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Metabolites
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Nuclear Magnetic Resonance-Powered Metabolomics: Progress and Future Prospects
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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: 30 September 2025 | Viewed by 3043

Special Issue Editor

Dr. Abhinav Dubey

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (3 papers)

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Research

12 pages, 2067 KiB  
Article
Suppress or Not to Suppress … CRAFT It: A Targeted Metabolomics Case Study Extracting Essential Biomarker Signals Directly from the Full 1H NMR Spectra of Horse Serum Samples
by James Chen, Ayelet Yablon, Christina Metaxas, Matheus Guedin, Joseph Hu, Kenith Conover, Merrill Simpson, Sarah L. Ralston, Krish Krishnamurthy and István Pelczer
Metabolites 2025, 15(6), 387; https://doi.org/10.3390/metabo15060387 - 10 Jun 2025
Viewed by 533
Abstract
Background: There are a few very specific inflammation biomarkers in blood, namely lipoprotein NMe+ signals of protein clusters (GlycA and GlycB) and a composite resonance of phospholipids (SPC). The relative integrals of these resonances provide clear indication of the unique metabolic [...] Read more.
Background: There are a few very specific inflammation biomarkers in blood, namely lipoprotein NMe+ signals of protein clusters (GlycA and GlycB) and a composite resonance of phospholipids (SPC). The relative integrals of these resonances provide clear indication of the unique metabolic changes associated with disease, specifically inflammatory conditions, often related to serious diseases such as cancer or COVID-19 infection. Relatively complicated, yet very efficient experimental methods have been introduced recently (DIRE, JEDI) to suppress the rest of the spectrum, thus allowing measurement of these integrals of interest. Methods: In this study, we introduce a simple alternative processing method using CRAFT (Complete Reduction to Amplitude-Frequency Table), a time-domain (FID) analysis tool which can highlight selected subsets of the spectrum by choice for quantitative analysis. The output of this approach is a direct, spreadsheet-based representation of the required peak amplitude (integral) values, ready for comparative analysis, completely avoiding all the convectional data processing and manipulation steps. The significant advantage of this alternative method is that it only needs a simple water-suppressed 1D spectrum with no further experimental manipulation whatsoever. In addition, there are no pre/post processing steps (such as baseline and/or phase), further minimizing potential dependency on subjective decisions by the user and providing an opportunity to automate the entire process. Results: We applied this methodology to horse serum samples to follow the presence of inflammation for cohorts with or without OCD (Osteochondritis Dissecans) conditions and find diagnostic separation of the of the cohorts through statistical methods. Conclusions: The powerful and simple CRAFT-based approach is suitable to extract selected biomarker information from complex NMR spectra and can be similarly applied to any other biofluid from any source or sample, also retrospectively. There is a potential to extend such a simple analysis to other, previously identified relevant markers as well. Full article
(This article belongs to the Special Issue Nuclear Magnetic Resonance-Powered Metabolomics: Progress and Future Prospects)
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21 pages, 6266 KiB  
Article
How Early Can Pancreatic Tumors Be Detected Using NMR-Based Urine Metabolic Profiling? Identification of Early-Stage Biomarkers of Tumor Initiation and Progression in an Orthotopic Xenograft Mouse Model of Pancreatic Cancer
by Tafadzwa Chihanga, Shenyuan Xu, Hannah N. Fultz, Jenna D. Nicholson, Mark D. Brombacher, Kayla Hawkins, Dan R. Fay, Maria M. Steil, Shuisong Ni and Michael A. Kennedy
Metabolites 2025, 15(3), 142; https://doi.org/10.3390/metabo15030142 - 20 Feb 2025
Viewed by 849
Abstract
Background: Pancreatic cancer is the most lethal of all human cancers. The disease has no obvious symptoms in its early stages and in the majority of cases, the cancer goes undetected until it has advanced to the point that surgery is no longer [...] Read more.
Background: Pancreatic cancer is the most lethal of all human cancers. The disease has no obvious symptoms in its early stages and in the majority of cases, the cancer goes undetected until it has advanced to the point that surgery is no longer a viable option or until it has metastasized to other organs. The absence of reliable and sensitive biomarkers for the early detection of pancreatic cancer contributes to the poor ability to detect the disease before it progresses to an untreatable stage. Objectives: Here, an orthotopic xenograft mouse model of pancreatic cancer was investigated to determine if urinary metabolic biomarkers could be identified and used to detect the early formation of pancreatic tumors. Methods: The orthotopic xenograft mouse model of pancreatic cancer was established by injecting human MiaPaCa-2 cells, derived from a male patient aged 65 years with pancreatic adenocarcinoma, into the pancreata of severe combined immunodeficient mice. Orthotopic pancreatic tumors, allowed to grow for eight weeks, were successfully established in the pancreata in 15 out of 20 mice. At the time of sacrifice, tumors were excised and histologically analyzed and the masses and volumes recorded. Urine samples were collected prior to injection, at one-week post injection, and every two weeks afterwards for eight weeks. Results: NMR-based metabolic profiling of the urine samples indicated that 31 metabolites changed significantly over the course of tumor initiation and growth. Longitudinal metabolic profiling analysis indicated an initial increase in activity of the metabolic pathways involved in energy production and/or cell synthesis by cancer cells as required to support tumor growth that was followed by a diminished difference between control and orthotopic mice associated with tumor senescence as the tumors reached 7–8 weeks post injection. Conclusions: The results indicate that NMR-based urinary metabolic profiling may be able to detect the earliest stages of pancreatic tumor initiation and growth, highlighting the potential for translation to human clinical studies. Full article
(This article belongs to the Special Issue Nuclear Magnetic Resonance-Powered Metabolomics: Progress and Future Prospects)
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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 1095
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
(This article belongs to the Special Issue Nuclear Magnetic Resonance-Powered Metabolomics: Progress and Future Prospects)
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