Evaluation of Fast 2D NMR for Metabolomics

Systematic Evaluation of Non-Uniform Sampling Parameters in the Targeted Analysis of Urine Metabolites by 1H,1H 2D NMR Spectroscopy

Non-uniform sampling (NUS) allows the accelerated acquisition of multidimensional NMR spectra. The aim of this contribution was the systematic evaluation of the impact of various quantitative NUS parameters on the accuracy and precision of 2D NMR measurements of urinary metabolites. Urine aliquots spiked with varying concentrations (15.6-500.0 µM) of tryptophan, tyrosine, glutamine, glutamic acid, lactic acid, and threonine, which can only be resolved fully by 2D NMR, were used to assess the influence of the sampling scheme, reconstruction algorithm, amount of omitted data points, and seed value on the quantitative performance of NUS in 1H,1H-TOCSY and 1H,1H-COSY45 NMR spectroscopy. Sinusoidal Poisson-gap sampling and a compressed sensing approach employing the iterative re-weighted least squares method for spectral reconstruction allowed a 50% reduction in measurement time while maintaining sufficient quantitative accuracy and precision for both types of homonuclear 2D NMR spectroscopy. Together with other advances in instrument design, such as state-of-the-art cryogenic probes, use of 2D NMR spectroscopy in large biomedical cohort studies seems feasible.

Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet

Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general “linear” multivariate analysis alone limits interpretations due to “non-linear” variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional variable importance measure into our KPCA-based analytical approach to demonstrate the relative importance of metabolites. Using a market basket analysis, hippurate, the most important variable detected in the importance measure, was associated with high levels of some vitamins and minerals present in foods eaten the previous day, suggesting a relationship between increased hippurate and intake of a wide variety of vegetables and fruits. Therefore, the KPCA-incorporated analytical approach described herein enabled us to capture input–output responses, and should be useful not only for metabolic profiling but also for profiling in other areas of biological and environmental systems.

Phase modulated 2D HSQC-TOCSY for unambiguous assignment of overlapping spin systems

We present a new method that allows one to unambiguously resolve overlapping spin systems often encountered in biomolecular systems such as peptides and proteins or in samples containing a mixture of different molecules such as in metabolomics. We address this problem using the recently proposed phase modulation approach. By evolving the ¹H chemical shifts in a conventional two dimensional (2D) HSQC-TOCSY experiment for a fixed delay period, the phase/intensity of set of cross peaks belonging to one spin system are modulated differentially relative to those of its overlapping counterpart, resulting in their discrimination and recognition. The method thus accelerates the process of identification and resonance assignment of individual compounds in complex mixtures. This approach facilitated the assignment of molecules in the embryo culture medium used in human assisted reproductive technology.

SOFAST-HMQC-an efficient tool for metabolomics

Nuclear magnetic resonance (NMR)-based metabolomics relies mostly on 1D NMR; however, the technique is limited by overlap of the signals from the metabolites. In order to circumvent this problem, 2D ¹H-¹³C correlation spectroscopy techniques are often used. However owing to poorer natural abundance and gyromagnetic ratio of ¹³C, the acquisition time for 2D ¹H-¹³C heteronuclear single quantum coherence spectroscopy (HSQC) is long. This makes it almost impossible to be used in high throughput study. We have reported the application of selective optimized flip angle short transient (SOFAST) technique coupled to heteronuclear multiple quantum correlation (HMQC) along with nonlinear sampling (NUS) in urine and serum samples. This technique takes sevenfold less experimental time than the conventional ¹H-¹³C HSQC experiment with retention of almost all molecular information. Hence, this can be used for high throughput study. Graphical abstract SOFAST-HMQC is a two-dimensional NMR technique that significantly decreases experimental time without loss of information. This technique is applied in complex biofluid samples that are used for high throughput metabolomics studies and shows promise of better information recovery than conventional two-dimensional NMR technique in shorter time.

pH detection in biological samples by 1D and 2D 1H-31P NMR

The chemical shifts of several important endogenous phosphorus compounds under different pH conditions were explored, including adenosine-5'-triphosphate, adenosine-5'-diphosphate, adenosine-5'-monophosphate, phosphorylcholine and phosphorylethanolamine. Their ³¹P NMR and ¹H NMR chemical shifts were all pH-sensitive in the similar pH range. Two dimensional (2D) ¹H-³¹P NMR spectra were found helpful to identify these endogenous phosphorus markers in biological samples from rather complicated NMR spectra. Herein, for the first time, a pH sensor based on 2D ¹H-³¹P NMR was established and applied to biological samples analysis with pH values determined in good agreement with those by potentiometric method. Apart from being simple, green, rapid and less sample-consuming, information concerning both the endogenous phosphorus markers and pH status could be attained in a single NMR run, which demonstrated the great potential of this method in rare sample analysis and even disease diagnosis.

