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.     

Application of NMR metabolomics to search for human disease biomarkers

Since antiquity, humans have used body fluids like saliva, urine and sweat for the diagnosis of diseases. The amount, color and smell of body fluids are still used in many traditional medical practices to evaluate an illness and make a diagnosis. The development and application of analytical methods for the detailed analysis of body fluids has led to the discovery of numerous disease biomarkers. Recently, mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The goal of these efforts is to identify metabolites that are uniquely correlated with a specific human disease in order to accurately diagnose and treat the malady. In this review we will discuss recent developments in sample preparation, experimental techniques, the identification and quantification of metabolites, and the chemometric tools used to search for biomarkers of human diseases using NMR.     

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.     

Application of orthogonal signal correction to minimise the effects of physical and biological variation in high resolution 1H NMR spectra of biofluids

1H nuclear magnetic resonance (NMR)-based metabonomics is a well-established technique used to analyse and interpret complex multiparametric metabolic data, and has a wide number of applications in the development of pharmaceuticals. However, interpretation of biological data can be confounded by extraneous variation in the data such as fluctuations in either experimental conditions or in physiological status. Here we have shown the novel application of a data filtering method, orthogonal signal correction (OSC), to biofluid NMR data to minimise the influence of inter- and intra-spectrometer variation during data acquisition, and also to minimise innate physiological variation. The removal of orthogonal variation exposed features of interest in the NMR data and facilitated interpretation of the derived multivariate models. Furthermore, analysis of the orthogonal variation provided an explanation of the systematic analytical/biological changes responsible for confounding the original NMR data.     

Characterization of the measurement error structure in 1D H NMR data for metabolomics studies

NMR-based metabolomics is characterized by high throughput measurements of the signal intensities of complex mixtures of metabolites in biological samples by assaying, typically, bio-fluids or tissue homogenates. The ultimate goal is to obtain relevant biological information regarding the dissimilarity in patho-physiological conditions that the samples experience. For a long time now, this information has been obtained through the analysis of measured NMR signals via multivariate statistics.NMR data are quite complex and the use of such multivariate statistical methods as principal components analysis (PCA) for their analysis assumes that the data are multivariate normal with errors that are identical, independent and normally distributed (i.e. iid normal). There is a consensus that these assumptions are not always true for these data and, thus, several methods have been devised to transform the data or weight them prior to analysis by PCA. The structure of NMR measurement noise, or the extent to which violations of error homoscedasticity affect PCA results have neither been characterized nor investigated.A comprehensive characterization of measurement uncertainties in NMR based metabolomics was achieved in this work using an experiment designed to capture contributions of several sources of error to the total variance in the measurements. The noise structure was found to be heteroscedastic and highly correlated with spectral characteristics that are similar to the mean of the spectra and their standard deviation. A model was subsequently developed that potentially allows errors in NMR measurements to be accurately estimated without the need for extensive replication.     

1H NMR-based metabolomics approach for exploring urinary metabolome modifications after acute and chronic physical exercise

Metabolomics is a comprehensive method for metabolite assessment that involves measuring the overall metabolic signature of biological samples. We used this approach to investigate biochemical changes due to acute and chronic physical exercise. Twenty-two women using identical oral contraceptives were segregated into an untrained (n = 10) or trained (n = 12) group depending on their physical training background. The subjects performed two exercises in a randomized order: a prolonged exercise test (75% of their \mathop V^· \textO_2   max \mathop V\limits^\cdot {{\text{O}}_{2\,\;\max }} until exhaustion) and a short-term, intensive exercise test (short-term, intensive exercise anaerobic test). Urine specimens were collected before and 30 min after each test. The samples were analyzed by ¹H NMR spectroscopy, and multivariate statistical techniques were utilized to process the data. Distinguishing characteristics were observed only in the urine profiles of specimens collected before vs. 30 min after the short-term, intensive exercise test. The metabolites responsible for such changes were creatinine, lactate, pyruvate, alanine, β-hydroxybutyrate, acetate, and hypoxanthine. In both groups, the excretion of lactate, pyruvate, alanine, β-hydroxybutyrate, and hypoxanthine increased similarly after the completion of the short-term, intensive exercise test (p < 0.03). However, acetate excretion increased to a lesser extent in trained than in untrained subjects (p < 0.05). In conclusion, metabolomics is a promising tool in order to gain insight into physiological status and to clarify the changes induced by short-term, intense physical exercise.     

