Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Characterization of the metabolomic profile of renal cell carcinoma by high resolution magic angle spinning proton magnetic resonance spectroscopy.

BACKGROUND: Renal cell carcinoma (RCC) is a metabolic disease, with subtypes exhibiting aberrations in different metabolic pathways. Metabolomics may offer greater sensitivity for revealing disease biology. We investigated the metabolomic profile of RCC using high-resolution magic angle spinning (HRMAS) proton magnetic resonance spectroscopy (1HMRS). METHODS: Surgical tissue samples were obtained from our frozen tissue bank, collected from radical or partial nephrectomy. Specimens were fresh-frozen, then stored at -80 °C until analysis. Tissue HRMAS-1HMRS was performed. A MatLab-based curve fitting program was used to process the spectra to produce relative intensities for 59 spectral regions of interest (ROIs). Comparisons of the metabolomic profiles of various RCC histologies and benign tumors, angiomyolipoma, and oncocytoma, were performed. False discovery rates (FDR) were used from the response screening to account for multiple testing; ROIs with FDR p < 0.05 were considered potential predictors of RCC. Wilcoxon rank sum test was used to compare median 1HMRS relative intensities for those metabolites that may differentiate between RCC and benign tumor. Logistic regression determined odds ratios for risk of malignancy based on the abundance of each metabolite. RESULTS: Thirty-eight RCC (16 clear cell, 11 papillary, 11 chromophobe), 10 oncocytomas, 7 angiomyolipomas, and 13 adjacent normal tissue specimens (matched pairs) were analyzed. Candidate metabolites for predictors of malignancy based on FDR p-values include histidine, phenylalanine, phosphocholine, serine, phosphocreatine, creatine, glycerophosphocholine, valine, glycine, myo-inositol, scyllo-inositol, taurine, glutamine, spermine, acetoacetate, and lactate. Higher levels of spermine, histidine, and phenylalanine at 3.15 to 3.13 parts per million (ppm) were associated with decreased risk of RCC (OR 4 × 10-5, 95% CI 7.42 × 10-8, 0.02), while 2.84 to 2.82 ppm increased the risk of malignant pathology (OR 7158.67, 95% CI 6.3, 8.3 × 106). The specific metabolites characterizing this region remain to be identified. Tumor stage did not affect metabolomic profile of malignant tumors, suggesting that metabolites are dependent on histologic subtype. CONCLUSIONS: HRMAS-1HMRS identified metabolites that may predict RCC. We demonstrated that those in the 3.14 to 3.13 ppm ROI were present in lower levels in RCC, while higher levels of metabolites in the 2.84 to 2.82 ppm ROI were associated with substantially increased risk of RCC. Further research in a larger population is required to validate these findings.

Differentiation of patients with and without prostate cancer using urine 1 H NMR metabolomics.

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. For its detection, serum prostate-specific antigen (PSA) screening is commonly used, despite its lack of specificity, high false positive rate, and inability to discriminate indolent from aggressive PCa. Following increases in serum PSA levels, clinicians often conduct prostate biopsies with or without advanced imaging. Nuclear magnetic resonance (NMR)-based metabolomics has proven to be promising for advancing early-detection and elucidation of disease progression, through the discovery and characterization of novel biomarkers. This retrospective study of urine-NMR samples, from prostate biopsy patients with and without PCa, identified several metabolites involved in energy metabolism, amino acid metabolism, and the hippuric acid pathway. Of note, lactate and hippurate-key metabolites involved in cellular proliferation and microbiome effects, respectively-were significantly altered, unveiling widespread metabolomic modifications associated with PCa development. These findings support urine metabolomics profiling as a promising strategy to identify new clinical biomarkers for PCa detection and diagnosis.

Transmission of Alzheimer's disease-associated microbiota dysbiosis and its impact on cognitive function: evidence from mice and patients.

Spouses of Alzheimer's disease (AD) patients are at a higher risk of developing incidental dementia. However, the causes and underlying mechanism of this clinical observation remain largely unknown. One possible explanation is linked to microbiota dysbiosis, a condition that has been associated with AD. However, it remains unclear whether gut microbiota dysbiosis can be transmitted from AD individuals to non-AD individuals and contribute to the development of AD pathogenesis and cognitive impairment. We, therefore, set out to perform both animal studies and clinical investigation by co-housing wild-type mice and AD transgenic mice, analyzing microbiota via 16S rRNA gene sequencing, measuring short-chain fatty acid amounts, and employing behavioral test, mass spectrometry, site-mutations and other methods. The present study revealed that co-housing between wild-type mice and AD transgenic mice or administrating feces of AD transgenic mice to wild-type mice resulted in AD-associated gut microbiota dysbiosis, Tau phosphorylation, and cognitive impairment in the wild-type mice. Gavage with Lactobacillus and Bifidobacterium restored these changes in the wild-type mice. The oral and gut microbiota of AD patient partners resembled that of AD patients but differed from healthy controls, indicating the transmission of microbiota. The underlying mechanism of these findings includes that the butyric acid-mediated acetylation of GSK3ß at lysine 15 regulated its phosphorylation at serine 9, consequently impacting Tau phosphorylation. Pending confirmative studies, these results provide insight into a potential link between the transmission of AD-associated microbiota dysbiosis and development of cognitive impairment, which underscore the need for further research in this area.

