Quantification of Metabolites from Two-Dimensional Nuclear Magnetic Resonance Spectroscopy: Application to Human Urine Samples

A novel approach for quantitative determination of cellulose content in tobacco via 2D HSQC NMR spectroscopy

Cellulose is an important component of tobacco (Nicotiana tabacum L.) cell walls, which can be precursors for many harmful compounds in smoke. Traditional cellulose content analysis methods involve sequential extraction and separation steps, which are time-consuming and environmentally unfriendly. In this study, a novel method was first introduced to analyze cellulose content in tobacco via two-dimensional heteronuclear single quantum coherence (2D HSQC) NMR spectroscopy. The method was based on derivatization approach to allow the dissolution of insoluble polysaccharide fractions of tobacco cell walls in DMSO‑d₆/pyridine-d₅ (4:1 v/v) for NMR analysis. The NMR results suggested that besides the main NMR signals of cellulose, partial signals of hemicellulose including mannopyranose, arabinofuranose, and galactopyranose units could also be identified. In addition, the utilization of relaxation reagents has proved to be an effective way to improve the sensitivity of 2D NMR spectroscopy, which was beneficial for quantification of biological samples with limited quantities. To overcome the limitations of quantification using 2D NMR, the calibration curve of cellulose with 1,3,5-trimethoxybenzene as internal reference was constructed and thus the accurate measurement of cellulose in tobacco was achieved. Compared with the chemical method, the interesting method was simple, reliable, and environmentally friendly, which provided a new insight for quantitative determination and structure analysis of plant macromolecules in complex samples.

Intrinsically Disordered Chromatin Protein NUPR1 Binds to the Enzyme PADI4

The nuclear protein 1 (NUPR1) is an intrinsically disordered protein involved in stress-mediated cellular conditions. Its paralogue nuclear protein 1-like (NUPR1L) is p53-regulated, and its expression down-regulates that of the NUPR1 gene. Peptidyl-arginine deiminase 4 (PADI4) is an isoform of a family of enzymes catalyzing arginine to citrulline conversion; it is also involved in stress-mediated cellular conditions. We characterized the interaction between NUPR1 and PADI4 in vitro, in silico, and in cellulo. The interaction of NUPR1 and PADI4 occurred with a dissociation constant of 18 ± 6 μM. The binding region of NUPR1, mapped by NMR, was a hydrophobic polypeptide patch surrounding the key residue Ala33, as pinpointed by: (i) computational results; and, (ii) site-directed mutagenesis of residues of NUPR1. The association between PADI4 and wild-type NUPR1 was also assessed in cellulo by using proximity ligation assays (PLAs) and immunofluorescence (IF), and it occurred mainly in the nucleus. Moreover, binding between NUPR1L and PADI4 also occurred in vitro with an affinity similar to that of NUPR1. Molecular modelling provided information on the binding hot spot for PADI4. This is an example of a disordered partner of PADI4, whereas its other known interacting proteins are well-folded. Altogether, our results suggest that the NUPR1/PADI4 complex could have crucial functions in modulating DNA-repair, favoring metastasis, or facilitating citrullination of other proteins.

Mechanistic analysis by NMR spectroscopy: A users guide

A 'principles and practice' tutorial-style review of the application of solution-phase NMR in the analysis of the mechanisms of homogeneous organic and organometallic reactions and processes. This review of 345 references summarises why solution-phase NMR spectroscopy is uniquely effective in such studies, allowing non-destructive, quantitative analysis of a wide range of nuclei common to organic and organometallic reactions, providing exquisite structural detail, and using instrumentation that is routinely available in most chemistry research facilities. The review is in two parts. The first comprises an introduction to general techniques and equipment, and guidelines for their selection and application. Topics include practical aspects of the reaction itself, reaction monitoring techniques, NMR data acquisition and processing, analysis of temporal concentration data, NMR titrations, DOSY, and the use of isotopes. The second part comprises a series of 15 Case Studies, each selected to illustrate specific techniques and approaches discussed in the first part, including in situ NMR (^1/2H, ^10/11B, ¹³C, ¹⁵N, ¹⁹F, ²⁹Si, ³¹P), kinetic and equilibrium isotope effects, isotope entrainment, isotope shifts, isotopes at natural abundance, scalar coupling, kinetic analysis (VTNA, RPKA, simulation, steady-state), stopped-flow NMR, flow NMR, rapid injection NMR, pure shift NMR, dynamic nuclear polarisation, ¹H/¹⁹F DOSY NMR, and in situ illumination NMR.

