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Metabolomics

, Volume 2, Issue 4, pp 221–233 | Cite as

Automated screening for metabolites in complex mixtures using 2D COSY NMR spectroscopy

  • Yuanxin Xi
  • Jeffrey S. de Ropp
  • Mark R. Viant
  • David L. Woodruff
  • Ping Yu
Article

Abstract

One of the greatest challenges in metabolomics is the rapid and unambiguous identification and quantification of metabolites in a biological sample. Although one-dimensional (1D) proton nuclear magnetic resonance (NMR) spectra can be acquired rapidly, they are complicated by severe peak overlap that can significantly hinder the automated identification and quantification of metabolites. Furthermore, it is currently not reasonable to assume that NMR spectra of pure metabolites are available a priori for every metabolite in a biological sample. In this paper we develop and report on tests of methods that assist in the automatic identification of metabolites using proton two-dimensional (2D) correlation spectroscopy (COSY) NMR. Given a database of 2D COSY spectra for the metabolites of interest, our methods provide a list sorted by a heuristic likelihood of the metabolites present in a sample that has been analyzed using 2D COSY NMR. Our models attempt to correct the displacement of the peaks that can occur from one sample to the next, due to pH, temperature and matrix effects, using a statistical and chemical model. The correction of one peak can result in an implied correction of others due to spin–spin coupling. Furthermore, these displacements are not independent: they depend on the relative position of functional groups in the molecule. We report experimental results using defined mixtures of amino acids as well as real complex biological samples that demonstrate that our methods can be very effective at automatically and rapidly identifying metabolites.

Keywords

metabolomics metabolite identification 2D COSY NMR pH 

Notes

Acknowledgments

This publication was made possible in part by grant number 5 P42 ES04699 from the National Institute of Environmental Health Sciences, NIH. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS, NIH. MRV thanks the Natural Environment Research Council, UK, for the award of an Advanced Fellowship (NER/J/S/2002/00618). The authors are indebted to Chenomx for the use of their NMR Suite software.

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Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Yuanxin Xi
    • 1
  • Jeffrey S. de Ropp
    • 2
  • Mark R. Viant
    • 3
  • David L. Woodruff
    • 4
  • Ping Yu
    • 2
  1. 1.Department of Applied ScienceUniversity of CaliforniaDavisUSA
  2. 2.NMR FacilityUniversity of CaliforniaDavisUSA
  3. 3.School of BiosciencesThe University of BirminghamEdgbastonUK
  4. 4.Institute for Data Analysis and Visualization and Graduate School of ManagementUniversity of CaliforniaDavisUSA

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