High-resolution Magic Angle Spinning Nuclear Magnetic Resonance Research Articles

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 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.

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.

1H NMR spectroscopic characterisation of HepG2 cells as a model metabolic system for toxicology studies

The immortalised human hepatocellular HepG2 cell line is commonly used for toxicology studies as an alternative to animal testing due to its characteristic liver-distinctive functions. However, little is known about the baseline metabolic changes within these cells upon toxin exposure. We have applied 1H Nuclear Magnetic Resonance (NMR) spectroscopy to characterise the biochemical composition of HepG2 cells at baseline and post-exposure to hydrogen peroxide (H2O2). Metabolic profiles of live cells, cell extracts, and their spent media supernatants were obtained using 1H high-resolution magic angle spinning (HR-MAS) NMR and 1H NMR spectroscopic techniques. Orthogonal partial least squares discriminant analysis (O-PLS-DA) was used to characterise the metabolites that differed between the baseline and H2O2 treated groups. The results showed that H2O2 caused alterations to 10 metabolites, including acetate, glutamate, lipids, phosphocholine, and creatine in the live cells; 25 metabolites, including acetate, alanine, adenosine diphosphate (ADP), aspartate, citrate, creatine, glucose, glutamine, glutathione, and lactate in the cell extracts, and 22 metabolites, including acetate, alanine, formate, glucose, pyruvate, phenylalanine, threonine, tryptophan, tyrosine, and valine in the cell supernatants. At least 10 biochemical pathways associated with these metabolites were disrupted upon toxin exposure, including those involved in energy, lipid, and amino acid metabolism. Our findings illustrate the ability of NMR-based metabolic profiling of immortalised human cells to detect metabolic effects on central metabolism due to toxin exposure. The established data sets will enable more subtle biochemical changes in the HepG2 model cell system to be identified in future toxicity testing.

Molecular Profiling of Peanut under Raw, Roasting, and Autoclaving Conditions Using High-Resolution Magic Angle Spinning and Solution 1H NMR Spectroscopy.

Higher rates of peanut allergy have been observed in countries that commonly roast peanuts prior to consumption. Despite the importance of understanding the role of thermal processing in allergy and on peanut composition, studies toward generating signatures that identify molecular contents following processing are scant. Here, we identified spectral signatures to track changes and differences in the molecular composition of peanuts under raw, roasted, and high-pressure and high-temperature autoclaved conditions. We analyzed both the solid flesh of the seed and solutions derived from soaking peanuts using High-Resolution Magic Angle Spinning (HR-MAS) and solution 1H Nuclear Magnetic Resonance (NMR) spectroscopy, respectively. The NMR spectra of intact peanuts revealed triglycerides as the dominant species, assigned on the basis of multiplets at 4.1 and 4.3 ppm, and corresponding defatted flours revealed the presence of sugars. Sucrose assigned based on a doublet at 5.4 ppm (anomeric proton), and triglycerides were the most abundant small molecules observed, with little variation between conditions. Soaked peanut solutions were devoid of lipids, and their resulting spectra matched the profiles of defatted peanuts. Spectral signatures resulting from autoclaving differed strikingly between those from raw and roasted peanuts, with considerable line-broadening in regions corresponding to proteins and amino-acid side chains, from 0.5 to 2.0 ppm and 6.5 to 8.5 ppm. Taken together, by using complementary NMR methods to obtain a fingerprint of the molecular components in peanuts, we demonstrated that autoclaving led to a distinct composition, likely resulting from the hydrolytic cleavage of proteins, the most important molecule of the allergic reaction.

The Relationship between Histological Composition and Metabolic Profile in Breast Tumors and Peritumoral Tissue Determined with 1H HR-MAS NMR Spectroscopy.

