of 39 metabolites including amino acids, organic acids and phospho-metabolites) with five representative metabolites at increasing concentrations, one metabolite at a time, and assessed the signals from replicate determinations. It was possible to detect quantitative changes in the spiked metabolites. Although analyte suppression was observed, it was possible to observe scenarios where the spiked metabolite had little or no influence on the quantitative detection of some metabolites. It appears that the mass spectral response of the metabolite is suppressed only when the spiked chemical species are relatively similar in chemical terms. This suggests that quantitation is possible in scenarios where changes in a specific metabolite or a class of metabolites are monitored following appropriate analyte separation strategies, and that careful interpretations must be made when using the technique for quantitative analysis in unbiased metabolomic approaches.
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