Application of 31P NMR spectroscopy and chemical derivatization for metabolite profiling of lipophilic compounds in human serum

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Exploring the potential application of alternative nuclei in NMR based metabolomics

INTRODUCTION

Nuclear magnetic resonance (NMR) is widely used in metabolomics but it focusses on ¹H over other NMR-active nuclei.

OBJECTIVES

To evaluate the potential of alternative NMR-sensitive nuclei to generate useful metabolomic data.

METHOD

Proton, carbon, phosphorus, and nitrogen-based NMR metabolomics was undertaken on extracts from mint and European honey bee tissue.

RESULTS

Carbon NMR provided useful information but required larger sample sizes. Phosphorus produced overlapping peaks in one dimensional (1D) analysis but showed potential in 2D experiments. ¹⁵N NMR was found to not be sensitive enough for general metabolomic work.

CONCLUSIONS

Alternative NMR active nuclei are useful for metabolomics.

Quantitative NMR Methods in Metabolomics

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the two major analytical platforms in the field of metabolomics, the other being mass spectrometry (MS). NMR is less sensitive than MS and hence it detects a relatively small number of metabolites. However, NMR exhibits numerous unique characteristics including its high reproducibility and non-destructive nature, its ability to identify unknown metabolites definitively, and its capabilities to obtain absolute concentrations of all detected metabolites, sometimes even without an internal standard. These characteristics outweigh the relatively low sensitivity and resolution of NMR in metabolomics applications. Since biological mixtures are highly complex, increased demand for new methods to improve detection, better identify unknown metabolites, and provide more accurate quantitation continues unabated. Technological and methodological advances to date have helped to improve the resolution and sensitivity and detection of a larger number of metabolite signals. Efforts focused on measuring unknown metabolite signals have resulted in the identification and quantitation of an expanded pool of metabolites including labile metabolites such as cellular redox coenzymes, energy coenzymes, and antioxidants. This chapter describes quantitative NMR methods in metabolomics with an emphasis on recent methodological developments, while highlighting the benefits and challenges of NMR-based metabolomics.

NMR-based isotope editing, chemoselection and isotopomer distribution analysis in stable isotope resolved metabolomics

NMR is a very powerful tool for identifying and quantifying compounds within complex mixtures without the need for individual standards or chromatographic separation. Stable Isotope Resolved Metabolomics (or SIRM) is an approach to following the fate of individual atoms from precursors through metabolic transformation, producing an atom-resolved metabolic fate map. However, extracts of cells or tissue give rise to very complex NMR spectra. While multidimensional NMR experiments may partially overcome the spectral overlap problem, additional tools may be needed to determine site-specific isotopomer distributions. NMR is especially powerful by virtue of its isotope editing capabilities using NMR active nuclei such as 13C, 15N, 19F and 31P to select molecules containing just these atoms in a complex mixture, and provide direct information about which atoms are present in identified compounds and their relative abundances. The isotope-editing capability of NMR can also be employed to select for those compounds that have been selectively derivatized with an NMR-active stable isotope at particular functional groups, leading to considerable spectral simplification. Here we review isotope analysis by NMR, and methods of chemoselection both for spectral simplification, and for enhanced isotopomer analysis.

Advancements in practical and scientific bioanalytical approaches to metabolism studies in drug development

Advancement in metabolism profiling approaches and bioanalytical techniques has been revolutionized over the last two decades. Different in vitro and in vivo approaches along with advanced bioanalytical techniques are enabling the accurate qualitative and quantitative analysis of metabolites. This review summarizes various modern in vitro and in vivo approaches for executing metabolism studies with special emphasis on the recent advancement in the field. Advanced bioanalytical techniques, which can be employed in metabolism studies, have been discussed suggesting their particular application based on specific study objectives. This article can efficiently guide the researchers to scientifically plan metabolism studies and their bioanalysis during drug development programs taking advantage of a detailed understanding of instances of failure in the past.

