Towards quantitative metabolome analysis
© Stein et al; licensee BioMed Central Ltd. 2007
Published: 8 May 2007
Meanwhile mass spectrometry serves as essential methodology for metabolome analysis in bacterial, mammalian and plant systems. Nevertheless, quantitative approaches are complicated to realise, because unknown amounts of metabolites are often lost during sample preparation. Recovery is difficult to acquire due to the lack of "blank" cell extracts. Therefore, methods like standard addition or isotope dilution – where stable isotopes of an analyte are used as internal standards – were introduced to bacterial metabolome analysis. Thus, there is an increasing demand for the quantification of different labelling states of metabolic intermediates. Methods to meet these demands have been developed in our group. They have been applied successfully to the analysis of cell extracts from various biological sources such as bacterial and mammalian cells.
Strong distinctions in metabolite concentrations were found with the quantification strategies applied. For dilutions ranging from 1:2 to 1:20 internal calibration and isotope dilution mass spectrometry were found to be most suitable for the compensation of matrix effects. Further more, the results showed that U13C4-aspartate as internal standard could be used for various amino acids in the chromatographic run. Using isotope dilution, where each metabolite has its own labelled analogue, the linear range of the calibration curve and the coefficient of correlation (up to 0.9999) were improved. The procedure of standard addition was only necessary for very low concentrated metabolites. To avoid this time consuming procedure, samples can as well be concentrated and measured via internal calibration instead. External calibration was sufficient for biological samples in dilutions of 1:20 or higher, because the measurements were hardly influenced by matrix effects.
Materials and methods
Measuring accurate intracellular concentrations is essential for the reflection of metabolic changes after physiological stimuli or genetic modifications of an organism. Especially for anabolic reaction sequences the identification of rate limiting steps is very important to identify possible metabolic engineering targets in production processes. Thus, the here presented approach builds the basis for modeling of in vivo enzyme kinetics, metabolic fluxes or thermodynamic states.
This article is published under license to BioMed Central Ltd.