Nuclear Magnetic Resonance-Powered Metabolomics: Progress and Future Prospects

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1195

Special Issue Editor


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Guest Editor
Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
Interests: metabolomics; solution-state NMR; computational biology

Special Issue Information

Dear Colleagues,

Metabolomics has undergone a transformation in recent years, and much of its success can be attributed to rapid developments made in analytical techniques like nuclear magnetic resonance (NMR) spectroscopy. Metabolomics has found applications in diverse areas, from establishing a fundamental understanding of altered metabolism in diseases like cancer to disease diagnosis using biomarkers and drug discovery.

The metabolomics study workflow can be divided into stages, including sample preparation, data collection, data analysis and metabolite and/or metabolic pathway identification. In this Special Issue, we focus on the advances made in these stages of the metabolomics workflow. Representative examples include, but are not limited to, (a) novel sample preparation approaches with enriched or selective isotope labelling for in-cell NMR studies; (b) advances in rapid data collection using one- or two-dimensional NMR experiments; (c) software and statistical techniques for analyzing data and identifying metabolites and mapping them to metabolic pathways; and (d) applications of NMR metabolomics in understanding diseases, biomarker discovery, and metabolic engineering

Dr. Abhinav Dubey
Guest Editor

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Keywords

  • solution-state NMR
  • metabolism
  • clinical studies
  • biomarker discovery
  • high-throughput data analysis

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Published Papers (1 paper)

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Research

14 pages, 3566 KiB  
Article
Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics
by Hayden Johnson and Aaryani Tipirneni-Sajja
Metabolites 2024, 14(12), 666; https://doi.org/10.3390/metabo14120666 - 1 Dec 2024
Viewed by 843
Abstract
Background: The introduction of benchtop NMR instruments has made NMR spectroscopy a more accessible, affordable option for research and industry, but the lower spectral resolution and SNR of a signal acquired on low magnetic field spectrometers may complicate the quantitative analysis of spectra. [...] Read more.
Background: The introduction of benchtop NMR instruments has made NMR spectroscopy a more accessible, affordable option for research and industry, but the lower spectral resolution and SNR of a signal acquired on low magnetic field spectrometers may complicate the quantitative analysis of spectra. Methods: In this work, we compare the performance of multiple neural network architectures in the task of converting simulated 100 MHz NMR spectra to 400 MHz with the goal of improving the quality of the low-field spectra for analyte quantification. Multi-layered perceptron networks are also used to directly quantify metabolites in simulated 100 and 400 MHz spectra for comparison. Results: The transformer network was the only architecture in this study capable of reliably converting the low-field NMR spectra to high-field spectra in mixtures of 21 and 87 metabolites. Multi-layered perceptron-based metabolite quantification was slightly more accurate when directly processing the low-field spectra compared to high-field converted spectra, which, at least for the current study, precludes the need for low-to-high-field spectral conversion; however, this comparison of low and high-field quantification necessitates further research, comparison, and experimental validation. Conclusions: The transformer method of NMR data processing was effective in converting low-field simulated spectra to high-field for metabolomic applications and could be further explored to automate processing in other areas of NMR spectroscopy. Full article
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