NMR-Based Metabolomics

Nuclear magnetic resonance (NMR) spectroscopy is a major analytical method used in the growing field of metabolomics. Although NMR is relatively less sensitive than mass spectrometry, this analytical platform has numerous characteristics including its high reproducibility and quantitative abilities, its nonselective and noninvasive nature, and the ability to identify unknown metabolites in complex mixtures and trace the downstream products of isotope labeled substrates ex vivo, in vivo, or in vitro. Metabolomic analysis of highly complex biological mixtures has benefitted from the advances in both NMR data acquisition and analysis methods. Although metabolomics applications span a wide range of disciplines, a majority has focused on understanding, preventing, diagnosing, and managing human diseases. This chapter describes NMR-based methods relevant to the rapidly expanding metabolomics field.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Molecular Tagging for the Molecular Characterization of Natural Organic Matter

Natural organic matter (NOM) is the product of microbial and abiotic decay of plant and animal remains in terrestrial and aquatic ecosystems. On a molecular level, NOM is a complex mixture of organic molecules, of which the vast majority of structures are unknown. By identifying these molecules, our understanding of the many functions of NOM could be greatly enhanced. However, given that they are chromatographically inseparable and number in the thousands, traditional analytical techniques have proven unable to achieve this goal. A promising approach to enumerate functional groups and elucidate molecular structures within NOM is based on a combination of molecular tagging and high resolution spectroscopic techniques, such as nuclear magnetic resonance spectroscopy and mass spectrometry. Molecular tagging involves the selective modification of particular functional groups, inserting nuclei to act as reporters on their surrounding chemical environment. This allows examination of only the tagged molecules within NOM, thereby reducing the complexity of the mixture. In this review, the effectiveness of molecular tagging methods incorporating carbon, silicon, nitrogen, phosphorus, and deuterium into NOM are discussed. Some potential tagging methods which have not yet been applied to NOM are also presented.

UPLC-MS/MS determination of steroid hormones via a novel reaction based on derivatisation by a cyclic-organophosphate

A cyclic-organophosphate, specifically 2-chloro-5,5-dimethyl-1,3,2-dioxaphosphorinane-2-oxide, was used to derivatise the hydroxyl group at the C3 position of selected steroid hormones to analyse the derivatives using UPLC-MS/MS (ultra-performance liquid chromatography-tandem mass spectrometry). Reactions were performed in an anhydrous pyridine environment in the presence of AlCl₃ at 50 °C. The developed reaction is suitable for analytical chemistry applications and was validated by analysis of selected contraceptive drugs. The sensitivity of the method depends on hormone tested and the limit of detection ranges from 130 pg/mL for β-estradiol to 240 pg/mL for estriol. The estimated efficiency of derivatisation reactions varies in the range from 77.5 to 95.7%, and depends upon the hormone undergoing derivatisation. The method's recovery rate for the lowest concentration tested (800 pg/mL) is 88.1-96.3%. The method exhibits linearity in the 390 pg/mL to 2.5 μg/mL range, with R² = 0.997. The developed steroid hormone derivatisation reaction was validated experimentally using UHPLC-QTOF-MS (ultra-high performance liquid chromatography quadrupole time of flight mass spectrometry) and NMR (nuclear magnetic resonance) spectroscopy. These studies show that the developed derivatisation reaction provides a precise and repeatable determination of selected steroid hormones in contraceptive drugs. At n = 10, CV (Coefficient of Variation) did not exceed 7%, which is a very good result compared with other analytical methods.

