Evaluation of Non-Uniform Sampling 2D 1H-13C HSQC Spectra for Semi-Quantitative Metabolomics

New analytical methods focusing on polar metabolite analysis in mass spectrometry and NMR-based metabolomics

Following in the footsteps of genomics and proteomics, metabolomics has revolutionised the way we investigate and understand biological systems. Rapid development in the last 25 years has been driven largely by technical innovations in mass spectrometry and nuclear magnetic resonance spectroscopy. However, despite the modest size of metabolomes relative to proteomes and genomes, methodological capabilities for robust, comprehensive metabolite analysis remain a major challenge. Therefore, development of new methods and techniques remains vital for progress in the field. Here, we review developments in LC-MS, GC-MS and NMR methods in the last few years that have enhanced quantitative and comprehensive metabolome coverage, highlighting the techniques involved, their technical capabilities, relative performance, and potential impact.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

NMR Precision Metabolomics: Dynamic Peak Sum Thresholding and Navigators for Highly Standardized and Reproducible Metabolite Profiling of Clinical Urine Samples

Metabolomics, especially urine-based studies, offers incredible promise for the discovery and development of clinically impactful biomarkers. However, due to the unique challenges of urine, a highly precise and reproducible workflow for NMR-based urine metabolomics is lacking. Using 1D and 2D non-uniform sampled (NUS) 1H-13C NMR spectroscopy, we systematically explored how changes in hydration or specific gravity (SG) and pH can impact biomarker discovery. Further, we examined additional sources of error in metabolomics studies and identified Navigator molecules that could monitor for those biases. Adjustment of SG to 1.002-1.02 coupled with a dynamic sum-based peak thresholding eliminates false positives associated with urine hydration and reduces variation in chemical shift. We identified Navigator molecules that can effectively monitor for inconsistencies in sample processing, SG, protein contamination, and pH. The workflow described provides quality assurance and quality control tools to generate high-quality urine metabolomics data, which is the first step in biomarker discovery.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

Application of Band-Selective HSQC NMR in Species Discrimination and Adulteration Identification of Panax Linn

The quality of Panax Linn products available in the market is threatened by adulteration with different Panax species, such as Panax quinquefolium (PQ), Panax ginseng (PG), and Panax notoginseng (PN). In this paper, we established a 2D band-selective heteronuclear single quantum coherence (bs-HSQC) NMR method to discriminate species and detect adulteration of Panax Linn. The method involves selective excitation of the anomeric carbon resonance region of saponins and non-uniform sampling (NUS) to obtain high-resolution spectra in less than 10 min. The combined strategy overcomes the signal overlap limitation in ¹H NMR and the long acquisition time in traditional HSQC. The present results showed that twelve well-separated resonance peaks can be assigned in the bs-HSQC spectra, which are of high resolution, good repeatability, and precision. Notably, the identification accuracy of species was found to be 100% for all tests conducted in the present study. Furthermore, in combination with multivariate statistical methods, the proposed method can effectively determine the composition proportion of adulterants (from 10% to 90%). Based on the PLS-DA models, the identification accuracy was greater than 80% when composition proportion of adulterants was 10%. Thus, the proposed method may provide a fast, practical, and effective analysis technique for food quality control or authenticity identification.

An Optimized Two-Dimensional Quantitative Nuclear Magnetic Resonance Strategy for the Rapid Quantitation of Diester-Type C19-Diterpenoid Alkaloids from Aconitum carmichaelii

With the development of nuclear magnetic resonance (NMR) spectrometers and probes, two-dimensional quantitative nuclear magnetic resonance (2D qNMR) technology with a high signal resolution and great application potential has become increasingly accessible for the quantitation of complex mixtures. However, the requirement that the relaxation recovery time be equal to at least five times T₁ (longitudinal relaxation time) makes it difficult for 2D qNMR to simultaneously achieve high quantitative accuracy and high data acquisition efficiency. By comprehensively using relaxation optimization and nonuniform sampling, we successfully established an optimized 2D qNMR strategy for HSQC experiments at the half-hour level and then accurately quantified the diester-type C₁₉-diterpenoid alkaloids in Aconitum carmichaelii. The optimized strategy had the advantages of high efficiency, high accuracy, good reproducibility, and low cost and thus could serve as a reference to optimize 2D qNMR experiments for quantitative analysis of natural products, metabolites, and other complex mixtures.

