Class selection of amino acid metabolites in body fluids using chemical derivatization and their enhanced 13C NMR

Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules

Chemical derivatization is a powerful strategy to enhance sensitivity and selectivity of liquid chromatography-mass spectrometry for non-targeted analysis of chemicals in complex mixtures. However, it remains impossible to obtain large sets of reference spectra for chemically derived molecules (CDMs), representing a major barrier in real-world applications. Herein, we describe a deep learning approach that enables accurate prediction of electrospray ionization tandem mass spectra for CDMs (DeepCDM). DeepCDM is established by transfer learning from a generic spectrum predicting model using a small set of experimentally acquired tandem mass spectra of CDMs, which converts a generic model with low predictability for CDMs into a specialized model with high predictability. We demonstrate DeepCDM by predicting electrospray ionization tandem mass spectra of dansylated molecules. The success in establishing Dns-MS further enables the development of DnsBank, a dansylation-specialized in silico spectral library. DnsBank achieves significant increases of accurate annotation rates of dansylated molecules, facilitating discovery of new hazardous pollutants from an environmental study of leather industrial wastewater. DeepCDM is also highly versatile for other classes of CDMs. Therefore, we envision that DeepCDM will pave a way for high-throughput identification of CDMs in non-targeted analysis to dig unknowns with potential health impacts from emerging anthropogenic chemicals.

Detecting Submicromolar Analytes in Mixtures with a 5 min Acquisition on 600 MHz NMR Spectrometers

Amino compounds are widely present in complex mixtures in chemistry, biology, medicine, food, and environmental sciences involving drug impurities and metabolisms of proteins, biogenic amines, neurotransmitters, and pyrimidine in biological systems. Nuclear magnetic resonance (NMR) spectroscopy is an excellent tool for simultaneously identifying and quantifying these in-mixture compounds but has a limit-of-detection (LOD) over several micromolarities (>5 μM). To break such a sensitivity barrier, we developed a sensitive and rapid method by combining the probe-induced sensitivity enhancement and nonuniform-sampling-based 1H-13C HSQC 2D-NMR (PRISE-NUS-HSQC). We introduced two 13CH3 tags for each analyte to respectively increase the 1H and 13C abundances for up to 6 and 200 fold. This enabled high-resolution detection of 0.4-0.8 μM analytes in mixtures in 5 mm tubes with a 5 min acquisition on 600 MHz spectrometers. The method is much more sensitive and faster than traditional 1H-13C HSQC methods (∼50 μM, >10 h). Using sulfanilic acid as a single reference, furthermore, we established a database covering chemical shifts and relative-response factors for >100 compounds, enabling reliable identification and quantification. The method showed good quantitation linearity, accuracy, precision, and applicability in multiple biological matrices, offering a rapid and sensitive approach for quantitative analysis of large cohorts of chemical, medicinal, metabolomic, food, and other mixtures.

Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples

Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC-MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC-MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC-RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC-RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC-RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

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.

Simultaneous analysis of amino acids based on discriminative 19F NMR spectroscopy

The simultaneous analysis of amino acids (AAs) is crucial for human health, diagnosis and treatment of disease, and nutritional quality evaluation in foodstuffs. Here, we establish an easy and rapid method for the simultaneous analysis of AAs using a single reagent 2-(trifluoromethyl)benzaldehyde (oTFMBA) based on spectral-separation-enabled ¹⁹F NMR spectroscopy. oTFMBA, a highly sensitive chemosensor, is capable of analyzing 19 proteinogenic AAs or non-amino acid amines (non-AAs) in a complex mixture by adjusting the pH in a toilless way. The ¹⁹F signals of oTFMBA-labeled AAs are distributed over a wide range of ∼ 0.7 ppm, demonstrating oTFMBA with higher resolution for simultaneous analysis of AAs compared to the o-phthaldialdehyde (OPA) method (<0.6 ppm). Additionally, 12 AAs were unambiguously identified in human urine, including Asp, Ser, Gly, Thr, Glu, Arg, Ala, Val, Ile, Tyr, His, and Phe. Furthermore, our method's detection limit for AAs is 5.83 μM, illustrating sensitivity with an ∼100-fold improvement over the OPA method. This work represents an approach to the analysis of AAs or non-AAs in a complicated mixture (even biofluid) using a ¹⁹F NMR probe with high sensitivity, which is of great significance for the simultaneous analysis of multiple analytes.

