Dataset Information

Robust Metabolite Quantification from J-Compensated 2D ¹H-¹³C-HSQC Experiments.

ABSTRACT: The spectral resolution of 2D 1H-13C heteronuclear single quantum coherence (1H-13C-HSQC) nuclear magnetic resonance (NMR) spectra facilitates both metabolite identification and quantification in nuclear magnetic resonance-based metabolomics. However, quantification is complicated by variations in magnetization transfer, which among others originate mainly from scalar coupling differences. Methods that compensate for variation in scalar coupling include the generation of calibration factors for individual signals or the use of additional pulse sequence schemes such as quantitative HSQC (Q-HSQC) that suppress the J_CH-dependence by modulating the polarization transfer delays of HSQC or, additionally, employ a pure-shift homodecoupling approach in the 1H dimension, such as Quantitative, Perfected and Pure Shifted HSQC (QUIPU-HSQC). To test the quantitative accuracy of these three methods, employing a 600 MHz NMR spectrometer equipped with a helium cooled cryoprobe, a Latin-square design that covered the physiological concentration ranges of 10 metabolites was used. The results show the suitability of all three methods for the quantification of highly abundant metabolites. However, the substantially increased residual water signal observed in QUIPU-HSQC spectra impeded the quantification of low abundant metabolites located near the residual water signal, thus limiting its utility in high-throughput metabolite fingerprinting studies.

SUBMITTER: Weitzel A

PROVIDER: S-EPMC7695005 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Robust Metabolite Quantification from J-Compensated 2D <sup>1</sup>H-<sup>13</sup>C-HSQC Experiments.

Weitzel Alexander A Samol Claudia C Oefner Peter J PJ Gronwald Wolfram W

Metabolites 20201107 11

The spectral resolution of 2D 1H-13C heteronuclear single quantum coherence (1H-13C-HSQC) nuclear magnetic resonance (NMR) spectra facilitates both metabolite identification and quantification in nuclear magnetic resonance-based metabolomics. However, quantification is complicated by variations in magnetization transfer, which among others originate mainly from scalar coupling differences. Methods that compensate for variation in scalar coupling include the generation of calibration factors for ...[more]

PMID: 33171777

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Project description:Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- 1H, 13C-HSQC NMR spectra arising from 13C - 13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- 1H, 13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

Dataset Information

Robust Metabolite Quantification from J-Compensated 2D ¹H-¹³C-HSQC Experiments.

Publications

Robust Metabolite Quantification from J-Compensated 2D <sup>1</sup>H-<sup>13</sup>C-HSQC Experiments.

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Dataset Information

Robust Metabolite Quantification from J-Compensated 2D 1H-13C-HSQC Experiments.

Publications

Robust Metabolite Quantification from J-Compensated 2D <sup>1</sup>H-<sup>13</sup>C-HSQC Experiments.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

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Robust Metabolite Quantification from J-Compensated 2D ¹H-¹³C-HSQC Experiments.