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.     

Ratio analysis nuclear magnetic resonance spectroscopy for selective metabolite identification in complex samples

Metabolite identification in the complex NMR spectra of biological samples is a challenging task due to significant spectral overlap and limited signal-to-noise. In this study we present a new approach, RANSY (ratio analysis NMR spectroscopy), which identifies all the peaks of a specific metabolite on the basis of the ratios of peak heights or integrals. We show that the spectrum for an individual metabolite can be generated by exploiting the fact that the peak ratios for any metabolite in the NMR spectrum are fixed and proportional to the relative numbers of magnetically distinct protons. When the peak ratios are divided by their coefficients of variation derived from a set of NMR spectra, the generation of an individual metabolite spectrum is enabled. We first tested the performance of this approach using one-dimensional (1D) and two-dimensional (2D) NMR data of mixtures of synthetic analogues of common body fluid metabolites. Subsequently, the method was applied to (1)H NMR spectra of blood serum samples to demonstrate the selective identification of a number of metabolites. The RANSY approach, which does not need any additional NMR experiments for spectral simplification, is easy to perform and has the potential to aid in the identification of unknown metabolites using 1D or 2D NMR spectra in virtually any complex biological mixture.     

Heteronuclear 1H-31P statistical total correlation NMR spectroscopy of intact liver for metabolic biomarker assignment: application to galactosamine-induced hepatotoxicity

As part of our ongoing development of methods for enhanced biomarker information recovery from spectroscopic data we present the first example of a new hetero-nuclear statistical total correlation spectroscopy (HET-STOCSY) approach applied to intact tissue samples collected as part of a toxicological study. One-dimensional 1H and 31P-{1H} magic angle spinning (MAS) NMR spectra of intact liver samples after galactosamine (galN) treatment to rats and after cotreatment of galN plus uridine were collected at 275 K. Individual samples were also followed by 1H and 31P-{1H} MAS NMR through time generating time dependent modulations in metabolite signatures relating to toxicity. High-resolution 1H NMR spectra of urine and plasma and clinical chemical data were also collected to establish a biological framework in which to place these novel statistical heterospectroscopic data. In HET-STOCSY, calculation of the covariance between the 31P-{1H} and 1H NMR signals of phosphorus containing metabolites allows their molecular connectivities to be established and the construction of virtual two-dimensional heteronuclear correlation spectra that connect all protons on the molecule to the heteroatom. We show how HET-STOCSY applied to MAS NMR spectra of liver samples can be used to augment biomarker detection. This approach is generic and can be applied to correlate the covarying signals for any spin-active nuclei where there is parallel or serial collection of data.     

Virtual chromatographic resolution enhancement in cryoflow LC-NMR experiments via statistical total correlation spectroscopy

A new approach to enhancing information recovery from cryogenic probe "on-flow" LC-NMR spectroscopic analyses of complex biological mixtures is demonstrated using a variation on the statistical total correlation spectroscopy (STOCSY) method. Cryoflow probe technology enables sensitive and efficient NMR detection of metabolites on-flow, and the rapid spectral scanning allows multiple spectra to be collected over chromatographic peaks containing several species with similar, but nonidentical, retention times. This enables 1H NMR signal connectivities between close-eluting metabolites to be identified resulting in a "virtual" chromatographic resolution enhancement visualized directly in the NMR spectral projection. We demonstrate the applicability of the approach for structure assignment of drug and endogenous metabolites in urine. This approach is of wide general applicability to any complex mixture analysis problem involving chromatographic peak overlap and with particular application in metabolomics and metabonomics.     

Hierarchical Alignment and Full Resolution Pattern Recognition of 2D NMR Spectra: Application to Nematode Chemical Ecology

Nuclear magnetic resonance (NMR) is the most widely used nondestructive technique in analytical chemistry. In recent years, it has been applied to metabolic profiling due to its high reproducibility, capacity for relative and absolute quantification, atomic resolution, and ability to detect a broad range of compounds in an untargeted manner. While one-dimensional (1D) (1)H NMR experiments are popular in metabolic profiling due to their simplicity and fast acquisition times, two-dimensional (2D) NMR spectra offer increased spectral resolution as well as atomic correlations, which aid in the assignment of known small molecules and the structural elucidation of novel compounds. Given the small number of statistical analysis methods for 2D NMR spectra, we developed a new approach for the analysis, information recovery, and display of 2D NMR spectral data. We present a native 2D peak alignment algorithm we term HATS, for hierarchical alignment of two-dimensional spectra, enabling pattern recognition (PR) using full-resolution spectra. Principle component analysis (PCA) and partial least squares (PLS) regression of full resolution total correlation spectroscopy (TOCSY) spectra greatly aid the assignment and interpretation of statistical pattern recognition results by producing back-scaled loading plots that look like traditional TOCSY spectra but incorporate qualitative and quantitative biological information of the resonances. The HATS-PR methodology is demonstrated here using multiple 2D TOCSY spectra of the exudates from two nematode species: Pristionchus pacificus and Panagrellus redivivus. We show the utility of this integrated approach with the rapid, semiautomated assignment of small molecules differentiating the two species and the identification of spectral regions suggesting the presence of species-specific compounds. These results demonstrate that the combination of 2D NMR spectra with full-resolution statistical analysis provides a platform for chemical and biological studies in cellular biochemistry, metabolomics, and chemical ecology.     