Simultaneous identification and quantification of amino acids in biofluids by reactive 19F-tags

A robust method to identify and quantify amino acids close to physiological conditions by 1D ¹⁹F NMR was established. Each ¹⁹F-derivatized amino acid has its characteristic chemical-shift profile that is readily identified in the mixture of amino acids or in biofluids including fetal bovine serum and cell lysates. The method shows great potential in metabolomics and biochemical analysis.

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.

Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative analytical tool commonly utilized for metabolomics analysis. Quantitative NMR (qNMR) is a field of NMR spectroscopy dedicated to the measurement of analytes through signal intensity and its linear relationship with analyte concentration. Metabolomics-based NMR exploits this quantitative relationship to identify and measure biomarkers within complex biological samples such as serum, plasma, and urine. In this review of quantitative NMR-based metabolomics, the advancements and limitations of current techniques for metabolite quantification will be evaluated as well as the applications of qNMR in biomedical metabolomics. While qNMR is limited by sensitivity and dynamic range, the simple method development, minimal sample derivatization, and the simultaneous qualitative and quantitative information provide a unique landscape for biomedical metabolomics, which is not available to other techniques. Furthermore, the non-destructive nature of NMR-based metabolomics allows for multidimensional analysis of biomarkers that facilitates unambiguous assignment and quantification of metabolites in complex biofluids.

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.

Simultaneous Amino Acid Analysis Based on 19F NMR Using a Modified OPA-Derivatization Method

To provide alternative methods of analyzing amino acids without liquid chromatography, ¹⁹F NMR-based simultaneous and individual detection methods for amino acids using o-phthalaldehyde (OPA)-based ¹⁹F labeling have been developed. Since the chemical shifts of almost all ¹⁹F-labeled amino acids differ from each other, and they can be discriminated on the ¹⁹F NMR spectrum, simultaneous detection of amino acids has been successfully demonstrated. The discrimination pattern of the peak identical to that of the ¹⁹F-labeled amino acids was largely dependent on the chemical structure of the thiols having ¹⁹F nuclei, strongly suggesting that there is a large potential for clearer discrimination of amino acids by optimizing the thiol structure and/or combined use of thiols.

Detecting acetylated aminoacids in blood serum using hyperpolarized 13C-1Η-2D-NMR

Dynamic Nuclear Polarization (DNP) can substantially enhance the sensitivity of NMR experiments. Among the implementations of DNP, ex-situ dissolution DNP (dDNP) achieves high signal enhancement levels owing to a combination of a large temperature factor between 1.4 and 300 K with the actual DNP effect in the solid state at 1.4 K. For sufficiently long T₁ relaxation times much of the polarization can be preserved during dissolution with hot solvent, thus enabling fast experiments during the life time of the polarization. Unfortunately, for many metabolites found in biological samples such as blood, relaxation times are too short to achieve a significant enhancement. We have therefore introduced ¹³C-carbonyl labeled acetyl groups as probes into amino acid metabolites using a simple reaction protocol. The advantage of such tags is a sufficiently long T₁ relaxation time, the possibility to enhance signal intensity by introducing ¹³C, and the possibility to identify tagged metabolites in NMR spectra. We demonstrate feasibility for mixtures of amino acids and for blood serum. In two-dimensional dDNP-enhanced HMQC experiments of these samples acquired in 8 s we can identify acetylated amino acids and other metabolites based on small differences in chemical shifts.

