Progress toward automated metabolic profiling of human serum: Comparison of CPMG and gradient-filtered NMR analytical methods

Rapid Targeted Screening and Identification of Active Ingredients in Herbal Extracts through Ligand-Detected NMR and Database Matching

Herbal extracts are rich sources of active compounds that can be used for drug screening due to their diverse and unique chemical structures. However, traditional methods for screening these compounds are notably laborious and time-consuming. In this manuscript, we introduce a new high-throughput approach that combines nuclear magnetic resonance (NMR) spectroscopy with a tailored database and algorithm to rapidly identify bioactive components in herbal extracts. This method distinguishes characteristic signals and structural motifs of active constituents in the raw extracts through a relaxation-weighted technique, particularly utilizing the perfect echo Carr-Purcell-Meiboom-Gill (peCPMG) sequence, complemented by precise 2D spectroscopic strategies. The cornerstone of our approach is a customized database designed to filter potential compounds based on defined parameters, such as the presence of CHn segments and unique chemical shifts, thereby expediting the identification of promising compounds. This innovative technique was applied to identifying substances interacting with choline kinase α (ChoKα1), resulting in the discovery of four new inhibitors. Our findings demonstrate a powerful tool for unraveling the complex chemical landscape of herbal extracts, considerably facilitating the search for new pharmaceutical candidates. This approach offers an efficient alternative to traditional methods in the quest for drug discovery from natural sources.

A multimodal pipeline using NMR spectroscopy and MALDI-TOF mass spectrometry imaging from the same tissue sample

NMR spectroscopy and matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) are both commonly used to detect large numbers of metabolites and lipids in metabolomic and lipidomic studies. We have demonstrated a new workflow, highlighting the benefits of both techniques to obtain metabolomic and lipidomic data, which has realized for the first time the combination of these two complementary and powerful technologies. NMR spectroscopy is frequently used to obtain quantitative metabolite information from cells and tissues. Lipid detection is also possible with NMR spectroscopy, with changes being visible across entire classes of molecules. Meanwhile, MALDI MSI provides relative measures of metabolite and lipid concentrations, mapping spatial information of many specific metabolite and lipid molecules across cells or tissues. We have used these two complementary techniques in combination to obtain metabolomic and lipidomic measurements from triple-negative human breast cancer cells and tumor xenograft models. We have emphasized critical experimental procedures that ensured the success of achieving NMR spectroscopy and MALDI MSI in a combined workflow from the same sample. Our data show that several phospholipid metabolite species were differentially distributed in viable and necrotic regions of breast tumor xenografts. This study emphasizes the power of combined NMR spectroscopy-MALDI imaging to advance metabolomic and lipidomic studies.

Quantitative NMR Methods in Metabolomics

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

Evaluation of Fumaric Acid and Maleic Acid as Internal Standards for NMR Analysis of Protein Precipitated Plasma, Serum, and Whole Blood

Significant advances have been made in unknown metabolite identification and expansion of the number of quantifiable metabolites in human plasma, serum, and whole blood using NMR spectroscopy. However, reliable quantitation of metabolites is still a challenge. A major bottleneck is the lack of a suitable internal standard that does not interact with the complex blood sample matrix and also does not overlap with metabolite peaks apart from exhibiting other favorable characteristics. With the goal of addressing this challenge, a comprehensive investigation of fumaric and maleic acids as potential internal standards was made along with a comparison with the conventional standards, TSP (trimethylsilylpropionic acid) and DSS (trimethylsilylpropanesulfonic acid). Both fumaric acid and maleic acid exhibited a surprisingly high performance with a quantitation error <1%, while the TSP and DSS caused an average error of up to 35% in plasma, serum, and whole blood. Further, the results indicate that while fumaric acid is a robust standard for all three biospecimens, maleic acid is suitable for only plasma and serum. Maleic acid is not suited for the analysis of whole blood due to its overlap with coenzyme peaks. These findings provide new opportunities for improved and accurate quantitation of metabolites in human plasma, serum, and whole blood using NMR spectroscopy. Moreover, the use of protein precipitation prior to NMR analysis mirrors the sample preparation commonly used for mass spectrometry based metabolomics, such that these findings further strengthen efforts to combine and compare NMR and MS based metabolite data of human plasma, serum, and whole blood for metabolomics based research.

