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Structural elucidation of low abundant metabolites in complex sample matrices

期刊

METABOLOMICS
卷 9, 期 5, 页码 1009-1018

出版社

SPRINGER
DOI: 10.1007/s11306-013-0519-8

关键词

Automation; Metabolite annotation; Metabolite identification; MS fragmentation; MSn; NMR; SPE-NMR; Structure elucidation

资金

  1. Netherlands Metabolomics Centre
  2. Centre for BioSystems Genomics

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Identification of metabolites is a major challenge in biological studies and relies in principle on mass spectrometry (MS) and nuclear magnetic resonance (NMR) methods. The increased sensitivity and stability of both NMR and MS systems have made dereplication of complex biological samples feasible. Metabolic databases can be of help in the identification process. Nonetheless, there is still a lack of adequate spectral databases that contain high quality spectra, but new developments in this area will assist in the (semi-)automated identification process in the near future. Here, we discuss new developments for the structural elucidation of low abundant metabolites present in complex sample matrices. We describe how a recently developed combination of high resolution MS multistage fragmentation (MS (n) ) and high resolution one dimensional (1D)-proton (H-1)-NMR of liquid chromatography coupled to solid phase extraction (LC-SPE) purified metabolites can circumvent the need for isolating extensive amounts of the compounds of interest to elucidate their structures. The LC-MS-SPE-NMR hardware configuration in conjunction with high quality databases facilitates complete structural elucidation of metabolites even at sub-microgram levels of compound in crude extracts. However, progress is still required to optimally exploit the power of an integrated MS and NMR approach. Especially, there is a need to improve and expand both MS (n) and NMR spectral databases. Adequate and user-friendly software is required to assist in candidate selection based on the comparison of acquired MS and NMR spectral information with reference data. It is foreseen that these focal points will contribute to a better transfer and exploitation of structural information gained from diverse analytical platforms.

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