Metal-organic frameworks for the sorption of acetone and isopropanol in exhaled breath of diabetics prior to quantitation by gas chromatography

A method is described for non-invasive glucose monitoring of diabetics by means of breath analysis. The metal-organic frameworks (MOFs) ZIF-7, UiO-66 and MOF-5 were chosen as sorbents in packed tubes for sampling and preconcentration of acetone and isopropanol which are established diabetes biomarkers. The MOF UiO-66 was found to be the most appropriate sorbent. Following thermal desorption, acetone and isopropanol where quantified by GC. The method has low limits of detection (0.79-0.84 μg·L^-1) and wide linear ranges (5-2000 μg·L^-1). It is assumed that the good performance of UiO-66 as a sorbent results from its large surface area and unique porous structure, and from van der Waals interactions. The relative standard deviation for six replicate cycles of sampling and preconcentration using one 50 mg UiO-66 packed tube ranged between 2.3 and 6.7% for intra-day assays, and from 2.7 to 4.3% for inter-day assays. A tube packed with 50 mg of UiO-66 packed tube can be used in over 120 cycles of adsorption/desorption without significant loss of collection efficiency. The GC method has been applied for the analysis of diabetic breath samples, and the recoveries from spiked samples ranged from 89.1 to 107.6%. Graphical abstract Schematic presentation of metal-organic frameworks as sorbents combined with thermal desorption-gas chromatography for the determination of acetone and isopropanol in exhaled breath of diabetics.

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.

Amperometric Detection of the Urinary Disease Biomarker p-HPA by Allosteric Modulation of a Redox Polymer-Embedded Bacterial Reductase

We report an amperometric biosensor for the urinary disease biomarker para-hydroxyphenylacetate ( p-HPA) in which the allosteric reductase component of a bacterial hydroxylase, C1-hpah, is electrically wired to glassy carbon electrodes through incorporation into a low-potential Os-complex modified redox polymer. The proposed biosensing strategy depends on allosteric modulation of C1-hpah by the binding of the enzyme activator and analyte p-HPA, stimulating oxidation of the cofactor NADH. The pronounced concentration-dependence of allosteric C1-hpah modulation in the presence of a constant concentration of NADH allowed sensitive quantification of the target, p-HPA. The specific design of the immobilizing redox polymer with suitably low working potential allowed biosensor operation without the risk of co-oxidation of potentially interfering substances, such as uric acid or ascorbic acid. Optimized sensors were successfully applied for p-HPA determination in artificial urine, with good recovery rates and reproducibility and sub-micromolar detection limits. The proposed application of the allosteric enzyme C1-hpah for p-HPA trace electroanalysis is the first successful example of simple amperometric redox enzyme/redox polymer biosensing in which the analyte acts as an effector, modulating the activity of an immobilized biocatalyst. A general advantage of the concept of allosterically modulated biosensing is its ability to broaden the range of approachable analytes, through the move from substrate to effector detection.

DL-Amino Acid Analysis Based on Labeling with Light and Heavy Isotopic Reagents Followed by UPLC-ESI-MS/MS

L-Pyroglutamic acid succinimidyl ester (L-PGA-OSu) and its isotopic variant (L-PGA[d₅]-OSu) were synthesized and used as the chiral labeling reagents for the enantioseparation of amino acids by reversed-phase UPLC-ESI-MS/MS. The enantiomers of amino acids were labeled with the reagents at 60 °C for 10 min in an alkaline medium. The resulting diastereomers were well separated by the reversed-phase chromatography using an ODS column, packed with small particles (1.7 μm) (Rs = 1.95-8.05). A highly sensitive detection at a low-fmol level (0.5-3.2 fmol) was obtained from the selected reaction monitoring (SRM) chromatograms. An isotope labeling strategy using light and heavy variants for the differential analysis of the DL-amino acids in different sample groups is also presented in this paper. The ratios of D/L-alanine in different yogurt products were successfully determined by the proposed method. The D/L ratios were almost comparable to those obtained from only using light reagent (i.e., L-PGA-OSu). Therefore, the proposed strategy seems to be useful for the differential analysis of DL-amino acids, not only in food products but also in biological samples.

Nuclear Magnetic Resonance technique in tumor metabolism

Cancer is one of the most serious diseases that cause an enormous number of deaths all over the world. Tumor metabolism has great discrimination from that of normal tissues. Exploring the tumor metabolism may be one of the best ways to find biomarkers for cancer detection, diagnosis and to provide novel insights into internal physiological state where subtle changes may happen in metabolite concentrations. Nuclear Magnetic Resonance (NMR) technique nowadays is a popular tool to analyze cell extracts, tissues and biological fluids, etc, since it is a relatively fast and an accurate technique to supply abundant biochemical information at molecular levels for tumor research. In this review, approaches in tumor metabolism are discussed, including sample collection, data profiling and multivariate data analysis methods etc. Some typical applications of NMR are also summarized in tumor metabolism.

