Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts

Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS). We summarize the main NMR spectroscopic applications in modern metabolic research, and provide detailed protocols for biofluid (urine, serum/plasma) and tissue sample collection and preparation, including the extraction of polar and lipophilic metabolites from tissues. 1H NMR spectroscopic techniques such as standard 1D spectroscopy, relaxation-edited, diffusion-edited and 2D J-resolved pulse sequences are widely used at the analysis stage to monitor different groups of metabolites and are described here. They are often followed by more detailed statistical analysis or additional 2D NMR analysis for biomarker discovery. The standard acquisition time per sample is 4-5 min for a simple 1D spectrum, and both preparation and analysis can be automated to allow application to high-throughput screening for clinical diagnostic and toxicological studies, as well as molecular phenotyping and functional genomics.     

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis.?Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.     

The application of NMR to study changes in polar metabolite concentrations in beef <Emphasis Type="Italic">longissimus dorsi</Emphasis> stored for different periods post mortem

In this study data generated by ¹H NMR spectroscopy were combined with chemometrics to analyse beef samples aged over a 21 day period. In particular, the amino acids, of which 12 were identified were found to increase over the ageing period with samples matured for 3 days having notably lower concentrations than carcasses aged for 21 days. This is believed to be a result of increased proteolysis within the muscle. This novel approach of using high resolution NMR spectrometry to analyse beef samples has not previously been reported and these findings demonstrate the potential of this technique linked with HPLC to be used as a suitable method for profiling meat samples.     

GC/MS analysis of the rat urine for metabonomic research

In this paper, an optimized protocol was established and validated for the metabonomic profiling in rat urine using GC/MS. The urine samples were extracted by methanol after treatment with urease to remove excessive urea, then the resulted supernatant was dried, methoximated, trimethylsilylated, and analyzed by GC/MS. Forty-nine endogenous metabolites were separated and identified in GC/MS chromatogram, of which 26 identified compounds were selected for quantitative analysis to evaluate the linearity, precision, and sensitivity of the method. It showed good linearity between mass spectrometry responses and relative concentrations of the 26 endogenous compounds over the range from 0.063 to 1.000 (v/v, urine/urine+water) and satisfactory reproducibility with intra-day and inter-days precision values all below 15%. The metabonomic profiling method based on GC/MS was successfully applied to urine samples from hyperlipidemia model rats. Obviously, separated clustering of model rats and the control rats were shown by principal components analysis (PCA); time-dependent metabonomic modification was detected as well. It was suggested that metabonomic profiling based on GC/MS be a robust method for urine samples.     

A sample preparation protocol for H nuclear magnetic resonance studies of water-soluble metabolites in blood and urine

We describe a general protocol for preparing protein-containing biofluids for ¹H nuclear magnetic resonance (NMR) metabolomic studies. In this protocol, untreated samples are diluted in deuterated solvents to precipitate proteins and recover metabolites quantitated relative to standard reference compounds such as 3-trimethylsilylpropionic acid (TSP) and 2,2-dimethyl-2-silapentane-5-sulfonic acid (DSS). The efficacy of this protocol was tested using a bovine serum albumin/metabolite mix and human serum samples. This sample preparation method can be readily applied to any protein-containing biofluid for ¹H NMR studies.     

Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data--a potential tool for multi-parametric diagnosis

A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e.g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample (approximately 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.     

High-throughput,multi-platform metabolomics on very small volumes: 1H NMR metabolite identification in an unadulterated tube-in-tube system

As small animal models of disease become more widely used, there is increasing importance and potential for characterizing their metabolomes. However, as the animal becomes smaller, the amounts of biofluids such as urine and cerebral spinal fluid available for metabolomic studies are more limited. Further, in multi-platform systems biology when the same small sample must be used for several analyses, it is a frequent requirement that no additions are made to the sample (even as simple as D₂O or an NMR chemical shift reference) to maintain sample integrity. Herein we describe a method for high-throughput ¹H-NMR studies using ~30 µl volumes, suitable for biofluid matrices. The compartmentalization of the sample and NMR standards, however, requires chemical shift corrections due to bulk magnetic susceptibility and ionic strength changes for metabolite profiling using a reference library or data-binning of the chemical shift axis. This set-up minimizes the cost of individual data collection per small animal and is suitable for high-throughput, longitudinal, multimodal metabolomic studies of biofluids available in limited quantities.     

Plasma free amino acid profiling of five types of cancer patients and its application for early detection

Background

Recently, rapid advances have been made in metabolomics-based, easy-to-use early cancer detection methods using blood samples. Among metabolites, profiling of plasma free amino acids (PFAAs) is a promising approach because PFAAs link all organ systems and have important roles in metabolism. Furthermore, PFAA profiles are known to be influenced by specific diseases, including cancers. Therefore, the purpose of the present study was to determine the characteristics of the PFAA profiles in cancer patients and the possibility of using this information for early detection.