Rapid quantitative 1H–13C two-dimensional NMR with high precision

Two dimensional (2D) ¹H–¹³C heteronuclear single-quantum correlation (HSQC) spectroscopy has recently been proposed for quantitative determination of typical linear low density polyethylene (LLDPE) with high accuracy. It requires highly precise measurement to achieve further reliable quantification. In this context, this paper aims at determining conditions that allow the achievement of high precision. On the basis of the optimized parameters, two time-saving strategies, nonuniform sampling (NUS) and band-selective HSQC are evaluated on model polyolefins in terms of repeatability. Precision better than 0.3% and 5% for ethylene content (E mol%) and 1-hexene content (H mol%) of the model poly(ethylene-co-1-hexene)s are obtained with 50% NUS or band-selective HSQC. Moreover, dramatic precision enhancements can be achieved with the combination of band-selective HSQC and 50% NUS, in which repeatabilities better than 0.15% and 2.5% for E mol% and H mol% are observed. The experiment times are reduced to about 0.5 h. These methods open important perspectives for rapid, precise and accurate quantitative analysis of complex polymers.

Precision better than 2.5% can be achieved with the combination of band-selective HSQC and 50% NUS in 0.5 h.

Quantitative NMR as a Versatile Tool for the Reference Material Preparation

The assessment of primary calibrator purity is critical for establishing traceability to the International System of Units (SI). Recently, quantitative nuclear magnetic resonance (qNMR) has been used as a purity determination method for reference material development, and many related measurement techniques have been designed to acquire accurate and reliable results. This review introduces the recent advances in these techniques (including multidimensional methods), focusing on the application of qNMR to reference material preparation.

Fast quantitative 2D NMR for metabolomics and lipidomics: A tutorial

Nuclear magnetic resonance (NMR) is a well-known analytical technique for the analysis of complex mixtures. Its quantitative capability makes it ideally suited to metabolomics or lipidomics studies involving large sample collections of complex biological samples. To overcome the ubiquitous limitation of spectral overcrowding when recording 1D NMR spectra on such samples, the acquisition of 2D NMR spectra allows a better separation between overlapped resonances while yielding accurate quantitative data when appropriate analytical protocols are implemented. Moreover, the experiment duration can be considerably reduced by applying fast acquisition methods. Here, we describe the general workflow to acquire fast quantitative 2D NMR spectra in the "omics" context. It is illustrated on three representative and complementary experiments: UF COSY, ZF-TOCSY with nonuniform sampling, and HSQC with nonuniform sampling. After giving some details and recommendations on how to apply this protocol, its implementation in the case of targeted and untargeted metabolomics/lipidomics studies is described.

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

Dietary methionine restriction reduces hepatic steatosis and oxidative stress in high-fat-fed mice by promoting H2S production

Dietary methionine restriction reduces hepatic steatosis and oxidative stress in high-fat-fed mice by promoting H₂S production.

SCoT: Swept coherence transfer for quantitative heteronuclear 2D NMR

Nuclear magnetic resonance (NMR) spectroscopy is frequently applied in quantitative chemical analysis (qNMR). It is easy to measure one-dimensional (1D) NMR spectra in a quantitative regime (with appropriately long relaxation delays and acquisition times); however, their applicability is limited in the case of complex samples with severe peak overlap. Two-dimensional (2D) NMR solves the overlap problem, but at the cost of biasing peak intensities and hence quantitativeness. This is partly due to the uneven coherence transfer between excited/detected ¹H nuclei and the heteronuclei coupled to them (typically ¹³C). In the traditional approach, the transfer occurs via the evolution of a spin system state under the J-coupling Hamiltonian during a delay of a fixed length. The delay length is set on the basis of the predicted average coupling constant in the sample. This leads to disturbances for pairs of nuclei with coupling constants deviating from this average. Here, we present a novel approach based on non-standard processing of the data acquired in experiments, where the coherence transfer delay is co-incremented with non-uniformly sampled evolution time. This method allows us to obtain the optimal transfer for all resonances, which improves quantitativeness. We demonstrate the concept for the coherence transfer and multiplicity-edit delays in a heteronuclear single-quantum correlation experiment (HSQC).

An adapted isotope dilution 1H-13C heteronuclear single-quantum correlation (ID-HSQC) for rapid and accurate quantification of endogenous and exogenous plasma glucose