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.

Hyperpolarized NMR probes for biological assays

During the last decade, the development of nuclear spin polarization enhanced (hyperpolarized) molecular probes has opened up new opportunities for studying the inner workings of living cells in real time. The hyperpolarized probes are produced ex situ, introduced into biological systems and detected with high sensitivity and contrast against background signals using high resolution NMR spectroscopy. A variety of natural, derivatized and designed hyperpolarized probes has emerged for diverse biological studies including assays of intracellular reaction progression, pathway kinetics, probe uptake and export, pH, redox state, reactive oxygen species, ion concentrations, drug efficacy or oncogenic signaling. These probes are readily used directly under natural conditions in biofluids and are often directly developed and optimized for cellular assays, thus leaving little doubt about their specificity and utility under biologically relevant conditions. Hyperpolarized molecular probes for biological NMR spectroscopy enable the unbiased detection of complex processes by virtue of the high spectral resolution, structural specificity and quantifiability of NMR signals. Here, we provide a survey of strategies used for the selection, design and use of hyperpolarized NMR probes in biological assays, and describe current limitations and developments.

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.

Fast and accurate quantitative metabolic profiling of body fluids by nonlinear sampling of 1H–13C two-dimensional nuclear magnetic resonance spectroscopy

Two-dimensional (2D) nuclear magnetic resonance (NMR) methods have shown to be an excellent analytical tool for the identification and characterization of statistically relevant changes in low-abundance metabolites in body fluid. The advantage of 2D NMR in terms of minimized ambiguities in peak assignment, aided in metabolite identifications and comprehensive metabolic profiling comes with the cost of increased NMR data collection time; making it inconvenient choice for routine metabolic profiling. We present here a method for the reduction in NMR data collection time of 2D (1)H-(13)C NMR spectroscopy for the purpose of quantitative metabolic profiling. Our method combines three techniques; which are nonlinear sampling (NLS), forward maximum (FM) entropy reconstruction, and J-compensated quantitative heteronuclear single quantum (HSQC) (1)H-(13)C NMR spectra. We report here that approximately 22-fold reduction in 2D NMR data collection time for the body fluid samples can be achieved by this method, without any compromise in quantitative information recovery of various low abundance metabolites. The method has been demonstrated in standard mixture solution, native, and lyophilized human urine samples. Our proposed method has potential to make quantitative metabolic profiling by 2D NMR as a routine method for various metabonomic studies.

Quantitative NMR for bioanalysis and metabolomics

Over the last several decades, significant technical and experimental advances have made quantitative nuclear magnetic resonance (qNMR) a valuable analytical tool for quantitative measurements on a wide variety of samples. In particular, qNMR has emerged as an important method for metabolomics studies where it is used for interrogation of large sets of biological samples and the resulting spectra are treated with multivariate statistical analysis methods. In this review, recent developments in instrumentation and pulse sequences will be discussed as well as the practical considerations necessary for acquisition of quantitative NMR experiments with an emphasis on their use for bioanalysis. Recent examples of the application of qNMR for metabolomics/metabonomics studies, the characterization of biologicals such as heparin, antibodies, and vaccines, and the analysis of botanical natural products will be presented and the future directions of qNMR discussed.

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.

