High-resolution magic-angle-spinning NMR spectroscopy of intact tissue

Sleep deprivation and aging are metabolically linked across tissues

STUDY OBJECTIVES

Insufficient sleep is a concerning hallmark of modern society because sleep deprivation (SD) is a risk factor for neurodegenerative and cardiometabolic disorders. SD imparts an aging-like effect on learning and memory, although little is known about possible common molecular underpinnings of SD and aging. Here, we examine this question by profiling metabolic features across different tissues after acute SD in young adult and aged mice.

METHODS

Young adult and aged mice were subjected to acute SD for 5 hours. Blood plasma, hippocampus, and liver samples were subjected to UPLC-MS/MS-based metabolic profiling.

RESULTS

SD preferentially impacts peripheral plasma and liver profiles (e.g. ketone body metabolism) whereas the hippocampus is more impacted by aging. We further demonstrate that aged animals exhibit SD-like metabolic features at baseline. Hepatic alterations include parallel changes in nicotinamide metabolism between aging and SD in young animals. Overall, metabolism in young adult animals is more impacted by SD, which in turn induces aging-like features. A set of nine metabolites was classified (79% correct) based on age and sleep status across all four groups.

CONCLUSIONS

Our metabolic observations demonstrate striking parallels to previous observations in studies of learning and memory and define a molecular metabolic signature of sleep loss and aging.

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.

Historical Biobanks in Breast Cancer Metabolomics- Challenges and Opportunities

Background: Metabolomic characterization of tumours can potentially improve prediction of cancer prognosis and treatment response. Here, we describe efforts to validate previous metabolomic findings using a historical cohort of breast cancer patients and discuss challenges with using older biobanks collected with non-standardized sampling procedures. Methods: In total, 100 primary breast cancer samples were analysed by high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) and subsequently examined by histology. Metabolomic profiles were related to the presence of cancer tissue, hormone receptor status, T-stage, N-stage, and survival. RNA integrity number (RIN) and metabolomic profiles were compared with an ongoing breast cancer biobank. Results: The 100 samples had a median RIN of 4.3, while the ongoing biobank had a significantly higher median RIN of 6.3 (p = 5.86 × 10⁻⁷). A low RIN was associated with changes in choline-containing metabolites and creatine, and the samples in the older biobank showed metabolic differences previously associated with tissue degradation. The association between metabolomic profile and oestrogen receptor status was in accordance with previous findings, however, with a lower classification accuracy. Conclusions: Our findings highlight the importance of standardized biobanking procedures in breast cancer metabolomics studies.

HR-MAS NMR Based Quantitative Metabolomics in Breast Cancer

High resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy is increasingly used for profiling of breast cancer tissue, delivering quantitative information for approximately 40 metabolites. One unique advantage of the method is that it can be used to analyse intact tissue, thereby requiring only minimal sample preparation. Importantly, since the method is non-destructive, it allows further investigations of the same specimen using for instance transcriptomics. Here, we discuss technical aspects critical for a successful analysis—including sample handling, measurement conditions, pulse sequences for one- and two dimensional analysis, and quantification methods—and summarize available studies, with a focus on significant associations of metabolite levels with clinically relevant parameters.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

NMR-Based Prostate Cancer Metabolomics

Prostate cancer is the second most common malignancy, and the fifth leading cause of cancer-related death among men, worldwide. A major unsolved clinical challenge in prostate cancer is the ability to accurately distinguish indolent cancer types from the aggressive ones. Reprogramming of metabolism is now a widely accepted hallmark of cancer development, where cancer cells must be able to convert nutrients to biomass while maintaining energy production. Metabolomics is the large-scale study of small molecules, commonly known as metabolites, within cells, biofluids, tissues, or organisms. Nuclear magnetic resonance (NMR) spectroscopy is commonly applied in metabolomics studies of cancer. This chapter provides protocols for NMR-based metabolomics of cell cultures, biofluids (serum and urine), and intact tissue, with concurrent advice for optimal biobanking and sample preparation procedures.

Time is ripe: maturation of metabolomics in chronobiology

Sleep and circadian rhythms studies have recently benefited from metabolomics analyses, uncovering new connections between chronobiology and metabolism. From untargeted mass spectrometry to quantitative nuclear magnetic resonance spectroscopy, a diversity of analytical approaches has been applied for biomarker discovery in the field. In this review we consider advances in the application of metabolomics technologies which have uncovered significant effects of sleep and circadian cycles on several metabolites, namely phosphatidylcholine species, medium-chain carnitines, and aromatic amino acids. Study design and data processing measures essential for detecting rhythmicity in metabolomics data are also discussed. Future developments in these technologies are anticipated vis-à-vis validating early findings, given metabolomics has only recently entered the ring with other systems biology assessments in chronometabolism studies.