The potentiality of NMR-based metabolomics in food science and food authentication assessment

In the last years, there was an increasing interest on nuclear magnetic resonance (NMR) spectroscopy, whose applications experienced an exponential growth in several research fields, particularly in food science. NMR was initially developed as the elective technique for structure elucidation of single molecules and nowadays is playing a dominant role in complex mixtures investigations. In the era of the “omics” techniques, NMR was rapidly enrolled as one of the most powerful methods to approach metabolomics studies. Its use in analytical routines, characterized by rapid and reproducible measurements, would provide the identification of a wide range of chemical compounds simultaneously, disclosing sophisticated frauds or addressing the geographical origin, as well as revealing potential markers for other authentication purposes. The great economic value of high-quality or guaranteed foods demands highly detailed characterization to protect both consumers and producers from frauds. The present scenario suggests metabolomics as the privileged approach of modern analytical studies for the next decades. The large potentiality of high-resolution NMR techniques is here presented through specific applications and using different approaches focused on the authentication process of some foods, like tomato paste, saffron, honey, roasted coffee, and balsamic and traditional balsamic vinegar of Modena, with a particular focus on geographical origin characterization, ageing determination, and fraud detection.     

Simple quality assessment approach for herbal extracts using high performance liquid chromatography-UV based metabolomics platform

A lack of adequate or accepted research methodology has been a major obstacle to study herbal medicines. In this study, instead of the prevalent hyphenated chromatographies, common high performance liquid chromatography equipped with ultraviolet detector (HPLC-UV) and multivariate statistical analysis were utilized to assess the qualities of total flavones of sea buckthorn (TFS), an 85% ethanol extract of the sea buckthorn berries. Two complementary HPLC-UV methods were developed, validated and combined to comprehensively determine the ingredients in TFS. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) of the combined analytical data showed that the six batches of TFS could be well differentiated. Hierarchical cluster analysis (HCA) using Ward's minimum variance method of the PLS-DA loading matrix demonstrated the known ingredients (quercetin, kaempferol, isorhamnetin, oleanolic acid and ursolic acid) and three unknown ingredients in TFS significantly contributed to the quality differences. A PLS regression model indicated that the results of the present method correlated well with the content of total flavones, which is now the quality control approach of TFS. Results from this study indicated that the proposed method is reliable for the quality reassessment of some widely used herbal extracts.     

Quality assessment of Astragali Radix based on pseudo-targeted metabolomics and chemometric approach

Astragali Radix is widely used because of its dual use in medicine and food, and its quality evaluation is of great importance. In this study, a pseudo-targeted metabolomics approach based on scheduled multiple reaction monitoring was developed, and a total of 114 compounds with good linearity, sensitivity, and reproducibility were selected for relative quantification, and the chemical differences between Astragali Radix of different growth patterns were further compared by chemometric analysis. With the help of multivariate and univariate analysis, 26 differential compounds between wild/semi-wild Astragali Radix and cultivated Astragali Radix were determined. Then five marker compounds were screened out by lasso regression, and further verified by systematic clustering, random forest, support vector machine, and logistic regression. In addition, malonyl-substituted flavonoids showed relatively higher content in wild/semi-wild Astragali Radix. Thus, the malonyl substitution was characteristic for flavonoids in wild/semi-wild Astragali Radix. In conclusion, the application of pseudo-targeted metabolomics and various statistical methods could offer multi-dimensional information for the holistic quality evaluation of Astragali Radix.     

