Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites

V. Mihaleva, S.P. Korhonen, J.P.M. van Duynhoven, M. Niemitz, J.J.M. Vervoort, D.M. Jacobs

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)

Abstract

An automated quantum mechanical total line shape (QMTLS) fitting model was implemented for quantitative nuclear magnetic resonance (NMR)-based profiling of 42 metabolites in ultrafiltrated human serum samples. Each metabolite was described by a set of chemical shifts, J-couplings, and line widths. These parameters were optimized for each metabolite in each sample by iteratively minimizing the difference between the calculated and the experimental spectrum. In total, 92.0 to 98.1 % of the signal intensities in the experimental spectrum could be explained by the calculated spectrum. The model was validated by comparison to signal integration of metabolites with isolated signals and by means of standard additions. Metabolites present at average concentration higher than 50 µM were quantified with average absolute relative error less than 10 % when using different initial parameters for the fitting procedure. Furthermore, the biological applicability of the QMTLS model was demonstrated on 287 samples from an intervention study in 37 human volunteers undergoing an exercise challenge. Our automated QMTLS model was able to cope with the large dynamic range of metabolite concentrations in serum and proved to be suitable for high-throughput analysis.
Original languageEnglish
Pages (from-to)3091-3102
JournalAnalytical and Bioanalytical Chemistry
Volume406
Issue number13
DOIs
Publication statusPublished - 2014

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Metabolites
Magnetic Resonance Spectroscopy
Nuclear magnetic resonance
Serum
Volunteers
Exercise
Chemical shift
Linewidth
Throughput

Keywords

  • h-1-nmr spectra
  • metabolomics
  • spectroscopy
  • quantification
  • deconvolution

Cite this

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title = "Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites",
abstract = "An automated quantum mechanical total line shape (QMTLS) fitting model was implemented for quantitative nuclear magnetic resonance (NMR)-based profiling of 42 metabolites in ultrafiltrated human serum samples. Each metabolite was described by a set of chemical shifts, J-couplings, and line widths. These parameters were optimized for each metabolite in each sample by iteratively minimizing the difference between the calculated and the experimental spectrum. In total, 92.0 to 98.1 {\%} of the signal intensities in the experimental spectrum could be explained by the calculated spectrum. The model was validated by comparison to signal integration of metabolites with isolated signals and by means of standard additions. Metabolites present at average concentration higher than 50 µM were quantified with average absolute relative error less than 10 {\%} when using different initial parameters for the fitting procedure. Furthermore, the biological applicability of the QMTLS model was demonstrated on 287 samples from an intervention study in 37 human volunteers undergoing an exercise challenge. Our automated QMTLS model was able to cope with the large dynamic range of metabolite concentrations in serum and proved to be suitable for high-throughput analysis.",
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Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites. / Mihaleva, V.; Korhonen, S.P.; van Duynhoven, J.P.M.; Niemitz, M.; Vervoort, J.J.M.; Jacobs, D.M.

In: Analytical and Bioanalytical Chemistry, Vol. 406, No. 13, 2014, p. 3091-3102.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Mihaleva, V.

AU - Korhonen, S.P.

AU - van Duynhoven, J.P.M.

AU - Niemitz, M.

AU - Vervoort, J.J.M.

AU - Jacobs, D.M.

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AB - An automated quantum mechanical total line shape (QMTLS) fitting model was implemented for quantitative nuclear magnetic resonance (NMR)-based profiling of 42 metabolites in ultrafiltrated human serum samples. Each metabolite was described by a set of chemical shifts, J-couplings, and line widths. These parameters were optimized for each metabolite in each sample by iteratively minimizing the difference between the calculated and the experimental spectrum. In total, 92.0 to 98.1 % of the signal intensities in the experimental spectrum could be explained by the calculated spectrum. The model was validated by comparison to signal integration of metabolites with isolated signals and by means of standard additions. Metabolites present at average concentration higher than 50 µM were quantified with average absolute relative error less than 10 % when using different initial parameters for the fitting procedure. Furthermore, the biological applicability of the QMTLS model was demonstrated on 287 samples from an intervention study in 37 human volunteers undergoing an exercise challenge. Our automated QMTLS model was able to cope with the large dynamic range of metabolite concentrations in serum and proved to be suitable for high-throughput analysis.

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