Determination of hydroxyl groups in biorefinery resources via quantitative 31P NMR spectroscopy

The analysis of chemical structural characteristics of biorefinery product streams (such as lignin and tannin) has advanced substantially over the past decade, with traditional wet-chemical techniques being replaced or supplemented by NMR methodologies. Quantitative ³¹P NMR spectroscopy is a promising technique for the analysis of hydroxyl groups because of its unique characterization capability and broad potential applicability across the biorefinery research community. This protocol describes procedures for (i) the preparation/solubilization of lignin and tannin, (ii) the phosphitylation of their hydroxyl groups, (iii) NMR acquisition details, and (iv) the ensuing data analyses and means to precisely calculate the content of the different types of hydroxyl groups. Compared with traditional wet-chemical techniques, the technique of quantitative ³¹P NMR spectroscopy offers unique advantages in measuring hydroxyl groups in a single spectrum with high signal resolution. The method provides complete quantitative information about the hydroxyl groups with small amounts of sample (~30 mg) within a relatively short experimental time (~30-120 min).

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Illuminating the dark metabolome to advance the molecular characterisation of biological systems

BACKGROUND

The latest version of the Human Metabolome Database (v4.0) lists 114,100 individual entries. Typically, however, metabolomics studies identify only around 100 compounds and many features identified in mass spectra are listed only as 'unknown compounds'. The lack of ability to detect all metabolites present, and fully identify all metabolites detected (the dark metabolome) means that, despite the great contribution of metabolomics to a range of areas in the last decade, a significant amount of useful information from publically funded studies is being lost or unused each year. This loss of data limits our potential gain in knowledge and understanding of important research areas such as cell biology, environmental pollution, plant science, food chemistry and health and biomedical research. Metabolomics therefore needs to develop new tools and methods for metabolite identification to advance as a field.

AIM OF REVIEW

In this critical review, some potential issues with metabolite identification are identified and discussed. New and novel emerging technologies and tools which may contribute to expanding the number of compounds identified in metabolomics studies (thus illuminating the dark metabolome) are reviewed. The aim is to stimulate debate and research in the molecular characterisation of biological systems to drive forward metabolomic research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The work specifically discusses dynamic nuclear polarisation nuclear magnetic resonance spectroscopy (DNP-NMR), non-proton NMR active nuclei, two-dimensional liquid chromatography (2DLC) and Raman spectroscopy (RS). It is suggested that developing new methods for metabolomics with these techniques could lead to advances in the field and better characterisation of biological systems.

The future perspective: metabolomics in laboratory medicine for inborn errors of metabolism

Metabolomics can be described as a simultaneous and comprehensive analysis of small molecules in a biological sample. Recent technological and bioinformatics advances have facilitated large-scale metabolomic studies in many areas, including inborn errors of metabolism (IEMs). Despite significant improvements in the diagnosis and treatment of some IEMs, it is still challenging to understand how genetic variation affects disease progression and susceptibility. In addition, a search for new more personalized therapies and a growing demand for tools to monitor the long-term metabolic effects of existing therapies set the stage for metabolomics integration in preclinical and clinical studies. While targeted metabolomics approach is a common practice in biochemical genetics laboratories for biochemical diagnosis and monitoring of IEMs, applications of untargeted metabolomics in the clinical laboratories are still in infancy, facing some challenges. It is however, expected in the future to dramatically change the scope and utility of the clinical laboratory playing a significant role in patient management. This review provides an overview of targeted and global, large-scale metabolomic studies applied to investigate various IEMs. We discuss an existing and prospective clinical applications of metabolomics in IEMs for better diagnosis and deep understanding of complex metabolic perturbations associated with the etiology of inherited metabolic disorders.

Recent Trends in Nucleotide Synthesis

Focusing on the recent literature (since 2000), this review outlines the main synthetic approaches for the preparation of 5'-mono-, 5'-di-, and 5'-triphosphorylated nucleosides, also known as nucleotides, as well as several derivatives, namely, cyclic nucleotides and dinucleotides, dinucleoside 5',5'-polyphosphates, sugar nucleotides, and nucleolipids. Endogenous nucleotides and their analogues can be obtained enzymatically, which is often restricted to natural substrates, or chemically. In chemical synthesis, protected or unprotected nucleosides can be used as the starting material, depending on the nature of the reagents selected from P(III) or P(V) species. Both solution-phase and solid-support syntheses have been developed and are reported here. Although a considerable amount of research has been conducted in this field, further work is required because chemists are still faced with the challenge of developing a universal methodology that is compatible with a large variety of nucleoside analogues.