Quantification of organic and amino acids in beer by 1H NMR spectroscopy

The quantification of organic and amino acids in beer using 1H NMR spectroscopy is demonstrated. Quantification was made both by integration of signals in the spectra together with use of calibration references and by use of partial least-squares (PLS) regression. Results from the NMR quantifications were compared with those obtained from determinations by amino acid analysis on HPLC and organic acid analysis by capillary electrophoresis. The described NMR-based methods could satisfactorily be used for quantification of several of the investigated metabolites in beer down to approximately 10 mg/L and for most with a good to high accuracy compared to results obtained by HPLC and capillary electrophoresis (R2 0.90-0.99). This was achieved with a simple sample preparation and one-dimensional 1H NMR spectra obtained in a few minutes. The use of PLS clearly improves the accuracy of the quantifications, based on comparison to results obtained by HPLC and capillary electrophoresis, and furthermore permits the determination of components with partially overlapped signals in the spectrum. NMR spectroscopy in combination with PLS will be a useful tool for the quantification of metabolites, not only in beer but also in other beverages and biofluids.     

Magic angle spinning NMR and 1H-31P heteronuclear statistical total correlation spectroscopy of intact human gut biopsies

Previously we have demonstrated the use of 1H magic angle spinning (MAS) NMR spectroscopy for the topographical variations in functional metabolic signatures of intact human intestinal biopsy samples. Here we have analyzed a series of MAS 1H NMR spectra (spin-echo, one-dimensional, and diffusion-edited) and 31P-{1H} spectra and focused on analyzing the enhancement of information recovery by use of the statistical total correlation spectroscopy (STOCSY) method. We have applied a heterospectroscopic cross-examination performed on the same samples and between 1H and 31P-{1H} spectra (heteronuclear STOCSY) to recover latent metabolic information. We show that heterospectroscopic correlation can give new information on the molecular compartmentation of metabolites in intact tissues, including the statistical "isolation" of a phospholipid/triglyceride vesicle pool in intact tissue. The application of 31P-1H HET-STOCSY allowed the cross-assignment of major 31P signals to their equivalent 1H NMR spectra, e.g., for phosphorylcholine and phosphorylethanolamine. We also show pathway correlations, e.g., the ascorbate-glutathione pathway, in the STOCSY analysis of intact tissue spectra. These 31P-1H HET-STOCSY spectra also showed different topographical regions, particular for minor signals in different tissue microenvironments. This approach could be extended to allow the detection of altered distributions within metabolic subcompartments as well as conventional metabonomics concentration-based diagnostics.     

Strategy for automated analysis of dynamic metabolic mixtures by NMR. Application to an insect venom

Elucidation of the composition of chemical-biological samples is a main focus of systems biology and metabolomics. Due to the inherent complexity of these mixtures, reliable, efficient, and potentially automatable methods are needed to identify the underlying metabolites and natural products. Because of its rich chemical information content, nuclear magnetic resonance (NMR) spectroscopy has a unique potential for this task. Here we present a generalization and application of a recently introduced NMR data collection, processing, and analysis strategy that circumvents the need for extensive purification and hyphenation prior to analysis. It uses covariance TOCSY NMR spectra measured on a 1-mm high-temperature cryogenic probe that are analyzed by a spectral trace clustering algorithm yielding 1D NMR spectra of the individual components for their unambiguous identification. The method is demonstrated on a metabolic model mixture and is then applied to the unpurified venom mixture of an individual walking stick insect that contains several slowly interconverting and closely related metabolites.     