NMR-based Stable Isotope Resolved Metabolomics in systems biochemistry

Metabolism is the basic activity of live cells, and monitoring the metabolic state provides a dynamic picture of the cells or tissues, and how they respond to external changes, for in disease or treatment with drugs. NMR is an extremely versatile analytical tool that can be applied to a wide range of biochemical problems. Despite its modest sensitivity its versatility make it an ideal tool for analyzing biochemical dynamics both in vitro and in vivo, especially when coupled with its isotope editing capabilities, from which isotope distributions can be readily determined. These are critical for any analyses of flux in live organisms. This review focuses on the utility of NMR spectroscopy in metabolomics, with an emphasis on NMR applications in stable isotope-enriched tracer research for elucidating biochemical pathways and networks with examples from nucleotide biochemistry. The knowledge gained from this area of research provides a ready link to genomic, epigenomic, transcriptomic, and proteomic information to achieve systems biochemical understanding of living cells and organisms.

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.

Multidimensional NMR approaches towards highly resolved, sensitive and high-throughput quantitative metabolomics

Multi-dimensional NMR is an appealing approach for dealing with the challenging complexity of biological samples in metabolomics. This article describes how spectroscopists have recently challenged their imagination in order to make 2D NMR a powerful tool for quantitative metabolomics, based on innovative pulse sequences combined with meticulous analytical chemistry approaches. Clever time-saving strategies have also been explored to make 2D NMR a high-throughput tool for metabolomics, relying on alternative data acquisition schemes such as ultrafast NMR. Currently, much work is aimed at drastically boosting the NMR sensitivity thanks to hyperpolarisation techniques, which have been used in combination with fast acquisition methods and could greatly expand the application potential of NMR metabolomics.

Cannibalism Affects Core Metabolic Processes in Helicoverpa armigera Larvae-A 2D NMR Metabolomics Study

Cannibalism is known in many insect species, yet its impact on insect metabolism has not been investigated in detail. This study assessed the effects of cannibalism on the metabolism of fourth-instar larvae of the non-predatory insect Helicoverpa armigera (Lepidotera: Noctuidea). Two groups of larvae were analyzed: one group fed with fourth-instar larvae of H. armigera (cannibal), the other group fed with an artificial plant diet. Water-soluble small organic compounds present in the larvae were analyzed using two-dimensional nuclear magnetic resonance (NMR) and principal component analysis (PCA). Cannibalism negatively affected larval growth. PCA of NMR spectra showed that the metabolic profiles of cannibal and herbivore larvae were statistically different with monomeric sugars, fatty acid- and amino acid-related metabolites as the most variable compounds. Quantitation of ¹H-¹³C HSQC (Heteronuclear Single Quantum Coherence) signals revealed that the concentrations of glucose, glucono-1,5-lactone, glycerol phosphate, glutamine, glycine, leucine, isoleucine, lysine, ornithine, proline, threonine and valine were higher in the herbivore larvae.

Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods

Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexity of these tasks.

Assessment of dietary exposure and effect in humans: The role of NMR

In human nutritional science progress has always depended strongly on analytical measurements for establishing relationships between diet and health. This field has undergone significant changes as a result of the development of NMR and mass spectrometry methods for large scale detection, identification and quantification of metabolites in body fluids. This has allowed systematic studies of the metabolic fingerprints that biological processes leave behind, and has become the research field of metabolomics. As a metabolic profiling technique, NMR is at its best when its unbiased nature, linearity and reproducibility are exploited in well-controlled nutritional intervention and cross-sectional population screening studies. Although its sensitivity is less good than that of mass spectrometry, NMR has maintained a strong position in metabolomics through implementation of standardisation protocols, hyphenation with mass spectrometry and chromatographic techniques, accurate quantification and spectral deconvolution approaches, and high-throughput automation. Thus, NMR-based metabolomics has contributed uniquely to new insights into dietary exposure, in particular by unravelling the metabolic fates of phytochemicals and the discovery of dietary intake markers. NMR profiling has also contributed to the understanding of the subtle effects of diet on central metabolism and lipoprotein metabolism. In order to hold its ground in nutritional metabolomics, NMR will need to step up its performance in sensitivity and resolution; the most promising routes forward are the analytical use of dynamic nuclear polarisation and developments in microcoil construction and automated fractionation.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

Metabolomics in rheumatic diseases: desperately seeking biomarkers

Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.

A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments

Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC-MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice.

Emerging new strategies for successful metabolite identification in metabolomics

This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties.