Discovery of safety biomarkers for realgar in rat urine using UFLC-IT-TOF/MS and 1H NMR based metabolomics

As an arsenical, realgar (As₄S₄) is known as a poison and paradoxically as a therapeutic agent. However, a complete understanding of the precise biochemical alterations accompanying the toxicity and therapy effects of realgar is lacking. Using a combined ultrafast liquid chromatography (UFLC) coupled with ion trap time-of-flight mass spectrometry (IT-TOF/MS) and ¹H NMR spectroscopy based metabolomics approach, we were able to delineate significantly altered metabolites in the urine samples of realgar-treated rats. The platform stability of the liquid chromatography LC/MS and NMR techniques was systematically investigated, and the data processing method was carefully optimized. Our results indicate significant perturbations in amino acid metabolism, citric acid cycle, choline metabolism, and porphyrin metabolism. Thirty-six metabolites were proposed as potential safety biomarkers related to disturbances caused by realgar, and glycine and serine are expected to serve as the central contacts in the metabolic pathways related to realgar-induced disturbance. The LC/MS and NMR based metabolomics approach established provided a systematic and holistic view of the biochemical effects of realgar on rats, and might be employed to investigate other drugs or xenobiotics in the future.

Untargeted metabolomics liquid chromatography-high resolution mass spectrometry approach for the geographical origin assessment of Italian dehydrated apples

Geographical provenience is nowadays a relevant aspect of the authenticity and the quality of many food commodities. Dehydrated apple cubes/slices represent an ingredient commonly used by food companies for bakery products. However, this apple-based matrix is not so known and studied from an analytical point of view. In the present work, seven compounds were identified as key molecules to distinguish between Italian and non-Italian samples, through an untargeted ultrahigh-pressure liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) approach. This methodology was merged with multivariate statistical analysis, and the principal features were studied and identified considering several identification steps. Samples from 2020 and 2021 harvesting campaigns, with partial and total dehydration rates, with or without peel, and from different apple varieties were considered for the study, for a total of 91 samples. Afterward, the same analysis protocol was applied to an external set (n = 12 samples), included in the statistical models, searching for the key compounds identified in the training set. Interesting and significant results underlined the potentiality of the UHPLC-HRMS technology as a confirmatory strategy for the geographical origin assessment of dehydrated apple commodities.     

Capillary electrophoresis with electrochemiluminescence detection for measurement of aspartate aminotransferase and alanine aminotransferase activities in biofluids

A new sensitive assay for aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activities in biofluids was developed, based on the separation and detection of alanine, glutamate, and aspartate using capillary electrophoresis (CE) with electrochemiluminescence (ECL) detection. The three amino acids were separated in 5 mM phosphate of pH 2.1 as background electrolyte, and detected on a 500 microm platinum disk electrode at 1.2V (versus Ag/AgCl) in the presence of 10 mM tris(2,2'-bipyridyl)ruthenium(II) dissolved in 80 mM phosphate of pH 10.5. A mass detection limit of 37.3 fmol (or 81.5 fmol) for glutamate, corresponding to the product in the enzyme reaction catalyzed by 1.24 x 10(-9)U AST (or 2.72 x 10(-9)U ALT) in a 30 min reaction period, was achieved. This assay was applied to investigate the cytotoxicity effect of ethanol on HepG2 cells and differentiating nonalcoholic steatohepatitis (NASH) from alcoholic liver disease, indicating that the technique is promising for the application in the cell biological and clinical fields.     

A smartphone metabolomics platform and its application to the assessment of cisplatin-induced kidney toxicity

The application of smartphones to medical devices has been gaining attention in addressing accessibility and cost issues in healthcare, and the detection of medically relevant compounds has been demonstrated using customized smartphone hardware and/or software. Metabolomics, a newly rising omics field, has also spawned many medical applications but requires highly sophisticated and expensive equipment. Here, we describe a portable smartphone platform, built with readily available and affordable materials, that can perform all of the critical aspects of metabolomics. Excluding the smartphone itself, the total materials for the platform were obtained at less than US $20. For spectral data acquisition, the system utilized visible light (400–700 nm) and a built-in camera. All of the data processing, statistical analysis, and final-visualization components necessary for decision making were implemented in the smartphone platform. The platform is generally applicable as long as the analytes absorb visible light. We provide a proof-of-concept example wherein the metabolomics platform was applied to the assessment of cisplatin-induced kidney toxicity in a rat model, correctly predicting 7 out of 8 test samples.     