Methods and Findings

Plasma samples were collected from approximately 200 patients from multiple institutes, each diagnosed with one of the following five types of cancer: lung, gastric, colorectal, breast, or prostate cancer. Patients were compared to gender- and age- matched controls also used in this study. The PFAA levels were measured using high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS). Univariate analysis revealed significant differences in the PFAA profiles between the controls and the patients with any of the five types of cancer listed above, even those with asymptomatic early-stage disease. Furthermore, multivariate analysis clearly discriminated the cancer patients from the controls in terms of the area under the receiver-operator characteristics curve (AUC of ROC >0.75 for each cancer), regardless of cancer stage. Because this study was designed as case-control study, further investigations, including model construction and validation using cohorts with larger sample sizes, are necessary to determine the usefulness of PFAA profiling.

Conclusions

These findings suggest that PFAA profiling has great potential for improving cancer screening and diagnosis and understanding disease pathogenesis. PFAA profiles can also be used to determine various disease diagnoses from a single blood sample, which involves a relatively simple plasma assay and imposes a lower physical burden on subjects when compared to existing screening methods.     

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.     

Automated SPE-RP-HPLC fractionation of biofluids combined to off-line NMR spectroscopy for biomarker identification in metabonomics

NMR-based metabonomics is a valuable and straightforward approach to measuring hundreds of metabolites in complex biofluids. However, metabolite identification is sometimes limited by overlapped signals in NMR spectra. We describe a new methodology using an automated hyphenation of solid phase extraction (SPE) with RP-HPLC combined to NMR spectroscopy, which allowed identification of 72 metabolites of various molecular classes in human urine. This methodology was also successfully applied to the fractionation of a cat urine sample to aid identification of aromatic compounds and felinine. The SPE-RP-HPLC method appears to be a reliable tool to support biomarker discovery in metabonomic studies.     

Pharmaceutical profiling method for lipophilicity and integrity using liquid chromatography-mass spectrometry

A method is described for the simultaneous profiling of sample lipophilicity, integrity, and purity. The method is rapid and is applicable to high throughput profiling of pharmaceutical properties in drug discovery. A short Polaris C(18) column is used with a rapid, wide-polarity mobile phase gradient, UV detection, and MS analysis. The lipophilicity of each component is estimated from a calibration curve using six drug or organic compounds and plotting their respective measured retention time versus LogD(7.4) (literature). The correlation of LogD(7.4) (literature) to LogD(7.4) (HPLC) for 60 structurally diverse drugs has a correlation coefficient r(2) of 0.89. The method is applicable to compounds with MW>200 and retention time>1.5 min for rapid, initial pharmaceutical profiling in drug discovery.     

NMR-based metabonomics of bovine blood: an investigation into the effects of long term storage on plasma samples

Freezers in research institutions often contain a plethora of samples left over from studies performed years or even decades ago. Along with samples stored in biobanks, these could prove to be treasure troves for metabonomic research. Although the influence of sample handling and short to medium term storage on conventionally determined blood parameters has been reported, little is known about the effects of long term storage (years to decades) on plasma samples. The aim of this study was to investigate the influence of long term storage on the metabolite profile and to assess the value of archived samples for metabonomic studies. Heparinised plasma of 22 heifers that had been stored at ?20 °C for between 2 and 15 years was analysed using NMR spectroscopy and statistical analysis techniques. Lactate (principal component 1) explained 79.6 % of variance between all spectra, but was not correlated with storage time. The highest correlation with storage time (R ² = 0.474) was found for betaine, with other metabolites (acetoacetate, histidines, glycerol, lipids and glucose) also showing moderate correlation (R ² values between 0.217 and 0.437). Our results indicate that samples stored for extended periods of time can potentially be used in metabonomics studies, if precautions are taken during data analysis.     

HPLC-MS-based methods for the study of metabonomics

The development and use of HPLC-MS for the study of metabonomics is reviewed. To date the technique has been applied to the analysis of urine samples obtained from studies in rodents in investigations of physiological variation (e.g., factors such as strain, gender, diurnal variation, etc.) and toxicity. Examples are provided of the use of conventional HPLC, capillary methods and the recently introduced high-resolution systems based on a combination of high pressure and small particle size ("UPLC"). Comparison is also made of the use of 1H NMR spectroscopy and HPLC-MS for the analysis of biofluid samples and the advantages and limitations of the two approaches are assessed. Likely future developments are considered.     

Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach

Detection and classification of in vivo drug toxicity is an expensive and time-consuming process. Metabolic profiling is becoming a key enabling tool in this area as it provides a unique perspective on the characterization and mechanisms of response to toxic insult. As part of the Consortium on Metabonomic Toxicology (COMET) project, a substantial metabolic and pathological database was constructed. We chose a set of 80 treatments to build a modeling system for toxicity prediction using NMR spectroscopy of urine samples (n=12935) from laboratory rats (n=1652). The compound structures and activities were diverse but there was an emphasis on the selection of hepato and nephrotoxins. We developed a two-stage strategy based on the assumptions that (a) adverse effects would produce metabolic profiles deviating from those of normal animals and (b) such deviations would be similar for treatments having similar physiological effects. To address the first stage, we developed a multivariate model of normal urine, using principal components analysis of specially preprocessed 1H NMR spectra. The model demonstrated a high correspondence between the occurrence of toxicity and abnormal metabolic profiles. In the second stage, we extended a density estimation method, "CLOUDS", to compute multidimensional similarities between treatments. Crucially, the technique allowed a distribution-free estimate of similarity across multiple animals and time points for each treatment and the resulting matrix of similarities showed segregation between liver toxins and other treatments. Using the similarity matrix, we were able to correctly identify the target organ of two "blind" treatments, even at sub-toxic levels. To further validate the approach, we then applied a leave-one-out approach to predict the main organ of toxicity (liver or kidney) showing significant responses using the three most similar matches in the matrix. Where predictions could be made, there was an error rate of 8%. The sensitivities to liver and kidney toxicity were 67 and 41%, respectively, whereas the corresponding specificities were 77 and 100%. In some cases, it was not possible to make predictions because of interference by drug-related metabolite signals (18%), an inconsistent histopathological or urinary response (11%), genuine class overlap (8%), or lack of similarity to any other treatment (2%). This study constitutes the largest validation to date of the metabonomic approach to preclinical toxicology assessment, confirming that the methodology offers practical utility for rapid in vivo drug toxicity screening.     

Hydrophilic interaction and reversed-phase ultraperformance liquid chromatography TOF-MS for serum metabonomic analysis of myocardial infarction in rats and its applications

An ultra performance liquid chromatography coupled to mass spectrometry-based metabonomic approach, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been developed to characterize the global serum metabolic profile associated with myocardial infarction (MI). The HILIC was found necessary for a comprehensive serum metabonomic profiling, providing complementary information to RP chromatography. By combining with partial least squares discriminant analysis, 21 potential biomarkers in rat serum were identified. To further elucidate the pathophysiology of MI, related metabolic pathways have been studied. It was found that MI was closely related to disturbed sphingolipid metabolism, phospholipid catabolism, fatty acid transportation and metabolism, tryptophan metabolism, branched-chain amino acids metabolism, phenylalanine metabolism, and arginine and proline metabolism. With the presented metabonomic method, we systematically analyzed the therapeutic effects of Traditional Chinese Medicine Sini decoction (SND). The results demonstrated that SND administration could provide satisfactory effects on MI through partially regulating the perturbed metabolic pathways.     

The role of nuclear magnetic resonance in medical mycology

With the continued emergence of drug-resistant invasive mycoses, rapid fungal identification and susceptibility testing are needed. Nuclear magnetic resonance (NMR) spectroscopy generates complex data (“fingerprints”) based on chemical composition and metabolite profiles, which can be applied to suspensions of living microorganisms or mammalian cells, cell and tissue extracts, biological fluids, tissue biopsies, and noninvasive diagnosis in patients when linked to MRI. Closely related fungal species can be rapidly identified based on their NMR spectra, and antifungal drug effects can be measured as metabolic end points. The feasibility of classifying groups of microorganisms directly from biological samples has been demonstrated in animal models and human infections. Potential advantages of NMR spectroscopy in medical mycology include accurate identification, automated sample delivery, automated analysis using computer-based methods, rapid turnaround time, high throughput, and low running costs. More work is needed to validate the automated approach on large data sets covering a broad spectrum of potential pathogens.     