In-depth investigation on quantitative characterization of pyrolysis oil by 31P NMR

The investigation on time-dependent changes when using ³¹P NMR to analyze pyrolysis bio-oils has been accomplished and the proposed application of this method is essential to achieve reliable quantitative data.

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.

15N-cholamine--a smart isotope tag for combining NMR- and MS-based metabolite profiling

Recently, the enhanced resolution and sensitivity offered by chemoselective isotope tags have enabled new and enhanced methods for detecting hundreds of quantifiable metabolites in biofluids using nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry. However, the inability to effectively detect the same metabolites using both complementary analytical techniques has hindered the correlation of data derived from the two powerful platforms and thereby the maximization of their combined strengths for applications such as biomarker discovery and the identification of unknown metabolites. With the goal of alleviating this bottleneck, we describe a smart isotope tag, (15)N-cholamine, which possesses two important properties: an NMR sensitive isotope and a permanent charge for MS sensitivity. Using this tag, we demonstrate the detection of carboxyl group containing metabolites in both human serum and urine. By combining the individual strengths of the (15)N label and permanent charge, the smart isotope tag facilitates effective detection of the carboxyl-containing metabolome by both analytical methods. This study demonstrates a unique approach to exploit the combined strength of MS and NMR in the field of metabolomics.

Combining Hydrophilic Interaction Chromatography (HILIC) and Isotope Tagging for Off-Line LC-NMR Applications in Metabolite Analysis

The complementary use of liquid chromatography (LC) and nuclear magnetic resonance (NMR) has shown high utility in a variety of fields. While the significant benefit of spectral simplification can be achieved for the analysis of complex samples, other limitations remain. For example, (1)H LC-NMR suffers from pH dependent chemical shift variations, especially during urine analysis, owing to the high physiological variation of urine pH. Additionally, large solvent signals from the mobile phase in LC can obscure lower intensity signals and severely limit the number of metabolites detected. These limitations, along with sample dilution, hinder the ability to make reliable chemical shift assignments. Recently, stable isotopic labeling has been used to detect quantitatively specific classes of metabolites of interest in biofluids. Here we present a strategy that explores the combined use of two-dimensional hydrophilic interaction chromatography (HILIC) and isotope tagged NMR for the unambiguous identification of carboxyl containing metabolites present in human urine. The ability to separate structurally related compounds chromatographically, in off-line mode, followed by detection using (1)H-(15)N 2D HSQC (two-dimensional heteronuclear single quantum coherence) spectroscopy, resulted in the assignment of low concentration carboxyl-containing metabolites from a library of isotope labeled compounds. The quantitative nature of this strategy is also demonstrated.

Monitoring biodegradation of poly(butylene sebacate) by Gel Permeation Chromatography, (1)H-NMR and (31)P-NMR techniques

The increasing use of new generation plastics has been accompanied by the development of standard methods for studying their biodegradability. Generally, test methods are based on the measurement of CO(2) production, i.e. the mineralization degree of the tested materials. However, in order to describe the biodegradation process, the determination of the residual amount of tested material which remains in the environment and its chemical characterization can be very important. In this study, the biodegradation in soil of a model polyester (poly(butylene sebacate)) was monitored. Gel Permeation Chromatography and Nuclear Magnetic Resonance ((31)P-NMR and (1)H-NMR) were used in order to obtain information about the polyester structure and the possible by-products that can be found in soil during and at the end of the incubation. The polyester mineralization (i.e. the CO(2) production) was tested according to ASTM 5988 standard method for 245 days. When the polyester mineralization was about 21% and 37% (after 78 and 140 days of incubation) and at the end of the process (63% of mineralization, 100% if compared to the cellulose used as reference material), the soil was extracted with chloroform (solvent of the tested substance) and the extracts were analyzed using GPC and NMR acquisitions. The analytical acquisitions showed high molecular weight polyester in soil during the incubation (78 and 140 days): the polyester concentration decreased but its structure remained almost the same with a slow decreasing in molecular weight. At the end of the test (245 days) no film of the polyester could be extracted from the soil: NMR acquisitions and GPC analyses of the extracts suggested a strong degraded structure of the residual polyester. Even if at the end of the process only 63% of carbon had been lost by mineralization, the whole of the added polyester seems to have disappeared after about eight months of incubation, suggesting substantial biomass formation.