Development of an HPLC method for the monitoring of tricyclic antidepressants in biofluids

A simple, rapid and sensitive HPLC method was developed and validated for the determination of four tricyclic antidepressants (TCAs): amitriptyline, doxepin, clomipramine (CLO) and imipramine, in pharmaceutical formulations and biological fluids. A Kromasil C(8 )analytical column (250 x 4 mm, 5 microm) was used for the separation, with a mobile phase consisting of 0.05 M CH(3)COONH(4) and CH(3)CN (45:55 v/v) delivered at 1.5 mL/min isocratically. Quantification was performed at 238 nm, with bromazepam (1.5 ng/microL) as the internal standard. The determination of TCAs in blood plasma was performed after protein precipitation. Urine analysis was performed by means of SPE using Lichrolut RP-18 Merck cartridges providing high absolute recoveries (> 94%). Direct analysis of urine was also performed after two-fold dilution. The developed method was fully validated in terms of selectivity, linearity, accuracy, precision, stability and sensitivity. Repeatability (n = 5) and between-day precision (n = 5) revealed RSD <13%. Recoveries from biological samples ranged from 91.0 to 114.0%. The absolute detection limit of the method was calculated as 0.1-0.6 ng in blood plasma and 0.2-0.5 ng in extracted urine or 0.4-0.7 in diluted urine. The method was applied to real samples of plasma from a patient under CLO treatment.     

Monitoring the effects of antagonists on protein-protein interactions with NMR spectroscopy

We describe an NMR method that directly monitors the influence of ligands on protein-protein interactions. For a two-protein interaction complex, the size of one component should be small enough (less than ca. 15 kDa) to provide a good quality (15)N((13)C) HSQC spectrum after (15)N((13)C) labeling. The size of the second unlabeled component should be large enough so that the molecular weight of the preformed complex is larger than ca. 40 kDa. When the smaller protein binds to a larger one, broadening of NMR resonances results in the disappearance of most of its cross-peaks in the HSQC spectrum. Addition of an antagonist that can dissociate the complex would restore the HSQC spectrum of the smaller component. The method directly shows whether an antagonist releases proteins in their wild-type folded states or whether it induces their denaturation, partial unfolding, or precipitation. We illustrate the method by studying lead compounds that have recently been reported to block the MDM2-p53 interaction. Activation of p53 in tumor cells by inhibiting its interaction with MDM2 offers new strategy for cancer therapy.     

Pressurized CEC coupled with QTOF‐MS for urinary metabolomics

Pressurized CEC (pCEC) coupled with ESI‐QTOF‐MS using a sheathless interface was applied for metabolomics to develop an alternative analytical method for metabolic profiling of complex biofluid samples such as urine. The hyphenated system was investigated with mixed standards and pooled urine samples to evaluate its precision, repeatability, linearity, sensitivity, and selectivity. The applied voltage, mobile phase, and gradient elution were optimized and applied for the analysis of urinary metabolites. Multivariate data analysis was subsequently performed and used to distinguish lung cancer patients from healthy controls successfully. High separation efficiency has been achieved in pCEC due to the EOF. For metabolite identification, the pCEC‐MS separation mechnism was helpful for discriminating the fragment ions of glutamine conjugates from co‐eluted metabolites. Three glutamine conjugates, including phenylacetylglutamine, acylglutamine C8:1, and acylglutamine C6:1 were identified among 16 differential urinary metabolites of lung cancer. Receiver‐operating‐characteristic analysis of acylglutamine C8:1 resulted in an area‐under‐curve value of 0.882. Overall, this work suggests that this pCEC‐ESI‐QTOF‐MS method may provide a novel and useful platform for metabolomic studies due to its superior separation and identification.