Rapid two-dimensional ALSOFAST-HSQC experiment for metabolomics and fluxomics studies: application to a 13C-enriched cancer cell model treated with gold nanoparticles

Isotope labeling enables the use of ¹³C-based metabolomics techniques with strongly improved resolution for a better identification of relevant metabolites and tracing of metabolic fluxes in cell and animal models, as required in fluxomics studies. However, even at high NMR-active isotope abundance, the acquisition of one-dimensional ¹³C and classical two-dimensional ¹H,¹³C-HSQC experiments remains time consuming. With the aim to provide a shorter, more efficient alternative, herein we explored the ALSOFAST-HSQC experiment with its rapid acquisition scheme for the analysis of ¹³C-labeled metabolites in complex biological mixtures. As an initial step, the parameters of the pulse sequence were optimized to take into account the specific characteristics of the complex samples. We then applied the fast two-dimensional experiment to study the effect of different kinds of antioxidant gold nanoparticles on a HeLa cancer cell model grown on ¹³C glucose-enriched medium. As a result, ¹H,¹³C-2D correlations could be obtained in a couple of seconds to few minutes, allowing a simple and reliable identification of various ¹³C-enriched metabolites and the determination of specific variations between the different sample groups. Thus, it was possible to monitor glucose metabolism in the cell model and study the antioxidant effect of the coated gold nanoparticles in detail. Finally, with an experiment time of only half an hour, highly resolved ¹H,¹³C-HSQC spectra using the ALSOFAST-HSQC pulse sequence were acquired, revealing the isotope-position-patterns of the corresponding ¹³C-nuclei from carbon multiplets. Graphical abstract Fast NMR applied to metabolomics and fluxomics studies with gold nanoparticles.

Comparative metabonomics of Wenxin Keli and Verapamil reveals differential roles of gluconeogenesis and fatty acid β-oxidation in myocardial injury protection

Metabonomics/metabolomics is a rapid technology for comprehensive profiling of small molecule metabolites in cells, tissues, or whole organisms, the application of which has led to understanding pathophysiologic mechanisms of cardiometabolic diseases, defining predictive biomarkers for those diseases, and also assessing the efficacious effects of incident drugs. In this study, proton nuclear magnetic resonance (NMR)-based metabonomics was employed to identify the metabolic changes in rat plasma caused by myocardial ischemia-reperfusion injury (MIRI), and to compare the metabolic regulatory differences between traditional Chinese medicine Wenxin Keli (WXKL) and Western medicine verapamil. The results revealed that energy-substrate metabolism were significantly disturbed by ischemia-reperfusion (I/R) in myocardium and bulk of the key metabolites could be further modulated by verapamil and/or WXKL. Lipid metabolism and amino acid transamination occurred mainly following the treatment of verapamil, whereas glucose oxidation and BCAA degradation were prominently ameliorated by WXKL to content the energy demands of heart. Moreover, both WXKL and verapamil improved the secretions of taurine and ketone bodies to overcome the oxidative stress and the shortage of energy sources induced by ischemia-reperfusion.

1H nuclear magnetic resonance-based extracellular metabolomic analysis of multidrug resistant Tca8113 oral squamous carcinoma cells

A major obstacle of successful chemotherapy is the development of multidrug resistance (MDR) in the cancer cells, which is difficult to reverse. Metabolomic analysis, an emerging approach that has been increasingly applied in various fields, is able to reflect the unique chemical fingerprints of specific cellular processes in an organism. The assessment of such metabolite changes can be used to identify novel therapeutic biomarkers. In the present study, ¹H nuclear magnetic resonance (NMR) spectroscopy was used to analyze the extracellular metabolomic spectrum of the Tca8113 oral squamous carcinoma cell line, in which MDR was induced using the carboplatin (CBP) and pingyangmycin (PYM) chemotherapy drugs in vitro. The data were analyzed using the principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods. The results demonstrated that the extracellular metabolomic spectrum of metabolites such as glutamate, glycerophosphoethanol amine, α-Glucose and β-Glucose for the drug-induced Tca8113 cells was significantly different from the parental Tca8113 cell line. A number of biochemicals were also significantly different between the groups based on their NMR spectra, with drug-resistant cells presenting relatively higher levels of acetate and lower levels of lactate. In addition, a significantly higher peak was observed at δ 3.35 ppm in the spectrum of the PYM-induced Tca8113 cells. Therefore, ¹H NMR-based metabolomic analysis has a high potential for monitoring the formation of MDR during clinical tumor chemotherapy in the future.