A wide variety of methods, such as enzymatic methods, LC-MS, and LC-MS/MS, are currently available for the concentration determination of plasma glucose in studies of diabetes, obesity, exercise, etc. However, these methods rarely discriminate endogenous and exogenous glucose in plasma. A novel NMR strategy for discriminative quantification of the endogenous and exogenous glucose in plasma has been developed using an adapted isotope dilution ¹H-¹³C heteronuclear single-quantum correlation (ID-HSQC) with uniformly ¹³C-labeled glucose as a tracer of exogenous glucose. This method takes advantage of the distinct ¹H-¹³C chemical shifts of the hemiacetal group of the α-D-glucopyranose and makes use of the ¹³C-¹³C one-bond J-coupling (¹J_CC) in uniformly ¹³C-labeled glucose to differentiate the ¹H-¹³C HSQC signal of labeled glucose from that of its natural counterpart when data are acquired in high-resolution mode. The molar ratio between the endogenous and exogenous plasma glucose can then be calculated from the peak intensities of the natural and labeled glucose. The accuracy and precision of the method were evaluated using a series of standard mixtures of natural and uniformly ¹³C-labeled glucose with varied but known concentrations. Application of this method is demonstrated for the quantification of endogenous and exogenous glucose in plasma derived from healthy and diabetic cynomolgus monkeys. The results nicely agree with our previous LC-MS/MS results. Considering the natural abundance of ¹³C isotope at the level of 1.1% in endogenous glucose, comparable peak intensities of quantitatively measurable signals derived from natural and labeled glucose imply that the ID-HSQC can tolerate a significantly high ratio of isotope dilution, with labeled/natural glucose at ~ 1%. We expect that the ID-HSQC method can serve as an alternative approach to the biomedical or clinical studies of glucose metabolism.

The FAQUIRE Approach: FAst, QUantitative, hIghly Resolved and sEnsitivity Enhanced 1H, 13C Data

The targeted analysis of metabolites in complex mixtures is a challenging issue. NMR is one of the major tools in this field, but there is a strong need for more sensitive, better-resolved, and faster quantitative methods. In this framework, we introduce the concept of FAst, QUantitative, hIghly Resolved and sEnsitivity enhanced (FAQUIRE) NMR to push forward the limits of metabolite NMR analysis. 2D ¹H, ¹³C 2D quantitative maps are promising alternatives for enhancing the spectral resolution but are highly time-consuming because of (i) the intrinsic nature of 2D, (ii) the longer recycling times required for quantitative conditions, and (iii) the higher number of scans needed to reduce the level of detection/quantification to access low concentrated metabolites. To reach this aim, speeding up the recently developed QUantItative Perfected and pUre shifted HSQC (QUIPU HSQC) is an interesting attempt to develop the FAQUIRE concept. Thanks to the combination of spectral aliasing, nonuniform sampling, and variable repetition time, the acquisition time of 2D quantitative maps is reduced by a factor 6 to 9, while conserving a high spectral resolution thanks to a pure shift approach. The analytical potential of the new Quick QUIPU HSQC (Q QUIPU HSQC) is evaluated on a model metabolite sample, and its potential is shown on breast-cell extracts embedding metabolites at millimolar to submillimolar concentrations.

Intrinsically disordered chromatin protein NUPR1 binds to the C-terminal region of Polycomb RING1B

Intrinsically disordered proteins (IDPs) are ubiquitous in eukaryotes, and they are often associated with diseases in humans. The protein NUPR1 is a multifunctional IDP involved in chromatin remodeling and in the development and progression of pancreatic cancer; however, the details of such functions are unknown. Polycomb proteins are involved in specific transcriptional cascades and gene silencing. One of the proteins of the Polycomb complex is the Ring finger protein 1 (RING1). RING1 is related to aggressive tumor features in multiple cancer types. In this work we characterized the interaction between NUPR1 and the paralogue RING1B in vitro, in silico, and in cellulo. The interaction occurred through the C-terminal region of RING1B (C-RING1B), with an affinity in the low micromolar range (∼10 μM). The binding region of NUPR1, mapped by NMR, was a hydrophobic polypeptide patch at the 30s region of its sequence, as pinpointed by computational results and site-directed mutagenesis at Ala33. The association between C-RING1B and wild-type NUPR1 also occurred in cellulo as tested by protein ligation assays; this interaction is inhibited by trifluoperazine, a drug known to hamper binding of wild-type NUPR1 with other proteins. Furthermore, the Thr68Gln and Ala33Gln/Thr68Gln mutants had a reduction in the binding toward C-RING1B as shown by in vitro, in silico, and in cellulo studies. This is an example of a well-folded partner of NUPR1, because its other interacting proteins are also unfolded. We hypothesize that NUPR1 plays an active role in chromatin remodeling and carcinogenesis, together with Polycomb proteins.

Detection of Lipid and Amphiphilic Biomarkers for Disease Diagnostics

Rapid diagnosis is crucial to effectively treating any disease. Biological markers, or biomarkers, have been widely used to diagnose a variety of infectious and non-infectious diseases. The detection of biomarkers in patient samples can also provide valuable information regarding progression and prognosis. Interestingly, many such biomarkers are composed of lipids, and are amphiphilic in biochemistry, which leads them to be often sequestered by host carriers. Such sequestration enhances the difficulty of developing sensitive and accurate sensors for these targets. Many of the physiologically relevant molecules involved in pathogenesis and disease are indeed amphiphilic. This chemical property is likely essential for their biological function, but also makes them challenging to detect and quantify in vitro. In order to understand pathogenesis and disease progression while developing effective diagnostics, it is important to account for the biochemistry of lipid and amphiphilic biomarkers when creating novel techniques for the quantitative measurement of these targets. Here, we review techniques and methods used to detect lipid and amphiphilic biomarkers associated with disease, as well as their feasibility for use as diagnostic targets, highlighting the significance of their biochemical properties in the design and execution of laboratory and diagnostic strategies. The biochemistry of biological molecules is clearly relevant to their physiological function, and calling out the need for consideration of this feature in their study, and use as vaccine, diagnostic and therapeutic targets is the overarching motivation for this review.

