High-resolution magic angle spinning NMR for intact biological specimen analysis: Initial discovery, recent developments, and future directions

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.

High-resolution magic angle spinning nuclear magnetic resonance of donor pancreatic tissue may predict islet viability prior to isolation

For patients with type 1 diabetes mellitus complicated by severe hypoglycemia, clinical islet transplantation is an efficacious alternative to whole pancreas transplantation. While islet transplantation has improved over the last few years, there remain questions regarding its cost-effectiveness and donor allosensitization, which is exacerbated when islets from more than one donor are required. Understanding the features of a pancreas that would provide viable islets prior to isolation may lead to development of an accurate assay that could identify suitable pancreases and provide significant cost savings to a clinical islet transplantation program. In this pilot study, solid-state high-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy was used to assess samples of convenience of human pancreatic tissue taken prior to islet isolation both before and after incubation using the two-layer perfluorocarbon (PFC)/University of Wisconsin (UW) solution cold-storage method. We observed that, prior to incubation, human pancreatic tissue exhibited evidence of hypoxia with decreased peak integrals associated with glucose and increased peak integrals corresponding to lactate and free fatty acids. After incubation, we observed a reversal of the hypoxia-induced damage, as integrals corresponding to glucose increased, and those corresponding to lactate and free fatty acid resonances decreased. Interestingly, a significant correlation between the ratio of the glucose integral (at 3.0-4.5 ppm) to the sum of the fatty acid (at 0.9 ppm) and lactate + fatty acid (at 1.3 ppm) integrals and glucose responsiveness, a measure of islet viability, of the isolated islets, was observed after incubation in PFC/UW solution for pancreases that responded to PFC/UW solution incubation (p = 0.02). Notably, pancreases with little or no change in the integral ratio after PFC/UW solution incubation had poor recovery. These results suggest that tissue recovery is a key feature for determining islet cell viability, and further that HRMAS NMR may be a practical method to quickly assess human donor pancreatic tissue prior to islet isolation for clinical transplantation.

Metabolomic Analysis of Histological Composition Variability of High-Grade Serous Ovarian Cancer Using 1H HR MAS NMR Spectroscopy

In this work, the HR MAS NMR (high-resolution magic-angle spinning nuclear magnetic resonance) spectroscopy technique was combined with standard histological examinations to investigate the metabolic features of high-grade serous ovarian cancer (HGSOC) with a special focus on the relation between a metabolic profile and a cancer cell fraction. The studied group consisted of 44 patients with HGSOC and 18 patients with benign ovarian tumors. Normal ovarian tissue was also excised from 13 control patients. The metabolic profiles of 138 tissue specimens were acquired on a Bruker Avance III 400 MHz spectrometer. The NMR spectra of the HGSOC samples could be discriminated from those acquired from the non-transformed tissue and were shown to depend on tumor purity. The most important features that differentiate the samples with a high fraction of cancer cells from the samples containing mainly fibrotic stroma are the increased intensities in the spectral regions corresponding to phosphocholine/glycerophosphocholine, phosphoethanolamine/serine, threonine, uridine nucleotides and/or uridine diphosphate (UDP) nucleotide sugars. Higher levels of glutamine, glutamate, acetate, lysine, alanine, leucine and isoleucine were detected in the desmoplastic stroma within the HGSOC lesions compared to the stroma of benign tumors. The HR MAS NMR analysis of the metabolic composition of the epithelial and stromal compartments within HGSOC contributes to a better understanding of the disease's biology.

High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance Spectroscopy of Paired Clinical Liver Tissue Samples from Hepatocellular Cancer and Surrounding Region

The global burden of liver cancer is increasing. Timely diagnosis is important for optimising the limited available treatment options. Understanding the metabolic consequences of hepatocellular carcinoma (HCC) may lead to more effective treatment options. We aimed to document metabolite differences between HCC and matched surrounding tissues of varying aetiology, obtained at the time of liver resection, and to interpret metabolite changes with clinical findings. High-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy analyses of N = 10 paired HCC and surrounding non-tumour liver tissue samples were undertaken. There were marked HRMAS-NMR differences in lipid levels in HCC tissue compared to matched surrounding tissue and more subtle changes in low-molecular-weight metabolites, particularly when adjusting for patient-specific variability. Differences in lipid-CH3, lipid-CH2, formate, and acetate levels were of particular interest. The obvious differences in lipid content highlight the intricate interplay between metabolic adaptations and cancer cell survival in the complex microenvironment of liver cancer. Differences in formate and acetate might relate to bacterial metabolites. Therefore, documentation of metabolites in HCC tissue according to histology findings in patients is of interest for personalised medicine approaches and for tailoring targeted treatment strategies.

