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1. Gadolinium-Based Paramagnetic Relaxation Enhancement Agent Enhances Sensitivity for NUS Multidimensional NMR-Based Metabolomics

   https://doi.org/10.3390/molecules26175115  

   Honrao, Chandrashekhar; Teissier, Nathalie; Zhang, Bo; Powers, Robert; O’Day, Elizabeth M. (September 2021, Molecules)

   Gadolinium is a paramagnetic relaxation enhancement (PRE) agent that accelerates the relaxation of metabolite nuclei. In this study, we noted the ability of gadolinium to improve the sensitivity of two-dimensional, non-uniform sampled NMR spectral data collected from metabolomics samples. In time-equivalent experiments, the addition of gadolinium increased the mean signal intensity measurement and the signal-to-noise ratio for metabolite resonances in both standard and plasma samples. Gadolinium led to highly linear intensity measurements that correlated with metabolite concentrations. In the presence of gadolinium, we were able to detect a broad array of metabolites with a lower limit of detection and quantification in the low micromolar range. We also observed an increase in the repeatability of intensity measurements upon the addition of gadolinium. The results of this study suggest that the addition of a gadolinium-based PRE agent to metabolite samples can improve NMR-based metabolomics. 

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2. Towards Optimizing Neural Network-Based Quantification for NMR Metabolomics

   https://doi.org/10.3390/metabo15040249  

   Johnson, Hayden; Tipirneni-Sajja, Aaryani (April 2025, Metabolites)

   Background: Quantification of metabolites from nuclear magnetic resonance (NMR) spectra in an accurate, high-throughput manner requires effective data processing tools. Neural networks are relatively underexplored in quantitative NMR metabolomics despite impressive speed and throughput compared to more conventional peak-fitting metabolomics software. Methods: This work investigates practices for dataset and model development in the task of metabolite quantification directly from simulated NMR spectra for three neural network models: the multi-layered perceptron, the convolutional neural network, and the transformer. Model architectures, training parameters, and training datasets are optimized before comparing each model on simulated 400-MHz 1H-NMR spectra of complex mixtures with 8, 44, or 86 metabolites to quantify in spectra ranging from simple to highly complex and overlapping peaks. The optimized models were further validated on spectra at 100- and 800-MHz. Results: The transformer was the most effective network for NMR metabolite quantification, especially as the number of metabolites per spectra increased or target concentrations were low or had a large dynamic range. Further, the transformer was able to accurately quantify metabolites in simulated spectra from 100-MHz up to 800-MHz. Conclusions: The methods developed in this work reveal that transformers have the potential to accurately perform fully automated metabolite quantification in real-time and, with further development with experimental data, could be the basis for automated quantitative NMR metabolomics software. 

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3. Advances in direct detection of lysine methylation and acetylation by nuclear magnetic resonance using 13C-enriched cofactors

   https://doi.org/10.1016/j.ymeth.2023.07.010  

   Fraser, Olivia A; Namitz, Kevin EW; Showalter, Scott A (October 2023, Methods)

   Post-translational modifications (PTMs) are reversible chemical modifications that can modulate protein structure and function. Methylation and acetylation are two such PTMs with integral and well-characterized biological roles, including modulation of chromatin structure; and unknown or poorly understood roles, exemplified by the influence of these PTMs on transcription factor structure and function. The need for biological insights into the function of these PTMs motivates the development of a nondestructive and label-free method that enables pursuit of molecular mechanisms. Here, we present a protocol for implementing nuclear magnetic resonance (NMR) methods that allow for unambiguous detection of methylation and acetylation events and demonstrate their utility by observing these marks on histone H3 tail as a model system. We leverage strategic isotopic enrichment of cofactor and peptide for visualization by [1H, 13C]-HSQC and 13C direct-detect NMR measurements. Finally, we present 13C-labeling schemes that facilitate one-dimensional NMR experiments, which combine reduced measurement time relative to two-dimensional spectroscopy with robust filtering of background signals that would otherwise create spectral crowding or limit detection of low-abundance analytes. 

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4. Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

   https://doi.org/10.3390/molecules25215128  

   Crook, Alexandra A.; Powers, Robert (November 2020, Molecules)

   null (Ed.)

   Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids. 

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5. Decoherence Principles and Algorithms for One-Dimensional Nonuniform Sampling Schedules for Multidimensional NMR

   https://doi.org/10.1021/acs.analchem.5c03754  

   Rovnyak, Henry B; Cullen, Lucille E; Rovnyak, David (December 2025, Analytical Chemistry)

   Nonuniform sampling (NUS) NMR is a potent method for enabling diverse multidimensional NMR spectroscopies, but it depends on the quality of the sampling schedule, particularly for one-dimensional NUS (i.e., for 2D-NMR) and for sparser sampling where noise-like artifacts (aka sampling noise) are commonly observed. Current NUS scheduling algorithms, while generally effective, can also allow flaws that lead to increased artifactual noise in spectral reconstructions. Computation or expert user curation can improve such schedules but are not easily reproduced at the spectrometer. This work builds on lessons that reducing patterns in NUS schedules can reduce artifacts and aid sparser NUS. It is proposed here that patterns in a sampling schedule be treated sequentially at local and global scales. First, a localized decoherence filter is presented that leverages the properties of the binary Thue−Morse (TM) sequence to remediate patterned subsequences in the schedule. Next, an approach to polishing the pointspread- function (PSF) by an iterative thresholding method was developed, where improving the PSF treats the schedule globally. These algorithms are implemented in a hands-free scheduler for one-dimensional NUS and tested with both iterative soft thresholding (IST) and iterative line shape (SMILE) reconstructions. While varying degrees of sampling noise are still expected, particularly in sparser NUS conditions, these methods reduce larger spectral artifacts and perform more consistent design of schedules for broader use, as illustrated with sodium naproxen, strychnine, and u-13C,15N-ubiquitin for weighted (e.g., quantile, Poisson gap, exponential), and random unweighted (RU) NUS, though limitations of sparse RU-NUS should be considered. 

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