Analysis of plasma metabolic biomarkers in the development of 4-nitroquinoline-1-oxide-induced oral carcinogenesis in rats

The aim of the present study was to identify time-dependent changes in the expression of metabolic biomarkers during the various stages of oral carcinogenesis to provide an insight into the sequential mechanism of oral cancer development. An ¹H nuclear magnetic resonance (NMR)-based metabolomics approach was used to analyze the blood plasma samples of Sprague-Dawley rats exhibiting various oral lesions induced by the administration of 4-nitroquinoline-1-oxide (4NQO) in drinking water. The ¹H NMR spectra were processed by principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) to determine the metabolic differences between the three developmental stages of oral mucosa cancer (health, oral leukoplakia [OLK] and oral squamous cell carcinoma [OSCC]). The variable importance in projection (VIP) score derived from the PLS-DA model was used to screen for important metabolites, whose significance was further verified through analysis of variance (ANOVA). Data from the present study indicated that 4NQO-induced rat oral carcinogenesis produced oral pre-neoplastic and neoplastic lesions and provided an effective model for analyzing sequential changes in the ¹H NMR spectra of rat blood plasma. The ¹H NMR-based metabolomics approach clearly differentiates between healthy, OLK and OSSC rats in the PCA and PLS-DA models. Furthermore, lactic acid, choline, glucose, proline, valine, isoleucine, aspartic acid and 2-hydroxybutyric acid demonstrated VIP>1 in the PLS-D model and P<0.05 with ANOVA. It was also identified that increases in lactic acid, choline and glucose, and decreases in proline, valine, isoleucine, aspartic acid and 2-hydroxybutyric acid may be relative to the characteristic mechanisms of oral carcinogenesis. Therefore, these plasma metabolites may serve as metabolic biomarkers in oral carcinogenesis and assist in the early diagnosis and preventive treatment of oral cancer.

Simultaneous solvent and J-modulation suppression in PGSTE-based diffusion experiments

The most favourable solvent suppression methods that have been applied to PGSTE experiments for the measurement of diffusion are WATERGATE and excitation sculpting. However, both methods come with significant J-modulation line-shape distortions on multiplets, a phenomenon that is known to be of particular concern for DOSY data processing. Here, two new PGSTE experiments are proposed that suppress both the solvent peak and J-modulation based on the perfect echo WATERGATE sequence. This allows narrow suppression bandwidths and thus measurement of diffusion on peaks close to the solvent peak. Both sequences perform admirably and the better option depends on the priority one puts on the quality of the solvent suppression or signal loss due to T2 weighting. Gradient-based solvent suppression in PGSTE experiments can often be compromised by the variable, diffusion-encoding gradient pulses. Special emphasis is put on how to maximise the robustness of the solvent suppression.

Quantitative NMR for bioanalysis and metabolomics

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

Comprehensive multiphase NMR spectroscopy: basic experimental approaches to differentiate phases in heterogeneous samples

Heterogeneous samples, such as soils, sediments, plants, tissues, foods and organisms, often contain liquid-, gel- and solid-like phases and it is the synergism between these phases that determine their environmental and biological properties. Studying each phase separately can perturb the sample, removing important structural information such as chemical interactions at the gel-solid interface, kinetics across boundaries and conformation in the natural state. In order to overcome these limitations a Comprehensive Multiphase-Nuclear Magnetic Resonance (CMP-NMR) probe has been developed, and is introduced here, that permits all bonds in all phases to be studied and differentiated in whole unaltered natural samples. The CMP-NMR probe is built with high power circuitry, Magic Angle Spinning (MAS), is fitted with a lock channel, pulse field gradients, and is fully susceptibility matched. Consequently, this novel NMR probe has to cover all HR-MAS aspects without compromising power handling to permit the full range of solution-, gel- and solid-state experiments available today. Using this technology, both structures and interactions can be studied independently in each phase as well as transfer/interactions between phases within a heterogeneous sample. This paper outlines some basic experimental approaches using a model heterogeneous multiphase sample containing liquid-, gel- and solid-like components in water, yielding separate (1)H and (13)C spectra for the different phases. In addition, (19)F performance is also addressed. To illustrate the capability of (19)F NMR soil samples, containing two different contaminants, are used, demonstrating a preliminary, but real-world application of this technology. This novel NMR approach possesses a great potential for the in situ study of natural samples in their native state.

NMR methods for unravelling the spectra of complex mixtures

The main methods for the simplification of the NMR of complex mixtures by selective attenuation/suppression of the signals of certain components are presented. The application of relaxation, diffusion and PSR filters and other techniques to biological samples, pharmaceuticals, foods, living organisms and natural products are illustrated with examples.