Breast tumors constitute the complex entities composed of cancer cells and stromal components. The compositional heterogeneity should be taken into account in bulk tissue metabolomics studies. The aim of this work was to find the relation between the histological content and 1H HR-MAS (high-resolution magic angle spinning nuclear magnetic resonance) metabolic profiles of the tissue samples excised from the breast tumors and the peritumoral areas in 39 patients diagnosed with invasive breast carcinoma. The total number of the histologically verified specimens was 140. The classification accuracy of the OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) model differentiating the cancerous from non-involved samples was 87% (sensitivity of 72.2%, specificity of 92.3%). The metabolic contents of the epithelial and stromal compartments were determined from a linear regression analysis of the levels of the evaluated compounds against the cancer cell fraction in 39 samples composed mainly of cancer cells and intratumoral fibrosis. The correlation coefficients between the levels of several metabolites and a tumor purity were found to be dependent on the tumor grade (I vs II/III). The comparison of the levels of the metabolites in the intratumoral fibrosis (obtained from the extrapolation of the regression lines to 0% cancer content) to those levels in the fibrous connective tissue beyond the tumors revealed a profound metabolic reprogramming in the former tissue. The joint analysis of the metabolic profiles of the stromal and epithelial compartments in the breast tumors contributes to the increased understanding of breast cancer biology.

HRMAS-NMR and simulation study of the self-assembly of surfactants on carbon nanotubes.

Polyethoxylated surfactants, such as those of the Tween and Pluronic series, are commonly used to disperse carbon nanotubes (CNTs) and other nanoparticles. However, the current understanding of the nature of interactions between these surfactants and CNTs is limited. The nature of the interactions between surfactants (Tween-80 [T80] and Pluronic F68 [PF68]) and CNTs was investigated using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) and coarse-grained molecular dynamics (MD) simulations. HRMAS-NMR revealed that T80 molecules interact with single-walled CNTs (SWCNTs) and multi-walled CNTs (MWCNTs) via the oleyl chain, whereas PF68 molecules interact with the surface of SWCNTs and MWCNTs via the polypropylene oxide residues. The polyethylene oxide chains were oriented towards the external aqueous environment. The HRMAS-NMR results were supported by MD simulations, and the latter provided further insights into the nature of the interactions.

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.

Compositional dependence of thermophysical properties in binary alkaline earth borate melts: Insights from structure in short-range and intermediate-range order

• Thermal conductivity of alkaline earth borate melts is investigated systematically for the first time using transient hot-wire method. • Thermal diffusivity is extrapolated from thermal conductivity, heat capacity and melt density, providing a new approach to thermal diffusivity measurement. • Short-range and intermediate-range order of alkaline earth borate structure is studied using high-resolution MAS-NMR, Raman spectroscopy and XPS for a precise and comprehensive insight into structure-conductivity relationship. • Compositional dependence of thermal conductivity is revealed with the discussion on “boron anomaly” phenomenon in alkaline earth borate melts. In this work, the thermal conductivity of alkaline earth borate melts was measured using hot-wire method from 1323 to 1623 K, and the thermal diffusivity was extrapolated from the thermal conductivity and heat capacity. The compositional dependence of thermophysical properties was interpreted according to the structure in short-range and intermediate-range order. Based on the Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and 11 B magic-angle spinning nuclear magnetic resonance (MAS-NMR) spectra, modifier cation with higher field strength prefers the formation of non-bridging oxygens (NBOs) for the charge compensation at high BO 1.5 / M O ratios. A lower amount of covalent bond and greater isolation of large borate groups lead to a lower thermal conductivity in calcium borate melt compared with strontium and barium borates. But the large size of Ba 2+ encounters difficulty in fitting around B [4] -O-B [4] linkages inside the overcrowded large borate groups when BO 1.5 /BaO = 2.5, promoting the formation of NBOs on the edge of borate groups for the charge compensation of modifier cations and leading to the decline in the thermal conductivity. Thermal conductivity plays a major role in regulating the thermal diffusivity at a given temperature since the compositional dependence of volumetric heat capacity is relatively weak compared with that of thermal conductivity.