The Emperor's new clothes: Myths and truths of in-cell NMR

In-cell NMR is a technique developed to study the structure and dynamical behavior of biological macromolecules in their natural environment, circumventing all isolation and purification steps. In principle, the potentialities of the technique are enormous, not only for the possibility of bypassing all purification steps but, even more importantly, for the wealth of information that can be gained from directly monitoring interactions among biological macromolecules in a natural cell. Here, we review critically the promises, successes and limits of this technique as it stands now. Interestingly, many of the problems of NMR in bacterial cells stem from the artificially high concentration of the protein under study whose overexpression is anyway necessary to select it from the background. This has, as a consequence, that when overexpressed, most globular proteins, do not show an NMR spectrum, limiting the applicability of the technique to intrinsically unfolded or specifically behaving proteins. The outlook for in-cell NMR of eukaryotic cells is more promising and is possibly the most attracting aspect for the future.

Fast profiling of metabolite mixtures using chemometric analysis of a speeded-up 2D heteronuclear correlation NMR experiment

One-dimensional (1D) NMR spectra of mixtures of metabolites suffer from severe overlap of spectral resonances and hence recent research in NMR-based metabolomics focuses on using two-dimensional (2D) NMR experiments for metabolite fingerprinting.

Challenges and perspectives in quantitative NMR

This perspective article summarizes, from the author's point of view at the beginning of 2016, the major challenges and perspectives in the field of quantitative NMR. The key concepts in quantitative NMR are first summarized; then, the most recent evolutions in terms of resolution and sensitivity are discussed, as well as some potential future research directions in this field. A particular focus is made on methodologies capable of boosting the resolution and sensitivity of quantitative NMR, which could open application perspectives in fields where the sample complexity and the analyte concentrations are particularly challenging. These include multi-dimensional quantitative NMR and hyperpolarization techniques such as para-hydrogen-induced polarization or dynamic nuclear polarization. Because quantitative NMR cannot be dissociated from the key concepts of analytical chemistry, i.e. trueness and precision, the methodological developments are systematically described together with their level of analytical performance. Copyright © 2016 John Wiley & Sons, Ltd.

NMR-Based Metabolic Snapshot from Minibronchoalveolar Lavage Fluid: An Approach To Unfold Human Respiratory Metabolomics

The utility of mini bronchoalveolar lavage (mBAL) and its applicability in metabolomics has not been explored in the field of human respiratory disease. mBAL, "an archetype" of the local lung environment, ensures a potent technique to get the snapshot of the epithelial lining fluid afflicted to human lung disorders. Characterization of the mBAL fluid has potential to help in elucidating the composition of the alveoli and airways in the diseased state, yielding diagnostic information on clinical applicability. In this study, one of the first attempts has been made to comprehensively assign and detect metabolites in mBAL fluid, extracted from human lungs, by the composite use of 800 MHz 1D and 2D NMR, J-resolved homonuclear spectroscopy, COSY, TOCSY, and heteronuclear HSQC correlation methods. A foremost all-inclusive sketch of the 50 metabolites has been corroborated and assigned, which can be a resourceful archive to further lung-directed metabolomics, prognosis, and diagnosis. Thus, NMR-based mBALF studies, as proposed in this article, will leverage many more prospective respiratory researches for routine clinical application and prove to be a viable approach to mirror the key predisposing factors contributing to the onset of lung 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.

Direct Analysis of Free and Sulfite-Bound Carbonyl Compounds in Wine by Two-Dimensional Quantitative Proton and Carbon Nuclear Magnetic Resonance Spectroscopy

Recent developments that have accelerated 2D NMR methods and improved quantitation have made these methods accessible analytical procedures, and the large signal dispersion allows for the analysis of complex samples. Few natural samples are as complex as wine, so the application to challenges in wine analysis look promising. The analysis of carbonyl compounds in wine, key oxidation products, is complicated by a multitude of kinetically reversible adducts, such as acetals and sulfonates, so that sample preparation steps can generate complex interferences. These challenges could be overcome if the compounds could be quantified in situ. Here, two-dimensional ((1)H-(1)H) homonuclear and heteronuclear ((13)C-(1)H) single quantum correlations (correlation spectroscopy, COSY, and heteronuclear single quantum coherence, HSQC) nuclear magnetic resonance spectra of undiluted wine samples were observed at natural abundance. These techniques achieve simultaneous direct identification and quantitation of acetaldehyde, pyruvic acid, acetoin, methylglyoxal, and α-ketoglutaric acid in wine with only a small addition of D2O. It was also possible to observe and sometimes quantify the sulfite, hydrate, and acetal forms of the carbonyl compounds. The accuracy of the method was tested in wine samples by spiking with a mixture of all analytes at different concentrations. The method was applied to 15 wine samples of various vintages and grape varieties. The application of this method could provide a powerful tool to better understand the development, evolution, and perception of wine oxidation and insight into the impact of these sulfite bound carbonyls on antimicrobial and antioxidant action by SO2.