NMR spectroscopy for metabolomics in the living system: recent progress and future challenges

Metabolism is a fundamental process that underlies human health and diseases. Nuclear magnetic resonance (NMR) techniques offer a powerful approach to identify metabolic processes and track the flux of metabolites at the molecular level in living systems. An in vitro study through in-cell NMR tracks metabolites in real time and investigates protein structures and dynamics in a state close to their most natural environment. This technique characterizes metabolites and proteins involved in metabolic pathways in prokaryotic and eukaryotic cells. In vivo magnetic resonance spectroscopy (MRS) enables whole-organism metabolic monitoring by visualizing the spatial distribution of metabolites and targeted proteins. One limitation of these NMR techniques is the sensitivity, for which a possible improved approach is through isotopic enrichment or hyperpolarization methods, including dynamic nuclear polarization (DNP) and parahydrogen-induced polarization (PHIP). DNP involves the transfer of high polarization from electronic spins of radicals to surrounding nuclear spins for signal enhancements, allowing the detection of low-abundance metabolites and real-time monitoring of metabolic activities. PHIP enables the transfer of nuclear spin polarization from parahydrogen to other nuclei for signal enhancements, particularly in proton NMR, and has been applied in studies of enzymatic reactions and cell signaling. This review provides an overview of in-cell NMR, in vivo MRS, and hyperpolarization techniques, highlighting their applications in metabolic studies and discussing challenges and future perspectives.

Elucidating dynamic anaerobe metabolism with HRMAS 13C NMR and genome-scale modeling

Anaerobic microbial metabolism drives critical functions within global ecosystems, host-microbiota interactions, and industrial applications, yet remains ill-defined. Here we advance a versatile approach to elaborate cellular metabolism in obligate anaerobes using the pathogen Clostridioides difficile, an amino acid and carbohydrate-fermenting Clostridia. High-resolution magic angle spinning nuclear magnetic resonance (NMR) spectroscopy of C. difficile, grown with fermentable 13C substrates, informed dynamic flux balance analysis (dFBA) of the pathogen's genome-scale metabolism. Analyses identified dynamic recruitment of oxidative and supporting reductive pathways, with integration of high-flux amino acid and glycolytic metabolism at alanine's biosynthesis to support efficient energy generation, nitrogen handling and biomass generation. Model predictions informed an approach leveraging the sensitivity of 13C NMR spectroscopy to simultaneously track cellular carbon and nitrogen flow from [U-13C]glucose and [15N]leucine, confirming the formation of [13C,15N]alanine. Findings identify metabolic strategies used by C. difficile to support its rapid colonization and expansion in gut ecosystems.

Designing a quality assurance process for quality control of nuclear magnetic resonance metabolomics studies of human blood

High-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR)-based metabolomics has demonstrated its utility in studies of biofluids for various diseases. HRMAS NMR spectroscopy is uniquely well suited for analyzing human blood samples because of the small quantity of samples and minimal preparation required. To develop this methodology into standardized clinical protocols, establishment of the method's quality assurance (QA) and evaluations of its quality control (QC) are critical. This study aims to assess the QA/QC measured from human blood specimens in the form of serum and plasma through within-subject and between-subject comparisons, as well as stability and consistency comparisons over several freezing-thawing cycles of sample storage conditions, and most importantly, the agreement of pooled control samples against individual samples.

NMR and Metabolomics-A Roadmap for the Future

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

A Nuclear Magnetic Resonance Spectroscopy Method in Characterization of Blood Metabolomics for Alzheimer's Disease

There is currently a crucial need for improved diagnostic techniques and targeted treatment methods for Alzheimer's disease (AD), a disease which impacts millions of elderly individuals each year. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease. In this report, we present our AD metabolomics results measured with high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) on human blood plasma samples obtained from AD and non-AD subjects. Our study centers on developments of AD and non-AD metabolomics differentiating models with procedures of quality assurance (QA) and quality control (QC) through pooled samples. Our findings suggest that analysis of blood plasma samples using HRMAS NMR has the potential to differentiate between diseased and healthy subjects, which has important clinical implications for future improvements in AD diagnosis methodologies.