Leaf tissue metabolomics fingerprinting of Citronella gongonha Mart. by 1H HR-MAS NMR

This research characterizes key metabolites in the leaf from Citronella gongonha Martius (Mart.) Howard (Cardiopteridaceae). All metabolites were assessed in intact leaf tissue by proton (1H) high-resolution magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy integrated with the principal component analysis (PCA) to depict molecular association with the seasonal change. The major ‘known unknown’ metabolites detected in 1H HR-MAS NMR were derivatives of flavonoid, polyphenolic and monoterpenoid compounds such as kaempferol-3-O-dihexoside, caffeoyl glucoside (2), 3-O-caffeoylquinic acid (3), 5-O-caffeoylquinic acid (4), kingiside (5), 8-epi-kingisidic acid (6), (7α)-7-O-methylmorroniside (7), (7β)-7-O-methylmorroniside (8) and alpigenoside (9) together with the universally occurring sucrose (10), α-glucoses (11, 12), alanine (13), and fatty (linolenic) acid (14). Several of the major metabolites (1, 2–9) were additionally confirmed by liquid chromatography tandem mass spectrometry (LC–MS/MS). In regard with the PCA results, metabolites 1, 2–9 and 14 were influenced by seasonal variation and/or from further (a) biotic environmental conditions. The findings in this work indicate that C. gongonha Mart. is an effective medicinal plant by preserving particularly compounds 2, 3–9 in abundant amounts. Because of close susceptibility with seasonal shift and ecological trends, further longitudinal studies are needed to realize the physiology and mechanism involved in the production of these and new metabolites in this plant under controlled conditions. Also, future studies are recommended to classify different epimers, especially of the phenolics and monoterpenoids in the given plant.

SPION nanoparticles for delivery of dopaminergic isoquinoline and benzazepine derivatives

Superparamagnetic iron nanoparticles (SPIONs) have become one of the most useful colloidal systems in nanomedicine. We report here the preparation of new hybrid core@shell systems based on SPION nanoparticles coated with a SiO2 shell (SPION@SiO2) and functionalized with carboxyl groups (SPION@SiO2-COOH). A series of new N-alkylamino- and N-alkylamido-terminated 1-phenyl- tetrahydroisoquinolines (THIQs) and 3-tetrahydrobenzazepines (THBs) derivatives presenting -SMe and -Cl groups, respectively, with potential dopaminergic activity, are synthesized and incorporated to the hybrid system. We include the synthetic details for THIQs and THBs derivatives preparation and investigate the influence of the terminal-functional group as well as the number of carbon atoms linked to THIQ and THB molecules during the coupling to the SPION@SiO2-COOH. Nuclear magnetic resonance (NMR) and electron ionization mass spectrometry (EI-MS) are used to characterize the synthesized THIQs and THBs. High-angle annular dark-field transmission electron microscopy (HAADF-TEM), energy dispersive X-ray transmission electron microscopy (EDX-TEM), and proton high-resolution magic angle spinning NMR spectroscopy1H HRMAS-NMR) are used to confirm the presence of THB and THIQ molecules onto the surface of the nanoparticles. The hybrid SPION@SiO2-THIQ and THB systems show significant activity toward the D2 receptor, reaching Ki values of about 20 nM, thus having potential application in the treatment of central nervous system (CNS) diseases.

Metabolomic analysis of endometrial cancer by high-resolution magic angle spinning NMR spectroscopy.