Evaluation of standard and advanced preprocessing methods for the univariate analysis of blood serum 1H-NMR spectra

Proton nuclear magnetic resonance ((1)H-NMR)-based metabolomics enables the high-resolution and high-throughput assessment of a broad spectrum of metabolites in biofluids. Despite the straightforward character of the experimental methodology, the analysis of spectral profiles is rather complex, particularly due to the requirement of numerous data preprocessing steps. Here, we evaluate how several of the most common preprocessing procedures affect the subsequent univariate analyses of blood serum spectra, with a particular focus on how the standard methods perform compared to more advanced examples. Carr-Purcell-Meiboom-Gill 1D (1)H spectra were obtained for 240 serum samples from healthy subjects of the Asklepios study. We studied the impact of different preprocessing steps--integral (standard method) and probabilistic quotient normalization; no, equidistant (standard), and adaptive-intelligent binning; mean (standard) and maximum bin intensity data summation--on the resonance intensities of three different types of metabolites: triglycerides, glucose, and creatinine. The effects were evaluated by correlating the differently preprocessed NMR data with the independently measured metabolite concentrations. The analyses revealed that the standard methods performed inferiorly and that a combination of probabilistic quotient normalization after adaptive-intelligent binning and maximum intensity variable definition yielded the best overall results (triglycerides, R = 0.98; glucose, R = 0.76; creatinine, R = 0.70). Therefore, at least in the case of serum metabolomics, these or equivalent methods should be preferred above the standard preprocessing methods, particularly for univariate analyses. Additional optimization of the normalization procedure might further improve the analyses.

Quantitative two-dimensional HSQC experiment for high magnetic field NMR spectrometers

The finite RF power available on carbon channel in proton-carbon correlation experiments leads to non-uniform cross peak intensity response across carbon chemical shift range. Several classes of broadband pulses are available that alleviate this problem. Adiabatic pulses provide an excellent magnetization inversion over a large bandwidth, and very recently, novel phase-modulated pulses have been proposed that perform 90 degrees and 180 degrees magnetization rotations with good offset tolerance. Here, we present a study how these broadband pulses (adiabatic and phase-modulated) can improve quantitative application of the heteronuclear single quantum coherence (HSQC) experiment on high magnetic field strength NMR spectrometers. Theoretical and experimental examinations of the quantitative, offset-compensated, CPMG-adjusted HSQC (Q-OCCAHSQC) experiment are presented. The proposed experiment offers a formidable improvement to the offset performance; (13)C offset-dependent standard deviation of the peak intensity was below 6% in range of+/-20 kHz. This covers the carbon chemical shift range of 150 ppm, which contains the protonated carbons excluding the aldehydes, for 22.3 T NMR magnets. A demonstration of the quantitative analysis of a fasting blood plasma sample obtained from a healthy volunteer is given.

1H NMR-based metabonomic and pattern recognition analysis for detection of oral squamous cell carcinoma

BACKGROUND

Metabonomic analysis has been increasingly used to monitor metabolic abnormalities in cells and their microenvironment in order to detect the cancer markers recently. We evaluated the feasibility of applying (1)H nuclear magnetic resonance ((1)H NMR) based metabonomic method in the early detection of the differences in the plasma from 3 groups, which were patients with oral squamous cell carcinoma (OSCC), patients with oral leukoplakia (OLK), and the healthy control group.

METHODS

(1)H NMR spectra were obtained from human plasma samples prior to spectral analysis. The obtained data were processed by both the unsupervised principal component analysis (PCA) and the supervised partial least squares discriminant analysis (PLS-DA) to find out the differences among the three groups.

RESULTS

PLS-DA analysis has revealed a good model to detect the NMR data that can differentiate the OSCC patients from the OLK patients and the controls using a test set.

CONCLUSION

The results indicated that the (1)H NMR-based metabonomic approach is a feasible and efficient method for differentiating the OSCC patient's plasma from the healthy controls. As a potential novel strategy and a more convenient technique, it deserves a further evaluation for an early detection of oral cancer.

NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm

As with every -omics technology, metabolomics requires new methodologies for data processing. Due to the large spectral size, a standard approach in NMR-based metabolomics implies the division of spectra into equally sized bins, thereby simplifying subsequent data analysis. Yet, disadvantages are the loss of information and the occurrence of artifacts caused by peak shifts. Here, a new binning algorithm, Adaptive Intelligent Binning (AI-Binning), which largely circumvents these problems, is presented. AI-Binning recursively identifies bin edges in existing bins, requires only minimal user input, and avoids the use of arbitrary parameters or reference spectra. The performance of AI-Binning is demonstrated using serum spectra from 40 hypertensive and 40 matched normotensive subjects from the Asklepios study. Hypertension is a major cardiovascular risk factor characterized by a complex biochemistry and, in most cases, an unknown origin. The binning algorithm resulted in an improved classification of hypertensive status compared with that of standard binning and facilitated the identification of relevant metabolites. Moreover, since the occurrence of noise variables is largely avoided, AI-Binned spectra can be unit-variance scaled. This enables the detection of relevant, low-intensity metabolites. These results demonstrate the power of AI-Binning and suggest the involvement of alpha-1 acid glycoproteins and choline biochemistry in hypertension.