Analysis of bacterial biofilms using NMR-based metabolomics

Infectious diseases can be difficult to cure, especially if the pathogen forms a biofilm. After decades of extensive research into the morphology, physiology and genomics of biofilm formation, attention has recently been directed toward the analysis of the cellular metabolome in order to understand the transformation of a planktonic cell to a biofilm. Metabolomics can play an invaluable role in enhancing our understanding of the underlying biological processes related to the structure, formation and antibiotic resistance of biofilms. A systematic view of metabolic pathways or processes responsible for regulating this 'social structure' of microorganisms may provide critical insights into biofilm-related drug resistance and lead to novel treatments. This review will discuss the development of NMR-based metabolomics as a technology to study medically relevant biofilms. Recent advancements from case studies reviewed in this manuscript have shown the potential of metabolomics to shed light on numerous biological problems related to biofilms.

Isotope enhanced approaches in metabolomics

The rapidly growing area of "metabolomics," in which a large number of metabolites from body fluids, cells or tissue are detected quantitatively, in a single step, promises immense potential for a number of disciplines including early disease diagnosis, therapy monitoring, systems biology, drug discovery and nutritional science. Because of its ability to detect a large number of metabolites in intact biological samples reproducibly and quantitatively, nuclear magnetic resonance (NMR) spectroscopy has emerged as one of the most powerful analytical techniques in metabolomics. NMR spectroscopy of biological samples with isotope labeling of metabolites using nuclei such as (2)H, (13)C, (15)N and (31)P, either in vivo or ex vivo, has dramatically improved our ability to identify low concentrated metabolites and trace important metabolic pathways. Considering the somewhat limited sensitivity and high complexity of NMR spectra of biological samples, efforts have been made to increase sensitivity and selectivity through isotope labeling methods, which pave novel avenues to unravel biological complexity and understand cellular functions in health and various disease conditions. This chapter describes current developments in isotope labeling of metabolites in vivo as well as ex vivo, and their potential metabolomics applications.

Ratio analysis nuclear magnetic resonance spectroscopy for selective metabolite identification in complex samples

Metabolite identification in the complex NMR spectra of biological samples is a challenging task due to significant spectral overlap and limited signal-to-noise. In this study we present a new approach, RANSY (ratio analysis NMR spectroscopy), which identifies all the peaks of a specific metabolite on the basis of the ratios of peak heights or integrals. We show that the spectrum for an individual metabolite can be generated by exploiting the fact that the peak ratios for any metabolite in the NMR spectrum are fixed and proportional to the relative numbers of magnetically distinct protons. When the peak ratios are divided by their coefficients of variation derived from a set of NMR spectra, the generation of an individual metabolite spectrum is enabled. We first tested the performance of this approach using one-dimensional (1D) and two-dimensional (2D) NMR data of mixtures of synthetic analogues of common body fluid metabolites. Subsequently, the method was applied to (1)H NMR spectra of blood serum samples to demonstrate the selective identification of a number of metabolites. The RANSY approach, which does not need any additional NMR experiments for spectral simplification, is easy to perform and has the potential to aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.