To analyze endometrial metabolite profiles between patients with endometrial cancer and controls. Seventeen (17) women with endometrium cancer and 18 controls were enrolled in this study. 1H HR-MAS (High Resolution-Magic Angle Spinning) NMR (Nuclear Magnetic Resonance) spectroscopy data obtained from endometrial tissue samples of patients with endometrial cancer and control group were analyzed with bioinformatics methods. Principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA) score plots obtained with the multivariate statistical analysis of pre-processed spectral data shows a separation between the samples from patients with endometrial cancer and controls. Analysis results suggest that the levels of lactate, glucose, o-phosphoethanolamine, choline, glycerophosphocholine, phosphocholine, leucine, isoleucine, valine, glutamate, glutamine, n-acetyltyrosine, methionine, taurine, alanine, aspartate and phenylalanine are increased in patients with endometrial cancer compared to the controls. The metabolomics signature of patients with endometrial cancer is different from that of benign endometrial tissue.

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.

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.

Metabolomic Analysis of Actinic Keratosis and SCC Suggests a Grade-Independent Model of Squamous Cancerization.

Simple SummaryActinic keratoses (AKs) are the most common sun-induced precancerous lesions that can progress to squamocellular carcinoma (SCC). AK I have been considered low-risk lesions, often evolving into AK II, the AK grade II and III have the potential to evolve to SCC. This research has assessed the metabolomic fingerprints of AK I, AK II, AK III and SCC by HR-MAS NMR spectroscopy, with the aim of evaluating the hypothesis of grade-association AK to SCC. The association between AKs and SCCs has also been evaluated by histopathology. Our findings support the notion that AK I are different from healthy skin and share different features with SCCs, indeed, they are metabolically active lesions with metabolic profiles similar to high-grade AKs and to SCC. The negative association of AKs with parakeratosis and the positive association with hypertrophy also suggested a similar behavior between AKs and SCCs. Therefore, all AKs should be treated independently from their clinical appearance or histological grade, since it is not possible to predict their potential evolution to SCC.Background—Actinic keratoses (AKs) are the most common sun-induced precancerous lesions that can progress to squamocellular carcinoma (SCC). Recently, the grade-independent association between AKs and SCC has been suggested; however, the molecular bases of this potential association have not been investigated. This study has assessed the metabolomic fingerprint of AK I, AK II, AK III and SCC using high resolution magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy in order to evaluate the hypothesis of grade-independent association between AK and SCC. Association between AKs and SCCs has also been evaluated by histopathology. Methods—Metabolomic data were obtained through HR-MAS NMR spectroscopy. The whole spectral profiles were analyzed through multivariate statistical analysis using MetaboAnalyst 5.0. Histologic examination was performed on sections stained with hematoxylin and eosin; statistical analysis was performed using STATA software version 14. Results—A group of 35 patients affected by AKs and/or SCCs and 10 healthy controls were enrolled for metabolomics analysis. Histopathological analysis was conducted on 170 specimens of SCCs and AKs (including the ones that underwent metabolomic analysis). SCCs and AK I were found to be significantly associated in terms of the content of some metabolites. Moreover, in the logistic regression model, the presence of parakeratosis in AKs appeared to be less frequently associated with SCCs, while AKs with hypertrophy had a two-fold higher risk of being associated with SCC. Conclusions—Our findings, derived from metabolomics and histopathological data, support the notion that AK I are different from healthy skin and share some different features with SCCs. This may further support the expanding notion that all AKs should be treated independently from their clinical appearance or histological grade because they may be associated with SCC.

Sampling Method Affects HR-MAS NMR Spectra of Healthy Caprine Brain Biopsies.

The metabolic profiling of tissue biopsies using high-resolution–magic angle spinning (HR-MAS) 1H nuclear magnetic resonance (NMR) spectroscopy may be influenced by experimental factors such as the sampling method. Therefore, we compared the effects of two different sampling methods on the metabolome of brain tissue obtained from the brainstem and thalamus of healthy goats by 1H HR-MAS NMR spectroscopy—in vivo-harvested biopsy by a minimally invasive stereotactic approach compared with postmortem-harvested sample by dissection with a scalpel. Lactate and creatine were elevated, and choline-containing compounds were altered in the postmortem compared to the in vivo-harvested samples, demonstrating rapid changes most likely due to sample ischemia. In addition, in the brainstem samples acetate and inositols, and in the thalamus samples ƴ-aminobutyric acid, were relatively increased postmortem, demonstrating regional differences in tissue degradation. In conclusion, in vivo-harvested brain biopsies show different metabolic alterations compared to postmortem-harvested samples, reflecting less tissue degradation. Sampling method and brain region should be taken into account in the analysis of metabolic profiles. To be as close as possible to the actual situation in the living individual, it is desirable to use brain samples obtained by stereotactic biopsy whenever possible.