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.

13 C NMR quantification of polyamine syntheses in rat prostate.

Currently, many prostate cancer patients, detected through the prostate specific antigen test, harbor organ-confined indolent disease that cannot be differentiated from aggressive cancer according to clinically and pathologically known measures. Spermine has been considered as an endogenous inhibitor for prostate-confined cancer growth and its expression has shown correlation with prostate cancer growth rates. If established clinically, measurements of spermine bio-synthesis rates in prostates may predict prostate cancer growth and patient outcomes. Using rat models, we tested the feasibility of quantifying spermine bio-synthesis rates with 13 C NMR. Male Copenhagen rats (10 weeks, n = 6) were injected with uniformly 13 C-labeled L-ornithine HCl, and were sacrificed in pairs at 10, 30, and 60 min after injection. Another two rats were injected with saline and sacrificed at 30 min as controls. Prostates were harvested and extracted with perchloric acid and the neutralized solutions were examined by 13 C NMR at 600 MHz. 13 C NMR revealed measurable ornithine, as well as putrescine-spermidine-spermine syntheses in rat prostates, allowing polyamine bio-synthetic and ornithine bio-catabolic rates to be calculated. Our study demonstrated the feasibility of 13 C NMR for measuring bio-synthesis rates of ornithine to spermine enzymatic reactions in rat prostates. The current study established a foundation upon which future investigations of protocols that differentiate prostate cancer growth rates according to the measure of ornithine to spermine bio-synthetic rates may be developed.

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.

Visualizing metabolomics data with R.

In communicating scientific results, convincing data visualization is of utmost importance. Especially in metabolomics, results based on large numbers of dimensions and variables necessitate particular attention in order to convey their message unambiguously to the reader; also, in the era of open science, traceability and reproducibility are becoming increasingly important. This article describes the use of the R programming language to visualize published metabolomics data resulting from ex vivo NMR spectroscopy and mass spectrometry experiments with a special focus on reproducibility, including example figures as well as associated R code for ease of reuse. Examples include various types of plots (bar plots, swarm plots, and violin plots; volcano plots, heatmaps, Euler diagrams, Kaplan-Meier survival plots) and annotations (groupings, intragroup line connections, significance brackets, text annotations). Advantages of code-generated plots as well as advanced techniques and best practices are discussed.

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

High-resolution magic angle spinning (HRMAS) NMR, an approach for intact biological material analysis discovered more than 25 years ago, has been advanced by many technical developments and applied to many biomedical uses. This article provides a history of its discovery, first by explaining the key scientific advances that paved the way for HRMAS NMR's invention, and then by turning to recent developments that have profited from applying and advancing the technique during the last 5 years. Developments aimed at directly impacting healthcare include HRMAS NMR metabolomics applications within studies of human disease states such as cancers, brain diseases, metabolic diseases, transplantation medicine, and adiposity. Here, the discussion describes recent HRMAS NMR metabolomics studies of breast cancer and prostate cancer, as well as of matching tissues with biofluids, multimodality studies, and mechanistic investigations, all conducted to better understand disease metabolic characteristics for diagnosis, opportune windows for treatment, and prognostication. In addition, HRMAS NMR metabolomics studies of plants, foods, and cell structures, along with longitudinal cell studies, are reviewed and discussed. Finally, inspired by the technique's history of discoveries and recent successes, future biomedical arenas that stand to benefit from HRMAS NMR-initiated scientific investigations are presented.

Total ice content and lipid saturation determine adipose tissue cryolipolysis by injection of ice-slurry.