Nuclear magnetic resonance: a key metabolomics platform in the drug discovery process

Metabolomics is an innovative tool that is now emerging in the drug discovery process. Indeed, its ability to follow the dynamic perturbations in the metabolome resulting from pathologies but also from drug treatment and or/toxicity is of value for the development of new therapeutic approaches. Nuclear magnetic resonance (NMR) spectroscopy, which is an important analytical technique for several steps of the lead discovery, validation and optimization processes, has been described, together with mass spectrometry (MS) as one of the major platform that could be used for metabolomics studies. This review highlights why NMR could be considered a key tool for the application of metabolomics in drug discovery.

Quantitative metabolic profiling of NMR spectral signatures of branched chain amino acids in blood serum

Branched Chain Amino Acids (BCAAs) are related to different aspects of diseases like pathogenesis, diagnosis and even prognosis. While in some diseases, levels of all the BCAAs are perturbed; in some cases, perturbation occurs in one or two while the rest remain unaltered. In case of ischemic heart disease, there is an enhanced level of plasma leucine and isoleucine but valine level remains unaltered. In 'Hypervalinemia', valine is elevated in serum and urine, but not leucine and isoleucine. Therefore, identification of these metabolites and profiling of individual BCAA in a quantitative manner in body-fluid like blood plasma/serum have long been in demand. (1)H NMR resonances of the BCAAs overlap with each other which complicates quantification of individual BCAAs. Further, the situation is limited by the overlap of broad resonances of lipoprotein with the resonances of BCAAs. The widely used commercially available kits cannot differentially estimate the BCAAs. Here, we have achieved proper identification and characterization of these BCAAs in serum in a quantitative manner employing a Nuclear Magnetic Resonance-based technique namely T2-edited Correlation Spectroscopy (COSY). This approach can easily be extended to other body fluids like bile, follicular fluids, saliva, etc.

Unified and isomer-specific NMR metabolomics database for the accurate analysis of (13)C-(1)H HSQC spectra

A new metabolomics database and query algorithm for the analysis of ¹³C–¹H HSQC spectra is introduced, which unifies NMR spectroscopic information on 555 metabolites from both the Biological Magnetic Resonance Data Bank (BMRB) and Human Metabolome Database (HMDB). The new database, termed Complex Mixture Analysis by NMR (COLMAR) ¹³C–¹H HSQC database, can be queried via an interactive, easy to use web interface at http://spin.ccic.ohio-state.edu/index.php/hsqc/index. Our new HSQC database separately treats slowly exchanging isomers that belong to the same metabolite, which permits improved query in cases where lowly populated isomers are below the HSQC detection limit. The performance of our new database and query web server compares favorably with the one of existing web servers, especially for spectra of samples of high complexity, including metabolite mixtures from the model organisms Drosophila melanogaster and Escherichia coli. For such samples, our web server has on average a 37% higher accuracy (true positive rate) and a 82% lower false positive rate, which makes it a useful tool for the rapid and accurate identification of metabolites from ¹³C–¹H HSQC spectra at natural abundance. This information can be combined and validated with NMR data from 2D TOCSY-type spectra that provide connectivity information not present in HSQC spectra.

Quantification of multiple compounds containing heterogeneous elements in the mixture by one-dimensional nuclear magnetic resonance spectroscopy of different nuclei using a single universal concentration reference

One-dimensional (1D) quantitative NMR (qNMR) is a useful tool for concentration determination due to its experimental simplicity and the direct proportionality of the integrated signal area to the number of nuclei spin. For complex mixtures, however, signal overlapping often in one-dimensional quantitative (1) H NMR (1D (1) H qNMR) spectrum limits the accurate quantification of individual compound. Here, we introduced employing joint 1D qNMR methods of different nuclei, such as (1) H and (31) P (or/and (19) F), to quantify multiple compounds in a complex mixture using a single universal concentration reference. When the concentration ratio of several compounds containing different elements in a complex mixture is of interest, the result calculated from measured intensities from 1D qNMR of different nuclei is independent of the gravimetric error from the reference. In this case, the common reference also serves as a 'quantitative bridge' among these 1D qNMR of different nuclei. Quantitative analysis of choline, phosphocholine, and glycerophosphocholine mixture is given as an example using trimethylphosphine oxide ((CH(3))(3) P(O)) as concentration reference. Compounds containing multiple elements, such as tetramethylammonium hexafluorophosphate (N(+) (CH(3))(4 PF6 (-) are proposed as the common concentration reference for (1) H, (13) C, (15) N, (31) P, and (19) F qNMR for the quantitative analysis of complex mixture containing these different elements. We anticipate that the proposed joint 1D qNMR approach using a universal concentration reference will be a valuable alternative for simultaneous quantification of multiple compounds in a complex mixture due to its accuracy and single and simple sample preparation.