Targeted profiling: quantitative analysis of 1H NMR metabolomics data

Extracting meaningful information from complex spectroscopic data of metabolite mixtures is an area of active research in the emerging field of "metabolomics", which combines metabolism, spectroscopy, and multivariate statistical analysis (pattern recognition) methods. Chemometric analysis and comparison of 1H NMR1 spectra is commonly hampered by intersample peak position and line width variation due to matrix effects (pH, ionic strength, etc.). Here a novel method for mixture analysis is presented, defined as "targeted profiling". Individual NMR resonances of interest are mathematically modeled from pure compound spectra. This database is then interrogated to identify and quantify metabolites in complex spectra of mixtures, such as biofluids. The technique is validated against a traditional "spectral binning" analysis on the basis of sensitivity to water suppression (presaturation, NOESY-presaturation, WET, and CPMG), relaxation effects, and NMR spectral acquisition times (3, 4, 5, and 6 s/scan) using PCA pattern recognition analysis. In addition, a quantitative validation is performed against various metabolites at physiological concentrations (9 microM-8 mM). "Targeted profiling" is highly stable in PCA-based pattern recognition, insensitive to water suppression, relaxation times (within the ranges examined), and scaling factors; hence, direct comparison of data acquired under varying conditions is made possible. In particular, analysis of metabolites at low concentration and overlapping regions are well suited to this analysis. We discuss how targeted profiling can be applied for mixture analysis and examine the effect of various acquisition parameters on the accuracy of quantification.

Application of NMR-based metabonomics in the investigation of myocardial ischemia-reperfusion, ischemic preconditioning and antioxidant intervention in rabbits

NMR based metabonomics was applied in rabbit plasma samples during myocardial ischemia-reperfusion injury, with the following interventions: (1) Control (no intervention); (2) ischemic preconditioning (IpC); (3) administration of melatonin; (4) IpC+administration of melatonin; (5) treatment of the indole derivative C6458. The (1)H NMR signal intensity ratio of lactate/glucose was found to increase in Control samples during reperfusion compared to baseline, while lactate+alanine/acetate was decreased suggesting impairment of aerobic glycolysis and concomitant lipid utilization. In contrast, after IpC or treatment with C6458, the lactate/glucose ratio was similar to baseline in accordance with the previously reported decrease in infarct size. Multivariate statistical methods such as Principal Component Analysis (PCA), and Discriminant Analysis (DA) were used for the discrimination of samples. The use of ANOVA variable preselection prior to PCA was advantageous in producing adequate models. PCA could classify the Control group in three clusters according to the condition of the heart (baseline-ischemia-reperfusion) while in the IpC groups no classification was evident. PCA discrimination upon treatment with melatonin and C6458 provided further evidence of their effect on the metabolic profile. The supervised DA resulted in fine discrimination between the different subgroups. Plasma NMR spectra in combination with pattern recognition techniques proved to be an efficient and simple method to depict the metabolic changes produced upon ischemia-reperfusion of the myocardium.

Metabolomic analysis using optimized NMR and statistical methods

NMR-based metabolomics requires robust automated methodologies, and the accuracy of NMR-based metabolomics data is greatly influenced by the reproducibility of data acquisition and processing methods. Effective water resonance signal suppression and reproducible spectral phasing and baseline traces across series of related samples are crucial for statistical analysis. We assess robustness, repeatability, sensitivity, selectivity, and practicality of commonly used solvent peak suppression methods in the NMR analysis of biofluids with respect to the automated processing of the NMR spectra and the impact of pulse sequence and data processing methods on the sensitivity of pattern recognition and statistical analysis of the metabolite profiles. We introduce two modifications to the excitation sculpting pulse sequence whereby the excitation solvent suppression pulse cascade is preceded by low-power water resonance presaturation pulses during the relaxation delay. Our analysis indicates that combining water presaturation with excitation sculpting water suppression delivers the most reproducible and information-rich NMR spectra of biofluids.