OBJECTIVES: Cryolipolysis uses tissue cooling to solidify lipids, preferentially damaging lipid-rich cells. Topical cooling is popular for the reduction of local subcutaneous fat. Injection of biocompatible ice-slurry is a recently introduced alternative. We developed and verified a quantitative model that simulates the heat exchange and phase changes involved, offering insights into ice-slurry injection for treating subcutaneous fat. METHODS: Finite element method was used to model the spatial and temporal progression of heat transfer between adipose tissue and injected ice-slurry, estimating dose-response relationships between properties of the slurry and size of tissue affected by cryolipolysis. Phase changes of both slurry and adipose tissue lipids were considered. An in vivo swine model was used to validate the numerical solutions. Oils with different lipid compositions were exposed to ice-slurry in vitro to evaluate the effects of lipid freezing temperature. Microscopy and nuclear magnetic resonance (NMR) were performed to detect lipid phase changes. RESULTS: A ball of granular ice was deposited at the injection site in subcutaneous fat. Total injected ice content determines both the effective cooling region of tissue, and the duration of tissue cooling. Water's high latent heat of fusion enables tissue cooling long after slurry injection. Slurry temperature affects the rate of tissue cooling. In swine, when 30 ml slurry injection at -3.5°C was compared to 15 ml slurry injection at -4.8°C (both with the same total ice content), the latter led to almost twice faster tissue cooling. NMR showed a large decrease in diffusion upon lipid crystallization; saturated lipids with higher freezing temperatures were more susceptible to solidification after ice-slurry injection. CONCLUSIONS: Total injected ice content determines both the volume of tissue treated by cryolipolysis and the cooling duration after slurry injection, while slurry temperature affects the cooling rate. Lipid saturation, which varies with diet and anatomic location, also has an important influence.

Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer.

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

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.

Ex Vivo High-Resolution Magic Angle Spinning (HRMAS) 1H NMR Spectroscopy for Early Prostate Cancer Detection.

The aim of our study was to assess ex vivo HRMAS (high-resolution magic angle spinning) 1H NMR spectroscopy as a diagnostic tool for early PCa detection by testing whether metabolomic alterations in prostate biopsy samples can predict future PCa diagnosis. In a primary prospective study (04/2006-10/2018), fresh biopsy samples of 351 prostate biopsy patients were NMR spectroscopically analyzed (Bruker 14.1 Tesla, Billerica, MA, USA) and histopathologically evaluated. Three groups of 16 patients were compared: group 1 and 2 represented patients whose NMR scanned biopsy was histobenign, but patients in group 1 were diagnosed with cancer before the end of the study period, whereas patients in group 2 remained histobenign. Group 3 included cancer patients. Single-metabolite concentrations and metabolomic profiles were not only able to separate histobenign and malignant prostate tissue but also to differentiate between samples of histobenign patients who received a PCa diagnosis in the following years and those who remained histobenign. Our results support the hypothesis that metabolomic alterations significantly precede histologically visible changes, making metabolomic information highly beneficial for early PCa detection. Thanks to its predictive power, metabolomic information can be very valuable for the individualization of PCa active surveillance strategies.

Multiplatform Metabolomics Studies of Human Cancers With NMR and Mass Spectrometry Imaging.

The status of metabolomics as a scientific branch has evolved from proof-of-concept to applications in science, particularly in medical research. To comprehensively evaluate disease metabolomics, multiplatform approaches of NMR combining with mass spectrometry (MS) have been investigated and reported. This mixed-methods approach allows for the exploitation of each individual technique's unique advantages to maximize results. In this article, we present our findings from combined NMR and MS imaging (MSI) analysis of human lung and prostate cancers. We further provide critical discussions of the current status of NMR and MS combined human prostate and lung cancer metabolomics studies to emphasize the enhanced metabolomics ability of the multiplatform approach.

High Resolution Magic Angle Spinning Proton NMR Study of Alzheimer's Disease with Mouse Models.

Alzheimer's disease (AD) is a crippling condition that affects millions of elderly adults each year, yet there remains a serious need for improved methods of diagnosis. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease; however, studies of human brain tissue metabolomics are challenging, due to sample limitations and ethical considerations. Comprehensive comparisons of imaging measurements in animal models to identify similarities and differences between aging- and AD-associated metabolic changes should thus be tested and validated for future human non-invasive studies. In this paper, we present the results of our highresolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) studies of AD and wild-type (WT) mouse models, based on animal age, brain regions, including cortex vs. hippocampus, and disease status. Our findings suggest the ability of HRMAS NMR to differentiate between AD and WT mice using brain metabolomics, which potentially can be implemented in in vivo evaluations.

High resolution magic angle spinning MRS in prostate cancer.

INTRODUCTION: Prostate cancer (PCa) is one of the leading causes of death among men worldwide. The current methods utilized to screen for prostate cancer may not have sufficient sensitivity in distinguishing aggressive from indolent diseases, which affect the quality of life of patients in the short and long term. The overdiagnosis of cases and overtreatment are prevalent due to the heterogeneity of the disease in terms of latent and progressive variants, as well as in the tissue types present in biopsy samples. METHODS: The purpose of this review is to discuss the potential clinical benefits of incorporating high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS) modalities to overcome the current challenges in the diagnosis, prognostication, and monitoring of PCa.

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.