Quantitative 2D liquid-state NMR

Two-dimensional (2D) liquid-state NMR has a very high potential to simultaneously determine the absolute concentration of small molecules in complex mixtures, thanks to its capacity to separate overlapping resonances. However, it suffers from two main drawbacks that probably explain its relatively late development. First, the 2D NMR signal is strongly molecule-dependent and site-dependent; second, the long duration of 2D NMR experiments prevents its general use for high-throughput quantitative applications and affects its quantitative performance. Fortunately, the last 10 years has witnessed an increasing number of contributions where quantitative approaches based on 2D NMR were developed and applied to solve real analytical issues. This review aims at presenting these recent efforts to reach a high trueness and precision in quantitative measurements by 2D NMR. After highlighting the interest of 2D NMR for quantitative analysis, the different strategies to determine the absolute concentrations from 2D NMR spectra are described and illustrated by recent applications. The last part of the manuscript concerns the recent development of fast quantitative 2D NMR approaches, aiming at reducing the experiment duration while preserving - or even increasing - the analytical performance. We hope that this comprehensive review will help readers to apprehend the current landscape of quantitative 2D NMR, as well as the perspectives that may arise from it.

1H NMR detects different metabolic profiles in glioblastoma stem-like cells

The metabolic profiles of glioblastoma stem-like cells (GSCs) growing in neurospheres were examined by (1)H NMR spectroscopy. Spectra of two GSC lines, labelled 1 and 83, from tumours close to the subventricular zone of the temporal lobe were studied in detail and compared with those of neural stem/progenitor cells from the adult olfactory bulb (OB-NPCs) and of the T98G glioblastoma cell line. In both GSCs, signals from myoinositol (Myo-I), UDP-hexosamines (UDP-Hex) and glycine indicated an astrocyte/glioma metabolism. For line 1, the presence of signals from N-acetyl aspartate, GABA and creatine pointed to a neuronal fingerprint. These metabolites were almost absent from line 83 spectra, whereas lipid signals, absent from normal neural lineages, were intense in line 83 spectra and remained low in those of line 1, irrespective of apoptotic fate. Spectra of OB-NPC cells displayed strong similarities with those from line 1, with low lipid signals and clearly detectable neuronal signals. In contrast, the spectral profile of line 83 was more similar to that of T98G, displaying high lipids and nearly complete absence of the neuronal markers. A mixed neural-astrocyte metabolic phenotype with a strong neuronal fingerprint was therefore found in line 1, while an astrocytic/glioma-like metabolism prevailed in line 83. We found a signal assigned to the amide proton of N-acetyl galactosamine in GSC lines and in OB-NPC spectra, whereas it was absent from those of T98G cells. This signal may be related to a stem-cell-specific protein glycosylation pattern and is therefore suggested as a marker of cell multipotency. Other GSC lines from patients with different clinical outcomes were then examined. Unsupervised analysis of spectral data from 13 lines yielded two clusters, with six lines resembling spectral features of line 1 and seven resembling those of line 83, suggesting that distinct metabolic phenotypes may be present in GSC lines.

MetaboQuant: a tool combining individual peak calibration and outlier detection for accurate metabolite quantification in 1D (1)H and (1)H-(13)C HSQC NMR spectra

Solution nuclear magnetic resonance (NMR) spectroscopy is widely used to analyze complex mixtures of organic compounds such as biological fluids and tissue extracts. Targeted profiling approaches with reliable compound quantitifcation are hampered, however, by signal overlap and other interferences. Here, we present a tool named MetaboQuant for automated compound quantification from pre-processed 1D and 2D heteronuclear single quantum coherence (HSQC) NMR spectral data and concomitant validation of results. Performance of MetaboQuant was tested on a urinary spike-in data set and compared with other quantification strategies. The use of individual calibration factors in combination with the validation algorithms of MetaboQuant raises the reliability of the quantification results. MetaboQuant can be downloaded at http://genomics.uni-regensburg.de/site/institute/software/metaboquant/ as stand-alone software for Windows or run on other operating systems from within Matlab. Separate software for peak fitting and integration is necessary in order to use MetaboQuant.

