A novel metabolite identification strategy is presented for the combined NMR/MS analysis of complex metabolite mixtures. The approach first identifies metabolite candidates from 1D or 2D NMR spectra by NMR database query, which is followed by the determination of the masses (m/z) of their possible ions, adducts, fragments, and characteristic isotope distributions. The expected m/z ratios are then compared with the MS(1) spectrum for the direct assignment of those signals of the mass spectrum that contain information about the same metabolites as the NMR spectra. In this way, the mass spectrum can be assigned with very high confidence, and it provides at the same time validation of the NMR-derived metabolites. The method was first demonstrated on a model mixture, and it was then applied to human urine collected from a pool of healthy individuals. A number of metabolites could be detected that had not been reported previously, further extending the list of known urine metabolites. The new analysis approach, which is termed NMR/MS Translator, is fully automated and takes only a few seconds on a computer workstation. NMR/MS Translator synergistically uses the power of NMR and MS, enhancing the accuracy and efficiency of the identification of those metabolites compiled in databases.
Keywords: Metabolomics/metabonomics; NMR and MS hybrid approach; complex mixture analysis; human urine metabolome.