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Identification of metabolites from 2D 1H-13C HSQC NMR using peak correlation plots
Umeå University, Faculty of Science and Technology, Department of Chemistry.
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2014 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 15, 413Article in journal (Refereed) Published
Abstract [en]

Background: Identification of individual components in complex mixtures is an important and sometimes daunting task in several research areas like metabolomics and natural product studies. NMR spectroscopy is an excellent technique for analysis of mixtures of organic compounds and gives a detailed chemical fingerprint of most individual components above the detection limit. For the identification of individual metabolites in metabolomics, correlation or covariance between peaks in 1H NMR spectra has previously been successfully employed. Similar correlation of 2D 1H-13C Heteronuclear Single Quantum Correlation spectra was recently applied to investigate the structure of heparine. In this paper, we demonstrate how a similar approach can be used to identify metabolites in human biofluids (post-prostatic palpation urine).

Results: From 50 1H-13C Heteronuclear Single Quantum Correlation spectra, 23 correlation plots resembling pure metabolites were constructed. The identities of these metabolites were confirmed by comparing the correlation plots with reported NMR data, mostly from the Human Metabolome Database.

Conclusions: Correlation plots prepared by statistically correlating 1H-13C Heteronuclear Single Quantum Correlation spectra from human biofluids provide unambiguous identification of metabolites. The correlation plots highlight cross-peaks belonging to each individual compound, not limited by long-range magnetization transfer as conventional NMR experiments.

Place, publisher, year, edition, pages
2014. Vol. 15, 413
Keyword [en]
HSQC, Correlation, Metabolite, Biofluid, Identification
National Category
Chemical Sciences
URN: urn:nbn:se:umu:diva-80196DOI: 10.1186/s12859-014-0413-zISI: 000347650900001OAI: diva2:647348

Originally published in manuscript form.

Available from: 2013-09-11 Created: 2013-09-11 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Multivariate Analysis of 2D-NMR Spectroscopy: Applications in wood science and metabolomics
Open this publication in new window or tab >>Multivariate Analysis of 2D-NMR Spectroscopy: Applications in wood science and metabolomics
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Wood is our most important renewable resource. We need better quality and quantity both according to the wood itself and the processes that are using wood as a raw material. Hence, the understanding of the chemical composition of the wood is of high importance. Improved and new methods for analyzing wood are important to achieve better knowledge about both refining processes and raw material. The combination of NMR and multivariate analyses (MVA) is a powerful method for these analyses but so far it has been limited mainly to 1D NMR. In this project, we have developed methods for combining 2D NMR and MVA in both wood analysis and metabolomics. This combination was used to compare samples from normal wood and tension wood, and also trees with a down regulation of a pectin responsible gene. Dissolving pulp was also examined using the same combination of 2D-NMR and MVA, together with FT-IR and solid state 13C CP-MAS NMR. Here we focused on the difference between wood type (softwood and hardwood), process type (sulfite and sulfate) and viscosity. These methods confirmed and added knowledge about the dissolving pulp. Also reactivity was compared in relation to morphology of the cellulose and pulp composition. Based on the method and software used in the wood analysis projects, a new method called HSQC-STOCSY was developed. This method is especially suited for assignment of substances in complex mixtures. Peaks in 2D NMR spectra that correlate between different samples are plotted in correlation plots resembling regular NMR spectra. These correlation plots have great potential in identifying individual components in complex mixtures as shown here in a metabolic data set. This method could potentially also be used in other areas such as drug/target analyses, protein dynamics and assignment of wood spectra.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2013. 56 p.
2D NMR, HSQC, cellulose, ligning, STOCSY, HSQC-STOCSY, crystallinity
National Category
Chemical Sciences
Research subject
biological chemistry
urn:nbn:se:umu:diva-80201 (URN)978-91-7459-728-8 (ISBN)
Public defence
2013-10-04, KBC-huset, KB3B1, Umeå universitet, Umeå, 13:00 (Swedish)
Available from: 2013-09-13 Created: 2013-09-11 Last updated: 2013-09-11Bibliographically approved

Open Access in DiVA

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