Identification of Metastasis-Associated Metabolic Profiles of Tumors by (1)H-HR-MAS-MRS

Tumors develop an abnormal microenvironment during growth, and similar to the metastatic phenotype, the metabolic phenotype of cancer cells is tightly linked to characteristics of the tumor microenvironment (TME). In this study, we explored relationships between metabolic profile, metastatic propensity, and hypoxia in experimental tumors in an attempt to identify metastasis-associated metabolic profiles. Two human melanoma xenograft lines (A-07, R-18) showing different TMEs were used as cancer models. Metabolic profile was assessed by proton high resolution magic angle spinning magnetic resonance spectroscopy (¹H-HR-MAS-MRS). Tumor hypoxia was detected in immunostained histological preparations by using pimonidazole as a hypoxia marker. Twenty-four samples from 10 A-07 tumors and 28 samples from 10 R-18 tumors were analyzed. Metastasis was associated with hypoxia in both A-07 and R-18 tumors, and ¹H-HR-MAS-MRS discriminated between tissue samples with and tissue samples without hypoxic regions in both models, primarily because hypoxia was associated with high lactate resonance peaks in A-07 tumors and with low lactate resonance peaks in R-18 tumors. Similarly, metastatic and non-metastatic R-18 tumors showed significantly different metabolic profiles, but not metastatic and non-metastatic A-07 tumors, probably because some samples from the metastatic A-07 tumors were derived from tumor regions without hypoxic tissue. This study suggests that ¹H-HR-MAS-MRS may be a valuable tool for evaluating the role of hypoxia and lactate in tumor metastasis as well as for identification of metastasis-associated metabolic profiles.

Impact of Freezing Delay Time on Tissue Samples for Metabolomic Studies

Introduction

Metabolic profiling of intact tumor tissue by high-resolution magic angle spinning (HR MAS) MR spectroscopy (MRS) provides important biological information possibly useful for clinical diagnosis and development of novel treatment strategies. However, generation of high-quality data requires that sample handling from surgical resection until analysis is performed using systematically validated procedures. In this study, we investigated the effect of postsurgical freezing delay time on global metabolic profiles and stability of individual metabolites in intact tumor tissue.

Materials and methods

Tumor tissue samples collected from two patient-derived breast cancer xenograft models (n = 3 for each model) were divided into pieces that were snap-frozen in liquid nitrogen at 0, 15, 30, 60, 90, and 120 min after surgical removal. In addition, one sample was analyzed immediately, representing the metabolic profile of fresh tissue exposed neither to liquid nitrogen nor to room temperature. We also evaluated the metabolic effect of prolonged spinning during the HR MAS experiments in biopsies from breast cancer patients (n = 14). All samples were analyzed by proton HR MAS MRS on a Bruker Avance DRX600 spectrometer, and changes in metabolic profiles were evaluated using multivariate analysis and linear mixed modeling.

Results

Multivariate analysis showed that the metabolic differences between the two breast cancer models were more prominent than variation caused by freezing delay time. No significant changes in levels of individual metabolites were observed in samples frozen within 30 min of resection. After this time point, levels of choline increased, whereas ascorbate, creatine, and glutathione (GS) levels decreased. Freezing had a significant effect on several metabolites but is an essential procedure for research and biobank purposes. Furthermore, four metabolites (glucose, glycine, glycerophosphocholine, and choline) were affected by prolonged HR MAS experiment time possibly caused by physical release of metabolites caused by spinning or due to structural degradation processes.

Conclusion

The MR metabolic profiles of tumor samples are reproducible and robust to variation in postsurgical freezing delay up to 30 min.

Metabolic profiles of placenta in preeclampsia using HR-MAS MRS metabolomics

INTRODUCTION

Preeclampsia is a heterogeneous gestational disease characterized by maternal hypertension and proteinuria, affecting 2-7% of pregnancies. The disorder is initiated by insufficient placental development, but studies characterizing the placental disease components are lacking.

METHODS

Our aim was to phenotype the preeclamptic placenta using high-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HR-MAS MRS). Placental samples collected after delivery from women with preeclampsia (n = 19) and normotensive pregnancies (n = 15) were analyzed for metabolic biomarkers including amino acids, osmolytes, and components of the energy and phospholipid metabolism. The metabolic biomarkers were correlated to clinical characteristics and inflammatory biomarkers in the maternal sera.

RESULTS

Principal component analysis showed inherent differences in placental metabolic profiles between preeclamptic and normotensive pregnancies. Significant differences in metabolic profiles were found between placentas from severe and non-severe preeclampsia, but not between preeclamptic pregnancies with fetal growth restricted versus normal weight neonates. The placental metabolites correlated with the placental stress marker sFlt-1 and triglycerides in maternal serum, suggesting variation in placental stress signaling between different placental phenotypes.

DISCUSSION

HR-MAS MRS is a sensitive method for defining the placental disease component of preeclampsia, identifying several altered metabolic pathways. Placental HR-MAS MRS analysis may improve insight into processes affected in the preeclamptic placenta, and represents a novel long-required tool for a sensitive placental phenotyping of this heterogeneous disease.

Can NMR solve some significant challenges in metabolomics?

The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR's excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact bio-specimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR's limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR's continuing role in the field with an emphasis on recent and ongoing research from our laboratory.