Quantitative analysis of metabolic mixtures by two-dimensional 13C constant-time TOCSY NMR spectroscopy

An increasing number of organisms can be fully (13)C-labeled, which has the advantage that their metabolomes can be studied by high-resolution two-dimensional (2D) NMR (13)C-(13)C constant-time (CT) total correlation spectroscopy (TOCSY) experiments. Individual metabolites can be identified via database searching or, in the case of novel compounds, through the reconstruction of their backbone-carbon topology. Determination of quantitative metabolite concentrations is another key task. Because strong peak overlaps in one-dimensional (1D) NMR spectra prevent straightforward quantification through 1D peak integrals, we demonstrate here the direct use of (13)C-(13)C CT-TOCSY spectra for metabolite quantification. This is accomplished through the quantum mechanical treatment of the TOCSY magnetization transfer at short and long-mixing times or by the use of analytical approximations, which are solely based on the knowledge of the carbon-backbone topologies. The methods are demonstrated for carbohydrate and amino acid mixtures.

The presence of two cyclase thioesterases expands the conformational freedom of the cyclic Peptide occidiofungin

Occidiofungin is a cyclic nonribosomally synthesized antifungal peptide with submicromolar activity produced by the Gram-negative bacterium Burkholderia contaminans. The biosynthetic gene cluster was confirmed to contain two cyclase thioesterases. NMR analysis revealed that the presence of both thioesterases is used to increase the conformational repertoire of the cyclic peptide. The loss of the OcfN cyclic thioesterase by mutagenesis results in a reduction of conformational variants and an appreciable decrease in bioactivity against Candida species. Presumably, the presence of both asparagine and β-hydroxyasparagine variants coordinates the enzymatic function of both of the cyclase thioesterases. OcfN has presumably evolved to be part of the biosynthetic gene cluster due to its ability to produce structural variants that enhance antifungal activity against some fungi. The enhancement of the antifungal activity from the incorporation of an additional cyclase thioesterase into the biosynthetic gene cluster of occidiofungin supports the need to explore new conformational variants of other therapeutic or potentially therapeutic cyclic peptides.

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.

Absolute quantification of metabolites in breast cancer cell extracts by quantitative 2D (1) H INADEQUATE NMR

Metabolomic studies by NMR spectroscopy are increasingly employed for a variety of biomedical applications. A very standardized 1D proton NMR protocol is generally employed for data acquisition, associated with multivariate statistical tests. Even if targeted approaches have been proposed to quantify metabolites from such experiments, quantification is often made difficult by the high degree of overlap characterizing (1) H NMR spectra of biological samples. Two-dimensional spectroscopy presents a high potential for accurately measuring concentrations in complex samples, as it offers a much higher discrimination between metabolite resonances. We have recently proposed an original approach relying on the (1) H 2D INADEQUATE pulse sequence, optimized for fast quantitative analysis of complex metabolic mixtures. Here, the first application of the quantitative (1) H 2D INADEQUATE experiment to a real metabonomic study is presented. Absolute metabolite concentrations are determined for different breast cancer cell line extracts, by a standard addition procedure. The protocol is characterized by high analytical performances (accuracy better than 1%, excellent linearity), even if it is affected by relatively long acquisition durations (15 min to 1 h per spectrum). It is applied to three different cell lines, expressing different hormonal and tyrosine kinase receptors. The absolute concentrations of 15 metabolites are determined, revealing significant differences between cell lines. The metabolite concentrations measured are in good agreement with previous studies regarding metabolic profile changes of breast cancer. While providing a high degree of discrimination, this methodology offers a powerful tool for the determination of relevant biomarkers.

Quantitative 1H NMR. Development and potential of an analytical method: an update

Covering the literature from mid-2004 until the end of 2011, this review continues a previous literature overview on quantitative (1)H NMR (qHNMR) methodology and its applications in the analysis of natural products. Among the foremost advantages of qHNMR is its accurate function with external calibration, the lack of any requirement for identical reference materials, a high precision and accuracy when properly validated, and an ability to quantitate multiple analytes simultaneously. As a result of the inclusion of over 170 new references, this updated review summarizes a wealth of detailed experiential evidence and newly developed methodology that supports qHNMR as a valuable and unbiased analytical tool for natural product and other areas of research.