Spatially-resolved metabolic profiling of living Drosophila in neurodegenerative conditions using 1H magic angle spinning NMR

Drosophila flies are versatile animal models for the study of gene mutations in neuronal pathologies. Their small size allows performing in vivo Magic Angle Spinning (MAS) experiments to obtain high-resolution 1H nuclear magnetic resonance (NMR) spectra. Here, we use spatially-resolved 1H high-resolution MAS NMR to investigate in vivo metabolite contents in different segments of the fly body. A comparative study of metabolic changes was performed for three neurodegenerative disorders: two cell-specific neuronal and glial models of Huntington disease (HD) and a model of glutamate excitotoxicity. It is shown that these pathologies are characterized by specific and sometimes anatomically localized variations in metabolite concentrations. In two cases, the modifications of 1H MAS NMR spectra localized in fly heads were significant enough to allow the creation of a predictive model.

1H HRMAS-NMR based metabolic fingerprints for discrimination of cheeses based on sensory qualities.

In this study, the 1H HRMAS-NMR (High-resolution Magic Angle Spinning-Nuclear Magnetic Resonance) spectra of 52 cheese samples obtained from the South Korean dairy farms were evaluated for their metabolic profiling and intensities associating with the sensory qualities. The NMR profiles displayed a broad range of compounds comprising amino acids, carbohydrates, organic acids, and phospholipids. Afterwards, the cheese samples were categorized into three groups (more likeness - G1, moderate likeness - G2, less likeness - G3), in relating to their sensory scores. The NMR data of the samples were later investigated through multivariate statistical tools to define the variations in metabolic fingerprints of every cheese sample and their intensities hailing in individual sensory groups. The unsupervised PCA employing all cheese samples unveiled the uniqueness in metabolite profiles of the brown and cheddar type cheeses (outliers). Moreover, Gouda and other types of cheeses displayed samples positioning in respective of their metabolite profiles. The pairwise comparison of sensory groups in the supervised models perceived better separation in OPLS-DA than PLS-DA. The corresponding VIP (PLS-DA) and loading (OPLS-DA) plots revealed amino acids and organic acids (lactate, citrate) as significant variables. The discrimination of G 1 Gouda type of cheeses against G 2 and G 3 was highly associated with their citrate levels. Further investigation using heatmaps displayed clear differentiation between each sensory group in terms of the levels of amino acids, lactate, citrate, phospholipids, and glycerol, conveying these variations are likely due to proteolytic and metabolic processes in cheese ripening. This study concluded that 1H HRMAS-NMR metabolite profile of the Korean cheeses is consistence with their sensory qualities. Further, the candidate metabolites identified in this study confers their potential application as biomarkers in cheese industries for faster and effective validation of sensory characteristics.

Modifications induced by chemical skin allergens on the metabolome of reconstructed human epidermis: A pilot high-resolution magic angle spinning nuclear magnetic resonance study.