Application of Market Basket Analysis for the Visualization of Transaction Data Based on Human Lifestyle and Spectroscopic Measurements

With the innovation of high-throughput metabolic profiling methods such as nuclear magnetic resonance (NMR), data mining techniques that can reveal valuable information from substantial data sets are constantly desired in this field. In particular, for the analytical assessment of various human lifestyles, advanced computational methods are ultimately needed. In this study, we applied market basket analysis, which is generally applied in social sciences such as marketing, and used transaction data derived from dietary intake information and urinary chemical data generated using NMR and inductively coupled plasma optical emission spectrometry measurements. The analysis revealed several relationships, such as fish diets with high trimethylamine N-oxide excretion and N-methylnicotinamide excreted at higher levels in the morning and produced from a protein that was consumed one day prior. Therefore, market basket analysis can be applied to metabolic profiling to effectively understand the relationships between metabolites and lifestyle.

Applications of NMR spectroscopy to systems biochemistry

The past decades of advancements in NMR have made it a very powerful tool for metabolic research. Despite its limitations in sensitivity relative to mass spectrometric techniques, NMR has a number of unparalleled advantages for metabolic studies, most notably the rigor and versatility in structure elucidation, isotope-filtered selection of molecules, and analysis of positional isotopomer distributions in complex mixtures afforded by multinuclear and multidimensional experiments. In addition, NMR has the capacity for spatially selective in vivo imaging and dynamical analysis of metabolism in tissues of living organisms. In conjunction with the use of stable isotope tracers, NMR is a method of choice for exploring the dynamics and compartmentation of metabolic pathways and networks, for which our current understanding is grossly insufficient. In this review, we describe how various direct and isotope-edited 1D and 2D NMR methods can be employed to profile metabolites and their isotopomer distributions by stable isotope-resolved metabolomic (SIRM) analysis. We also highlight the importance of sample preparation methods including rapid cryoquenching, efficient extraction, and chemoselective derivatization to facilitate robust and reproducible NMR-based metabolomic analysis. We further illustrate how NMR has been applied in vitro, ex vivo, or in vivo in various stable isotope tracer-based metabolic studies, to gain systematic and novel metabolic insights in different biological systems, including human subjects. The pathway and network knowledge generated from NMR- and MS-based tracing of isotopically enriched substrates will be invaluable for directing functional analysis of other 'omics data to achieve understanding of regulation of biochemical systems, as demonstrated in a case study. Future developments in NMR technologies and reagents to enhance both detection sensitivity and resolution should further empower NMR in systems biochemical research.

SENSI: signal enhancement by spectral integration for the analysis of metabolic mixtures

The method provided here can overcome the low S/N problem in ¹³C NMR by the integration of plural spectra to increase the resolution based on non-bucketing analysis without measurements.

Fast hybrid multi-dimensional NMR methods based on ultrafast 2D NMR

Conventional multi-dimensional (nD) NMR experiments are characterized by inherent long acquisition durations, while ultrafast (UF) NMR makes it possible to reduce to a few hundreds of milliseconds the overall acquisition duration of a complete nD NMR dataset. Although extremely promising for a number of specific applications, the UF strategy suffers from significant limitations compared with its conventional counterpart. The main limitations concern the sensitivity, the resolution, and the accessible spectral width. However, when the targeted applications are compatible with an acquisition duration between a few seconds and a few minutes, hybrid UF techniques can be used to improve the performance of UF nD NMR while remaining faster than conventional acquisitions. Much better results in terms of signal-to-noise ratio can be achieved with the multi-scan single-shot approach or with interleaved acquisitions. Even more, for the same experimental duration, and in the case of homonuclear 2D NMR, the multi-scan single-shot approach has a much higher precision than conventional 2D NMR. Interleaved 2D NMR overcomes the drawbacks of single-scan UF NMR in terms of spectral width and provides spectra for which the quality is not significantly different from that obtained with conventional 2D NMR. Finally, high spectral qualities have been demonstrated from hybrid conventional/UF 3D approaches capable of recording a whole 3D spectrum in the time needed to record a conventional 2D spectrum. This mini-review aims at describing the principles, the recent advances and the latest applications of these hybrid techniques. Copyright © 2015 John Wiley & Sons, Ltd.

Multidimensional NMR spectroscopy in a single scan

Multidimensional NMR has become one of the most widespread spectroscopic tools available to study diverse structural and functional aspects of organic and biomolecules. A main feature of multidimensional NMR is the relatively long acquisition times that these experiments demand. For decades, scientists have been working on a variety of alternatives that would enable NMR to overcome this limitation, and deliver its data in shorter acquisition times. Counting among these methodologies is the so-called ultrafast (UF) NMR approach, which in principle allows one to collect arbitrary multidimensional correlations in a single sub-second transient. By contrast to conventional acquisitions, a main feature of UF NMR is a spatiotemporal manipulation of the spins that imprints the chemical shift and/or J-coupling evolutions being sought, into a spatial pattern. Subsequent gradient-based manipulations enable the reading out of this information and its multidimensional correlation into patterns that are identical to those afforded by conventional techniques. The current review focuses on the fundamental principles of this spatiotemporal UF NMR manipulation, and on a few of the methodological extensions that this form of spectroscopy has undergone during the years.