NMR spectroscopy for discovery and quantitation of biomarkers of disease in human bile

Human liver synthesizes bile; bile, containing a large number of metabolites, is transported through the canaliculi and bile ducts, and stored in the gallbladder before entering into the intestine. In the intestine, a large number of bile metabolites are reabsorbed and sent back to the liver for recirculation. Owing to close association of the bile with the gastrointestinal system, the bile metabolic profile is highly sensitive to the onset of numerous gastrointestinal disease processes. A growing number of studies suggest that hepatobiliary disease biomarkers are richly populated in human bile. These studies stress the potential of profiling the human bile metabolome for early diagnostics as well as deeper insights into gastrointestinal disease processes. Once the biomarkers are established reliably using human bile, they can be targeted in easily accessible fluids such as blood and urine or targeted in bile itself using noninvasive methods such as in vivo magnetic resonance spectroscopy. NMR spectroscopy is one of the most powerful bioanalytical tools, which promises profiling of human bile metabolome and exploring early biomarkers for hepatobiliary diseases. Comprehensive analysis of human bile using NMR spectroscopy has lead to identification and quantification of major bile metabolites. This review describes the discovery and quantitation of biomarkers of hepatobiliary diseases in human bile using NMR spectroscopy.

"Add to subtract": a simple method to remove complex background signals from the 1H nuclear magnetic resonance spectra of mixtures

Because of its highly reproducible and quantitative nature and minimal requirements for sample preparation or separation, (1)H nuclear magnetic resonance (NMR) spectroscopy is widely used for profiling small-molecule metabolites in biofluids. However (1)H NMR spectra contain many overlapped peaks. In particular, blood serum/plasma and diabetic urine samples contain high concentrations of glucose, which produce strong peaks between 3.2 ppm and 4.0 ppm. Signals from most metabolites in this region are overwhelmed by the glucose background signals and become invisible. We propose a simple "Add to Subtract" background subtraction method and show that it can reduce the glucose signals by 98% to allow retrieval of the hidden information. This procedure includes adding a small drop of concentrated glucose solution to the sample in the NMR tube, mixing, waiting for an equilibration time, and acquisition of a second spectrum. The glucose-free spectra are then generated by spectral subtraction using Bruker Topspin software. Subsequent multivariate statistical analysis can then be used to identify biomarker candidate signals for distinguishing different types of biological samples. The principle of this approach is generally applicable for all quantitative spectral data and should find utility in a variety of NMR-based mixture analyses as well as in metabolite profiling.

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.

Advances in metabolite identification

One of the central challenges to metabolomics is metabolite identification. Regardless of whether one uses so-called 'targeted' or 'untargeted' metabolomics, eventually all paths lead to the requirement of identifying (and quantifying) certain key metabolites. Indeed, without metabolite identification, the results of any metabolomic analysis are biologically and chemically uninterpretable. Given the chemical diversity of most metabolomes and the character of most metabolomic data, metabolite identification is intrinsically difficult. Consequently a great deal of effort in metabolomics over the past decade has been focused on making metabolite identification better, faster and cheaper. This review describes some of the newly emerging techniques or technologies in metabolomics that are making metabolite identification easier and more robust. In particular, it focuses on advances in metabolite identification that have occurred over the past 2 to 3 years concerning the technologies, methodologies and software as applied to NMR, MS and separation science. The strengths and limitations of some of these approaches are discussed along with some of the important trends in metabolite identification.

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.

Targeted projection NMR spectroscopy for unambiguous metabolic profiling of complex mixtures

Unambiguous identification of individual metabolites present in complex mixtures such as biofluids constitutes a crucial prerequisite for quantitative metabolomics, toward better understanding of biochemical processes in living systems. Increasing the dimensionality of a given NMR correlation experiment is the natural solution for resolving spectral overlap. However, in the context of metabolites, natural abundance acquisition of (1)H and (13)C NMR data virtually excludes the use of higher dimensional NMR experiments (3D, 4D, etc.) that would require unrealistically long acquisition times. Here, we introduce projection NMR techniques for studies of complex mixtures, and we show how discrete sets of projection spectra from higher dimensional NMR experiments are obtained in a reasonable time frame, in order to capture essential information necessary to resolve assignment ambiguities caused by signal overlap in conventional 2D NMR spectra. We determine optimal projection angles where given metabolite resonances will have the least overlap, to obtain distinct metabolite assignment in complex mixtures. The method is demonstrated for a model mixture composition made of ornithine, putrescine and arginine for which acquisition of a single 2D projection of a 3D (1)H-(13)C TOCSY-HSQC spectrum allows to disentangle the metabolite signals and to access to complete profiling of this model mixture in the targeted 2D projection plane.

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.