High-resolution magic angle spinning (HRMAS) is a nuclear magnetic resonance (NMR) technique that enables the characterization of metabolic phenotypes/metabolite profiles of cells, tissues, and organs, under both normal and pathological conditions, without resorting to time-consuming extraction techniques. To assess the impact of chemical skin sensitizers vs non-sensitizers on the metabolome of three-dimensional reconstructed human epidermis (RHE) by HRMAS NMR. Based on the SENS-IS assay, 12 skin sensitizers and five non-sensitizing chemicals were investigated and applied on EpiSkin RHE at the published maximal non-irritating concentrations under the conditions of the test. The metabolome of RHE samples was then analyzed by HRMAS NMR. A total of 32 different metabolites were identified; 20 of these were quantified for all samples. Statistical univariate analysis showed that the tissue content of most measured metabolites (with the exception of acetate and glucose) was different in the untreated, treated with non-sensitizers, and treated with sensitizers samples. In RHE samples in contact with sensitizing chemicals, concentrations of 18 metabolites were significantly decreased. Alanine and tyrosine could not discriminate between sensitizer- and non-sensitizer-treated groups. A multivariate partial least-squares-discriminant analysis was performed on the two treated groups, discriminating sensitizing and non-sensitizing chemicals with a very good R2Y value of 0.87 and a good Q2Y value of 0.70. Data suggest that HRMAS NMR could be used to monitor the impact of chemicals, skin allergens vs non-sensitizers, on the metabolome of three-dimensional RHE.

Abstract 3721: Improved fitting of HRMAS NMR spectra for ex vivo metabolomic analysis of glioma tissue

Abstract Gliomas are aggressive brain tumors with high rates of treatment resistance and low survival rates. High-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy can quantify metabolite concentrations in glioma tissue, but most analysis techniques require manual peak selection and integration that prevents accurate and reliable quantitation of overlapping peaks and large macromolecule baselines. Our goal was to compare the effectiveness of operator-independent LCModel analysis of glioma spectra acquired from free induction decay (FID) and Carr-Purcell-Meiboom-Gill (CPMG) pulse sequences. HRMAS NMR spectra were acquired using FID and CPMG sequences from 14 histologically-confirmed glioma tissue samples (WHO grade II (n=5), III (n=5), and IV (n=4)) collected during surgical resection from human brain tumor patients. Metabolite concentration ratios and Cramer-Rao lower bounds (CRLBs) were estimated using LCModel. Metabolite CRLBs were compared using a paired two sample t-test. Differences in concentrations as a function of WHO grade were determined with ANOVA and Tukey-Kramer post-hoc tests. Significance was determined by p&amp;lt;.05. Metabolite CRLBs for lactate and myo-inositol were significantly lower for CPMG compared to FID spectra (p&amp;lt;.05). Most metabolites could be quantified with LCModel from spectra acquired with CPMG where many metabolites acquired with the FID sequence were not detected. For example, lactate was quantifiable from 3 of the spectra acquired with FID compared to 12 spectra with CPMG. The use of the CPMG sequence reduces quantification errors by eliminating confounding baseline signals. LCModel facilitates improved separation of overlapping resonances compared to manual peak integration, supporting the use of operator-independent methods for metabolic spectral analysis. Comparison of metabolite concentrations as a function of WHO grade revealed significant differences in lactate and glutamine plus glutamate normalized to creatine (p&amp;lt;.05). 2-hydroxyglutarate (2-HG) was also detected in 9 out of 13 isocitrate dehydrogenase (IDH)-mutated samples using both sequences. IDH-mutated tissue should produce 2-HG; however, these results are promising as 2-HG can be difficult to quantify in 1D NMR due to spectral overlap. In conclusion, LCModel can reliably quantify HRMAS spectra acquired with the CPMG sequence but is less reliable with the FID sequence. Increases in lactate and glutamine plus glutamate concentrations as a function of tumor grade were consistent with previous results using HRMAS for glioma metabolic analysis, and 2-HG was detected in 1D HRMAS spectra acquired with both sequences. We expect that improved spectral fitting will contribute to future NMR-based metabolomics studies in glioma. Note: This abstract was not presented at the meeting. Citation Format: Selin Ekici, Ren Geryak, Stewart G. Neill, Hui-Kuo Shu, Candace C. Fleischer. Improved fitting of HRMAS NMR spectra for ex vivo metabolomic analysis of glioma tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3721.