Quantitative analysis of blood plasma metabolites using isotope enhanced NMR methods

NMR spectroscopy is a powerful analytical tool for both qualitative and quantitative analysis. However, accurate quantitative analysis in complex fluids such as human blood plasma is challenging, and analysis using one-dimensional NMR is limited by signal overlap. It is impractical to use heteronuclear experiments involving natural abundance (13)C on a routine basis due to low sensitivity, despite their improved resolution. Focusing on circumventing such bottlenecks, this study demonstrates the utility of a combination of isotope enhanced NMR experiments to analyze metabolites in human blood plasma. (1)H-(15)N HSQC and (1)H-(13)C HSQC experiments on the isotope tagged samples combined with the conventional (1)H one-dimensional and (1)H-(1)H TOCSY experiments provide quantitative information on a large number of metabolites in plasma. The methods were first tested on a mixture of 28 synthetic analogues of metabolites commonly present in human blood; 27 metabolites in a standard NIST (National Institute of Standards and Technology) human blood plasma were then identified and quantified with an average coefficient of variation of 2.4% for 17 metabolites and 5.6% when all the metabolites were considered. Carboxylic acids and amines represent a majority of the metabolites in body fluids, and their analysis by isotope tagging enables a significant enhancement of the metabolic pool for biomarker discovery applications. Improved sensitivity and resolution of NMR experiments imparted by (15)N and (13)C isotope tagging are attractive for both the enhancement of the detectable metabolic pool and accurate analysis of plasma metabolites. The approach can be easily extended to many additional metabolites in almost any biological mixture.

Advances in NMR-based biofluid analysis and metabolite profiling

Significant improvements in NMR technology and methods have propelled NMR studies to play an important role in a rapidly expanding number of applications involving the profiling of metabolites in biofluids. This review discusses recent technical advances in NMR spectroscopy based metabolite profiling methods, data processing and analysis over the last three years.

Human bile as a rich source of biomarkers for hepatopancreatobiliary cancers

Metabolic profiling of biofluids is emerging as an important area with a promising number of applications in clinical medicine, including early diagnosis of numerous diseases that normally remain silent until late in the progress of disease. While blood and urine are more often used to explore biomarkers that distinguish he healthy from disease conditions, human bile is emerging as a rich source of biomarkers specifically for the cancers of the liver (hepatocellular carcinoma), bile ducts (cholangiocarcinoma), gallbladder and pancreas. This is owing to the fact that metabolites linked to the pathways of tumor cell metabolism are rich in bile by virtue of its association or proximity to the pathological source. Recent methodological developments have enabled the identification of a number of bile metabolites that have links with hepatopancreatobiliary diseases. Investigations of human bile are also considered to help the biomarker discovery process in vitro and provide avenues for translational research in detecting and following dynamic variations of biomarkers in clinical settings using noninvasive approaches, such as in vivo magnetic resonance spectroscopy. This article reviews the current status and potential applications of human bile as a source of biomarkers, with emphasis on metabolites, for early detection of cancers associated with the hepatopancreatobiliary system.

13C-formylation for improved nuclear magnetic resonance profiling of amino metabolites in biofluids

An increased interest in metabolite profiling is driving the need for improved analytical techniques with greater performance for a variety of important applications. Despite their limited sensitivity, nuclear magnetic resonance (NMR) methods are attractive because of their simplicity, reproducibility, quantitative nature, and wide applicability. The use of chemoselective isotopic tags has the potential to advance the application of NMR for analyzing metabolites in complex biofluids by allowing detection of metabolites down to the low micromoalr level with high resolution and specificity. Here, we report a new (13)C-tagging method using (13)C-formic acid that delivers high sensitivity, good quantitation, and excellent resolution for (1)H-(13)C 2D NMR profiling of amino metabolites. High reproducibility (coefficient of variation (CV) = 2%) was observed for metabolites in urine with concentrations down to 10 microM. As amino compounds comprise an important class of metabolites and small molecules of biological roles, this new method therefore should be amenable to a variety of applications.