Resolution-enhanced 2D NMR of complex mixtures by non-uniform sampling

NMR is a powerful tool for the analysis of complex mixtures and the identification of individual components. Two-dimensional (2D) NMR potentially offers a wealth of information, but resolution is often sacrificed in order to contain experimental times. We explore the use of non-uniform sampling (NUS) to increase substantially the resolution of 2D NMR spectra of complex mixtures of small molecules, with no increase in experimental time. Two common pulse sequences for metabolomics applications are analysed, HSQC and TOCSY. Specific attention is paid to sensitivity in resolution-enhanced NUS spectra, using the signal-to-maximum-noise ratio as a metric. With a careful choice of sampling schedule and reconstruction algorithm, resolution in the (13) C dimension for HSQC is increased by a factor of at least 32, with no loss in sensitivity and no spurious peaks. For TOCSY, multiplets can be resolved in the indirect dimension in a reasonable experimental time. These properties should increase the usefulness of 2D NMR for metabolomics applications by, for example, increasing the chances of metabolite identification.

Two elephants in the room: new hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics

PURPOSE OF REVIEW

This review describes some of the advances made over the past year in NMR-based metabolomics for the elucidation of known and unknown compounds, including new ways of how to combine this information with high-resolution mass spectrometry.

RECENT FINDINGS

A new method allows the back-calculation of mass spectra from NMR spectra that have been queried against databases improving the accuracy of the identified compounds by validation and consistency analysis. For the de-novo characterization of unknown compounds, an algorithm has been introduced that predicts all viable NMR spectra from accurate masses allowing, by comparison with experimental NMR data, the determination of the structures of new metabolites in complex mixtures.

SUMMARY

Recent advances in NMR and mass spectrometry-based metabolomics and their synergistic use promises to significantly improve metabolomics sample characterization both in terms of identification and quantitation, and accelerate metabolite discovery.

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.

Ultrafast double-quantum NMR spectroscopy

The acquisition of double-quantum NMR spectra in less than three seconds is demonstrated and the synergies between double-quantum and ultrafast NMR spectroscopy for the analysis of complex mixtures are illustrated.

In situ ultrafast 2D NMR spectroelectrochemistry for real-time monitoring of redox reactions

The in situ implementation of an electrochemical cell (EC) inside a nuclear magnetic resonance (NMR) spectrometer is extremely powerful to study redox reactions in real time and identify unstable reaction intermediates. Unfortunately, the implementation of an electrochemical device near the sensitive volume of an NMR probe significantly affects the quality of the NMR signal, inducing significant line broadening resulting in peak overlap and partial loss of the multiplet structures. Two-dimensional (2D) NMR spectroscopy allows one to bypass signal overlapping by spreading the peaks along two orthogonal dimensions, while providing precious information in terms of structural elucidation. Nevertheless, the acquisition of 2D NMR data suffers from long acquisition durations which are incompatible with fast redox processes taking place in solution. Here, we present a new approach to deal with this issue, consisting of coupling EC-NMR with ultrafast 2D spectroscopy, capable of recording 2D spectra much faster than conventional 2D NMR. This approach is applied to the real-time monitoring of a model reaction. Fast correlation spectroscopy (COSY) spectra are recorded every 3 min in the course of the 80 min reaction, leading to the unambiguous identification of one reaction intermediate and two reaction products. The evolution of 2D NMR peak volumes in the course of time provides further insight into the mechanism of this reaction involving an unstable intermediate. This study demonstrates the feasibility and the relevance of coupling in situ spectroelectrochemistry with ultrafast 2D spectroscopy to monitor real-time electrochemical reactions in the NMR tube.

Accelerated NMR spectroscopy with low-rank reconstruction

Accelerated multi-dimensional NMR spectroscopy is a prerequisite for high-throughput applications, studying short-lived molecular systems and monitoring chemical reactions in real time. Non-uniform sampling is a common approach to reduce the measurement time. Here, a new method for high-quality spectra reconstruction from non-uniformly sampled data is introduced, which is based on recent developments in the field of signal processing theory and uses the so far unexploited general property of the NMR signal, its low rank. Using experimental and simulated data, we demonstrate that the low-rank reconstruction is a viable alternative to the current state-of-the-art technique compressed sensing. In particular, the low-rank approach is good in preserving of low-intensity broad peaks, and thus increases the effective sensitivity in the reconstructed spectra.