NMR Reveals Functionally Relevant Thermally Induced Structural Changes within the Native Ensemble of G-CSF

Structure–function relationships in proteins refer to a trade-off between stability and bioactivity, molded by evolution of the molecule. Identifying which protein amino acid residues jeopardize global or local stability for the benefit of bioactivity would reveal residues pivotal to this structure–function trade-off. Here, we use ¹⁵N–¹H heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy to probe the microenvironment and dynamics of residues in granulocyte colony-stimulating factor (G-CSF) through thermal perturbation. From this analysis, we identified four residues (G4, A6, T133, and Q134) that we classed as significant to global stability, given that they all experienced large environmental and dynamic changes and were closely correlated to each other in their NMR characteristics. Additionally, we observe that roughly four structural clusters are subject to localized conformational changes or partial unfolding prior to global unfolding at higher temperature. Combining NMR observables with structure relaxation methods reveals that these structural clusters concentrate around loop AB (binding site III inclusive). This loop has been previously implicated in conformational changes that result in an aggregation prone state of G-CSF. Residues H43, V48, and S63 appear to be pivotal to an opening motion of loop AB, a change that is possibly also important for function. Hence, we present here an approach to profiling residues in order to highlight their potential roles in the two vital characteristics of proteins: stability and bioactivity.

NMR and Metabolomics-A Roadmap for the Future

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

Application of accelerated heteronuclear single quantum coherence experiments to the rapid quantification of monosaccharides and disaccharides in dairy products

Monosaccharides and disaccharides are important dietary components, but if insufficiently metabolized by some population subgroups, they are also linked to disease patterns. Thus, the correct analytical identification, quantification, and labeling of these food components are crucial to inform and potentially protect consumers. Enzymatic assays and high-performance anion-exchange chromatography with pulsed amperometric detection are established methods for the quantification of monosaccharides and disaccharides that, however, require long measuring times (60-180 min). Accelerated methods for the identification and quantification of the nutritionally relevant monosaccharides and disaccharides d-glucose, d-galactose, d-fructose, sucrose, lactose, and maltose were therefore developed. To realize this goal, the NMR experiments HSQC (heteronuclear single quantum coherence) and acceleration by sharing adjacent polarization (ASAP)-HSQC were applied. Measurement times were reduced to 27 and 6 min, respectively, by optimizing the interscan delay and applying non-uniform sampling. The optimized methods were used to quantify d-glucose, d-galactose, d-fructose, sucrose, and lactose in various dairy products. Results of the HSQC and ASAP-HSQC methods are equivalent to the results of the reference methods in terms of both precision and accuracy, demonstrating that these methods can be used to correctly analyze nutritionally relevant monosaccharides and disaccharides in short times.

Ultrafast 2D NMR for the analysis of complex mixtures

2D NMR is extensively used in many different fields, and its potential for the study of complex biochemical or chemical mixtures has been widely demonstrated. 2D NMR gives the ability to resolve peaks that overlap in 1D spectra, while providing both structural and quantitative information. However, complex mixtures are often analysed in situations where the data acquisition time is a crucial limitation, due to an ongoing chemical reaction or a moving sample from a hyphenated technique, or to the high-throughput requirement associated with large sample collections. Among the great diversity of available fast 2D methods, ultrafast (or single-scan) 2D NMR is probably the most general and versatile approach for complex mixture analysis. Indeed, ultrafast NMR has undergone an impressive number of methodological developments that have helped turn it into an efficient analytical tool, and numerous applications to the analysis of mixtures have been reported. This review first summarizes the main concepts, features and practical limitations of ultrafast 2D NMR, as well as the methodological developments that improved its analytical potential. Then, a detailed description of the main applications of ultrafast 2D NMR to mixture analysis is given. The two major application fields of ultrafast 2D NMR are first covered, i.e., reaction/process monitoring and metabolomics. Then, the potential of ultrafast 2D NMR for the analysis of hyperpolarized mixtures is described, as well as recent developments in oriented media. This review focuses on high-resolution liquid-state 2D experiments (including benchtop NMR) that include at least one spectroscopic dimension (i.e., 2D spectroscopy and DOSY) but does not cover in depth applications without spectral resolution and/or in inhomogeneous fields.

