Development of Spectral Libraries, Predictive Software, and UHPLC-MS-SPE-NMR Technologies for Large-Scale Plant Metabolite Identification
CEN Webinars: Stronger Bonds
Development of Spectral Libraries, Predictive Software, and UHPLC-MS-SPE-NMR Technologies for Large-Scale Plant Metabolite Identification
Wednesday August 12th 2015
USA 8:00 a.m. PDT / 11:00 a.m. EDT / 16:00 BST
Lloyd W. Sumner, Ph.D.,
Professor, Plant Biology Division
The Samuel Roberts Noble Foundation
Linda Wang,
Senior Editor,
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Integrated metabolomics is a revolutionary systems biology tool for understanding plant metabolism and elucidating gene function. Although the vast utility of metabolomics is well documented in the literature, its full scientific promise has not yet been realized due to multiple technical challenges. The number one, grand challenge of metabolomics is the large-scale confident chemical identification of metabolites. To address this challenge, we have developed tandem mass spectral libraries, powerful custom software entitled 'Plant Metabolite Annotation Toolbox' (PlantMAT) and a sophisticated ensemble composed of Ultrahigh pressure liquid chromatography coupled to mass spectrometry coupled to automated solid-phase extraction and NMR (UHPLC-MS-SPE-NMR) for the large-scale systematic and biological directed annotation of plant metabolomes.

The initial step in annotation involves dereplication, or the identification of known metabolites by comparing the mass spectra of analytes in the sample with the mass spectra of known compounds. Thus, MS and MS/MS libraries were constructed using a UHPLC coupled to a Bruker Impact HD QToFMS/MS and containing spectra from 222 authentic compounds. These libraries were used to identify approximately 100 metabolites in the Medicago truncatula extracts. The utility of these libraries to identify metabolites in plant extracts analysed on similar systems was also tested and it was that found that over 80% of metabolites could be identified using the custom libraries on similar UHPLC-QTofMS/MS instruments. Unfortunately, authentic compounds are not available for all metabolites; especially plant natural products, and a large number of the M. truncatula detected peaks could not be identified using spectral matching. Thus, UHPLC-MS/MS data were imported into custom PlantMAT software and structures for approximately 100 saponins and polyphenolic glycosides were predicted. The accuracy of these predictions were then validated by NMR. Targeted compounds were isolated, purified and concentrated by mass directed UHPLC-MS-SPE. The SPE isolated compounds were eluted with deuterated methanol and 1D and 2D NMR spectra acquired. The NMR spectral data confirmed a 100% accuracy in the PlantMAT predicted structures. To date, UHPLC-MS-SPE-NMR has been used to identify approximately 71 M. truncatula metabolites. The results demonstrate that the cumulative computational and empirical platforms allow for higher-throughput and high confidence metabolite identifications necessary for metabolome 'sequencing'.


- Known and unknown ID in Metabolomics research

- MS & MS/MS library construction

- Computational prediction of metabolite structures

- Higher-throughput metabolite identification by UHPLC-MS-SPE-NMR

  • • MS practitioners
  • • Metabolomics Researchers
  • • Researchers/ R&D Managers
  • • Laboratory Managers / Directors / Supervisors
  • • Laboratory Technicians / Operators
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