Holistic metabonomic profiling of urine affords potential early diagnosis for bladder and kidney cancers

The clinical exploration of urinary metabonomic analysis on discriminating between the top-two-incidence urological cancers, bladder and kidney cancers (BC and KC), is still virgin land. In this study, we discovered and evaluated the clinical utility of holistic metabonomic profiling and current single biomarker methods, and ultimately suggested a potential screening test for BC and KC. Urine metabonomic profiling for 19 BC patients, 25 KC patients, and 24 healthy controls was carried out using an LC–MS based platform, which utilized both reversed phase chromatography and hydrophilic interaction chromatography. The holistic method that refers to orthogonal partial least-squares-discriminant analysis based on all qualified profile data, successfully classified BC, KC and healthy control groups, showing 100 % sensitivity and specificity. Taurine, hippuric acid, phenylacetylglutamine and carnitine species contributed most to the BC and KC discrimination. The predictive power of each above metabolite is evaluated using receiver operator characteristic technique. Hippuric acid was found 10-fold decrease in concentration relative healthy controls, and performed the best as a biomarker for BC diagnosis, with its sensitivity and specificity of 78.9 and 86.5 %, respectively. Carnitine C10:3 was found 1.5-fold decrease, and reached 84.0 % of sensitivity and 60.5 % of specificity for KC diagnosis. In view of both sensitivity and specificity, the holistic method is more accurate for detecting BC and KC, than current single metabonomic biomarker methods, and it could be advocated as a prescreen to other forms of more invasive or uncomfortable screening. Moreover, this approach also demonstrates attractive performance in diagnosis of early stage (ES) BC and KC patients.     

Human Breath Analysis May Support the Existence of Individual Metabolic Phenotypes

The metabolic phenotype varies widely due to external factors such as diet and gut microbiome composition, among others. Despite these temporal fluctuations, urine metabolite profiling studies have suggested that there are highly individual phenotypes that persist over extended periods of time. This hypothesis was tested by analyzing the exhaled breath of a group of subjects during nine days by mass spectrometry. Consistent with previous metabolomic studies based on urine, we conclude that individual signatures of breath composition exist. The confirmation of the existence of stable and specific breathprints may contribute to strengthen the inclusion of breath as a biofluid of choice in metabolomic studies. In addition, the fact that the method is rapid and totally non-invasive, yet individualized profiles can be tracked, makes it an appealing approach.     

Evaluation of specific gravity as normalization strategy for cattle urinary metabolome analysis

Urine is an ideal biofluid for metabolomics studies since it is obtained noninvasively, and its composition is affected by genetic and environmental factors reflecting the physiology of multiple organs. However, urine dilution effects and instrumental variation from the analytical method play a significant confounding role when one attempts to characterize biological and physiological factors through NMR and MS measurements of small molecule concentrations. Several normalization approaches have been used for urinary metabolomics studies and normalization to osmolality or to total useful MS signal have been proposed. When dealing with urinary metabolome analysis in cattle, freeze-drying (FD) is the method commonly used for normalization purposes. Herein, normalization to specific gravity, which provides a fair estimation of urine osmolality, was compared to the time consuming FD step and to the normalization to total useful MS signal in order to assess if this approach could be used as normalization strategy to differentiate control from anabolic treated animals. The results revealed that ~80 % of the metabolites detected as constituting the acquired MS fingerprints for the freeze-dried samples and for the samples normalized to both specific gravity (SG) and total useful MS signal were in common. In addition, similar information from the multivariate statistical analysis was obtained by both normalization approaches. We demonstrate, therefore, that SG can be used as normalization approach for urinary metabolome analysis in cattle resulting in a high sample throughput procedure when compared with the FD step.     

Modelling the acid/base <Superscript>1</Superscript>H NMR chemical shift limits of metabolites in human urine

Introduction

Despite the use of buffering agents the ¹H NMR spectra of biofluid samples in metabolic profiling investigations typically suffer from extensive peak frequency shifting between spectra. These chemical shift changes are mainly due to differences in pH and divalent metal ion concentrations between the samples. This frequency shifting results in a correspondence problem: it can be hard to register the same peak as belonging to the same molecule across multiple samples. The problem is especially acute for urine, which can have a wide range of ionic concentrations between different samples.

Objectives

To investigate the acid, base and metal ion dependent ¹H NMR chemical shift variations and limits of the main metabolites in a complex biological mixture.

Methods

Urine samples from five different individuals were collected and pooled, and pre-treated with Chelex-100 ion exchange resin. Urine samples were either treated with either HCl or NaOH, or were supplemented with various concentrations of CaCl₂, MgCl₂, NaCl or KCl, and their ¹H NMR spectra were acquired.

Results

Nonlinear fitting was used to derive acid dissociation constants and acid and base chemical shift limits for peaks from 33 identified metabolites. Peak pH titration curves for a further 65 unidentified peaks were also obtained for future reference. Furthermore, the peak variations induced by the main metal ions present in urine, Na⁺, K⁺, Ca²⁺ and Mg²⁺, were also measured.

Conclusion

These data will be a valuable resource for ¹H NMR metabolite profiling experiments and for the development of automated metabolite alignment and identification algorithms for ¹H NMR spectra.