Isolation, Characterization, and Metabolic Profiling of Ceratorhiza hydrophila from the Aquatic Plant Myriophyllum spicatum

The goal of the present study was to investigate the antibacterial properties, enzyme production, and metabolic profiling of a new Ceratorhiza hydrophila strain isolated from the submerged aquatic plant Myriophyllum spicatum. Furthermore, the fungus' morphological characterization and DNA sequencing have been described. The fungus has been identified and submitted to the GenBank as Ceratorhiza hydrophila isolate EG19 and the fungus ID is MK387081. The enzyme analyses showed its ability to produce protease and cellulase enzymes. According to the CSLI standard, the ethyl acetate extract of C. hydrophila showed intermediate antibacterial activity against Streptococcus pneumonia, Micrococcus luteus, and Staphylococcus aureus. Metabolic profiling has been carried out using 700 MHz NMR spectroscopy. Based on the ¹H and ¹H-¹³C heteronuclear single quantum coherence (HSQC) NMR data and NMR databases, 23 compounds have been identified. The identified metabolites include 31% amino acids, 9% sugars, 9% amines, 4% sugar alcohols, and 4% alkaloids. This is the first report for the metabolic characterization of C. hydrophila, which gave preliminary information about the fungus. It is expected that our findings not only will pave the way to other perspectives in enormous applications using C. hydrophila as a new promising source of antimicrobial agents and essential metabolites, but also it will be valuable in the classification and chemotaxonomy of the species.

Metabolite Biomarkers of Response (BoRs): Towards a fingerprint for the evolution of metastatic breast cancer

Breast cancer is the most common cancer in women worldwide and despite improved treatment strategies, it persists as the second leading cause of death of women globally. Overall prognosis drops drastically once the cancer has metastasized, which is also associated with resistance to therapy. The evolution from a localized breast cancer to metastatic disease is complex and multifactorial. Metabolic reprogramming is a pre-requisite for this transition. In this graphical review, we provide an overview of altered metabolic pathways observed in metastatic breast cancer (mBC) and detail how metabolite biomarkers could serve as a novel class of precision medicine tools to improve the diagnosis, monitoring, and treatment of mBC.

Gadolinium-Based Paramagnetic Relaxation Enhancement Agent Enhances Sensitivity for NUS Multidimensional NMR-Based Metabolomics

Gadolinium is a paramagnetic relaxation enhancement (PRE) agent that accelerates the relaxation of metabolite nuclei. In this study, we noted the ability of gadolinium to improve the sensitivity of two-dimensional, non-uniform sampled NMR spectral data collected from metabolomics samples. In time-equivalent experiments, the addition of gadolinium increased the mean signal intensity measurement and the signal-to-noise ratio for metabolite resonances in both standard and plasma samples. Gadolinium led to highly linear intensity measurements that correlated with metabolite concentrations. In the presence of gadolinium, we were able to detect a broad array of metabolites with a lower limit of detection and quantification in the low micromolar range. We also observed an increase in the repeatability of intensity measurements upon the addition of gadolinium. The results of this study suggest that the addition of a gadolinium-based PRE agent to metabolite samples can improve NMR-based metabolomics.

Virtual decoupling to break the simplification versus resolution trade-off in nuclear magnetic resonance of complex metabolic mixtures

The heteronuclear single quantum correlation (HSQC) experiment developed by Bodenhausen and Ruben (1980) in the early days of modern nuclear magnetic resonance (NMR) is without a doubt one of the most widely used experiments, with applications in almost every aspect of NMR including metabolomics. Acquiring this experiment, however, always implies a trade-off: simplification versus resolution. Here, we present a method that artificially lifts this barrier and demonstrate its application towards metabolite identification in a complex mixture. Based on the measurement of clean in-phase and clean anti-phase (CLIP/CLAP) HSQC spectra (Enthart et al., 2008), we construct a virtually decoupled HSQC (vd-HSQC) spectrum that maintains the highest possible resolution in the proton dimension. Combining this vd-HSQC spectrum with a J -resolved spectrum (Pell and Keeler, 2007) provides useful information for the one-dimensional proton spectrum assignment and for the identification of metabolites in Dreissena polymorpha (Prud'homme et al., 2020).

Rapid nuclear magnetic resonance data acquisition with improved resolution and sensitivity for high-throughput metabolomic analysis

Nuclear magnetic resonance (NMR)-based metabolomics has witnessed rapid advancements in recent years with the continuous development of new methods to enhance the sensitivity, resolution, and speed of data acquisition. Some of the approaches were earlier used for peptide and protein resonance assignments and have now been adapted to metabolomics. At the same time, new NMR methods involving novel data acquisition techniques, suited particularly for high-throughput analysis in metabolomics, have been developed. In this review, we focus on the different sampling strategies or data acquisition methods that have been developed in our laboratory and other groups to acquire NMR spectra rapidly with high sensitivity and resolution for metabolomics. In particular, we focus on the use of multiple receivers, phase modulation NMR spectroscopy, and fast-pulsing methods for identification and assignments of metabolites.

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.