Measurement of absolute concentrations of individual compounds in metabolite mixtures by gradient-selective time-zero 1H-13C HSQC with two concentration references and fast maximum likelihood reconstruction analysis

Time-zero 2D (13)C HSQC (HSQC(0)) spectroscopy offers advantages over traditional 2D NMR for quantitative analysis of solutions containing a mixture of compounds because the signal intensities are directly proportional to the concentrations of the constituents. The HSQC(0) spectrum is derived from a series of spectra collected with increasing repetition times within the basic HSQC block by extrapolating the repetition time to zero. Here we present an alternative approach to data collection, gradient-selective time-zero (1)H-(13)C HSQC(0) in combination with fast maximum likelihood reconstruction (FMLR) data analysis and the use of two concentration references for absolute concentration determination. Gradient-selective data acquisition results in cleaner spectra, and NMR data can be acquired in both constant-time and non-constant-time mode. Semiautomatic data analysis is supported by the FMLR approach, which is used to deconvolute the spectra and extract peak volumes. The peak volumes obtained from this analysis are converted to absolute concentrations by reference to the peak volumes of two internal reference compounds of known concentration: DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) at the low concentration limit (which also serves as chemical shift reference) and MES (2-(N-morpholino)ethanesulfonic acid) at the high concentration limit. The linear relationship between peak volumes and concentration is better defined with two references than with one, and the measured absolute concentrations of individual compounds in the mixture are more accurate. We compare results from semiautomated gsHSQC(0) with those obtained by the original manual phase-cycled HSQC(0) approach. The new approach is suitable for automatic metabolite profiling by simultaneous quantification of multiple metabolites in a complex mixture.

NMR quantitation: influence of RF inhomogeneity

The NMR peak integral is ideally linearly dependent on the sine of excitation angle (θ), which has provided unsurpassed flexibility in quantitative NMR by allowing the use of a signal of any concentration as the internal concentration reference. Controlling the excitation angle is particularly critical for solvent proton concentration referencing to minimize the negative impact of radiation damping, and to reduce the risk of receiver gain compression. In practice, due to the influence of RF inhomogeneity for any given probe, the observed peak integral is not exactly proportional to sin θ. To evaluate the impact quantitatively, we introduce a RF inhomogeneity factor I(θ) as a function of the nominal pulse excitation angle and propose a simple calibration procedure. Alternatively, I(θ) can be calculated from the probe's RF profile, which can be readily obtained as a gradient image of an aqueous sample. Our results show that without consideration of I(θ), even for a probe with good RF homogeneity, up to 5% error can be introduced due to different excitation pulse angles used for the analyte and the reference. Hence, a simple calibration of I(θ) can eliminate such errors and allow an accurate description of the observed NMR signal's dependence on the excitation angle in quantitative analysis.

Deconvolution of chemical mixtures with high complexity by NMR consensus trace clustering

Identification and quantification of analytes in complex solution-state mixtures are critical procedures in many areas of chemistry, biology, and molecular medicine. Nuclear magnetic resonance (NMR) is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of the samples. We present three new multidimensional-NMR based approaches that are geared toward the analysis of mixtures with high complexity at natural (13)C abundance, including approaches that are encountered in metabolomics. Common to all three approaches is the concept of the extraction of one-dimensional (1D) consensus spectral traces or 2D consensus planes followed by clustering, which significantly improves the capability to identify mixture components that are affected by strong spectral overlap. The methods are demonstrated for covariance (1)H-(1)H TOCSY and (13)C-(1)H HSQC-TOCSY spectra and triple-rank correlation spectra constructed from pairs of (13)C-(1)H HSQC and (13)C-(1)H HSQC-TOCSY spectra. All methods are first demonstrated for an eight-compound metabolite model mixture before being applied to an extract from E. coli cell lysate.

Simultaneous quantification and identification of individual chemicals in metabolite mixtures by two-dimensional extrapolated time-zero (1)H-(13)C HSQC (HSQC(0))

Quantitative one-dimensional (1D) (1)H NMR spectroscopy is a useful tool for determining metabolite concentrations because of the direct proportionality of signal intensity to the quantity of analyte. However, severe signal overlap in 1D (1)H NMR spectra of complex metabolite mixtures hinders accurate quantification. Extension of 1D (1)H to 2D (1)H-(13)C HSQC leads to the dispersion of peaks along the (13)C dimension and greatly alleviates peak overlapping. Although peaks are better resolved in 2D (1)H-(13)C HSQC than in 1D (1)H NMR spectra, the simple proportionality of cross peaks to the quantity of individual metabolites is lost by resonance-specific signal attenuation during the coherence transfer periods. As a result, peaks for individual metabolites usually are quantified by reference to calibration data collected from samples of known concentration. We show here that data from a series of HSQC spectra acquired with incremented repetition times (the time between the end of the first (1)H excitation pulse to the beginning of data acquisition) can be extrapolated back to zero time to yield a time-zero 2D (1)H-(13)C HSQC spectrum (HSQC(0)) in which signal intensities are proportional to concentrations of individual metabolites. Relative concentrations determined from cross peak intensities can be converted to absolute concentrations by reference to an internal standard of known concentration. Clustering of the HSQC(0) cross peaks by their normalized intensities identifies those corresponding to metabolites present at a given concentration, and this information can assist in assigning these peaks to specific compounds. The concentration measurement for an individual metabolite can be improved by averaging the intensities of multiple, nonoverlapping cross peaks assigned to that metabolite.

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.