Curve-fitting method for direct quantitation of compounds in complex biological mixtures using 1H NMR: application in metabonomic toxicology studies

A new software tool has been developed that provides automated measurement of signal intensities in NMR spectra of complex mixtures without using data reduction procedures. The algorithm finds best-fit transformations between signals in reference compound spectra and the corresponding signals in analyte spectra. Unlike other algorithms, it is insensitive to variation in chemical shift and can even be used for relative quantitation of compounds whose identities have not yet been established. Additionally, the parameters of the transformation provide information and error metrics that may assist in the streamlining of quality control. The approach presented is general in scope but has been tested by application to peak quantitation in NMR spectra of biofluids. Replicate NMR measurements of solutions of biologically important compounds at various concentrations were made. Further NMR data were collected on urine samples from human, rat, and mouse, which were "spiked" with reference compound solutions at known concentrations. Finally, existing data from an independent toxicology project involving several hundred samples were analyzed, and the consistency of the measurements for metabolites that give multiple NMR signals was assessed. The results of all these tests give confidence that the technique can be used in automated quantitation of compounds in large NMR data sets with minimal operator intervention.     

A simple background elimination method for Raman spectra

In this paper, we consider a new background elimination method for Raman spectra. The proposed method is based on peak detection, smoothing, and interpolation. Since the background is usually slowly varying with respect to wavelength, we could estimate the background by eliminating significant peaks. For this purpose, we seek the peaks by inspecting the smoothed derivative of a given spectrum. After clipping out the corresponding peak regions, we estimate the background by applying a modified linear interpolation. Then the background is eliminated from the measured Raman spectrum by simple subtraction. The experimental results showed that the proposed method gave satisfactory results for real Raman spectra as well as synthetic data. As the proposed method requires no prior knowledge of spectrum, we expect that the method could be applied to other spectral data as well.     

Application of nonselective 1D (1)H-(31)P inverse NMR spectroscopy to the screening of solutions for the presence of organophosphorus compounds related to the chemical weapons convention

1D nonselective (1)H-(31)P HSQMBC, HSQC, and (31)P decoupled HSQC NMR experiments were applied to the screening of original OPCW proficiency test samples for the presence of organophosphorus (OP) compounds related to the Chemical Weapons Convention. The HSQC and HSQMBC spectra are compared to 1D (1)H NMR spectra with WET solvent suppression and (31)P[(1)H] spectra of the same samples. The 1D nonselective HSQC and HSQMBC experiments are shown to be the most sensitive NMR experiments to selectively screen samples for the presence of organophosphorus(OP) compounds. These experiments are at least three times more sensitive than the (31)P[(1)H] NMR experiment and allow the determination of the number of OP compounds present in the sample and their alkyl group bound to the phosphorus atom. Samples spiked at the 5-10 ppm level can be screened within an hour for the presence of OP compounds, whereas for the (31)P[(1)H] experiments, an overnight acquisition is necessary. The sensitivity of the experiments decreases in the order (31)P decoupled HSQC, HSQMBC, and HSQC. For the different alkyl groups, the sensitivity of these experiments decreases in the order methyl approximately isopropyl > ethyl > propyl.     

Effect of magnetic field strength on NMR-based metabonomic human urine data. Comparative study of 250, 400, 500, and 800 MHz

Metabonomic analysis of urine utilizing high-resolution NMR spectroscopy and chemometric techniques has proven valuable in characterizing the biochemical response to an intervention. To assess the effect of magnetic field strength on information contained in NMR-based metabonomic data sets, 1H NMR spectra were acquired on 250-, 400-, 500-, and 800-MHz instruments, respectively, on the same set of human urine samples collected before and after dietary interventions with milk and with meat proteins. Partial least-squares regression discriminant analyses (PLS-DA) were performed in order to elucidate the ability of the 1H spectra acquired at various field strengths to identify possible spectral differences and discriminate between pre- and postintervention samples. The loadings from PLS-DA contained the same spectral regions, implying that the same metabolites were involved in the discrimination independent of magnetic field strength. The investigation revealed a strong increase in prediction performance and thereby spectral information content when increasing the magnetic field strength from 250 to 500 MHz, while from 500 to 800 MHz the increase was less pronounced.     

Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets

We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.     

Online coupling of gas chromatography to nuclear magnetic resonance spectroscopy: method for the analysis of volatile stereoisomers

The identification of volatile cis/trans-stereoisomers was accomplished by employing a hyphenated GC-NMR system. The chromatographic and spectroscopic conditions were optimized with respect to the (1)H NMR detection. A special processing technique was developed to handle the recorded NMR spectra in the gas phase with very low sample amounts. The processed stopped-flow (1)H NMR spectra of the investigated chromatographic peaks unequivocally revealed the structure of the corresponding compounds.     

Method for determining molar concentrations of metabolites in complex solutions from two-dimensional 1H-13C NMR spectra

One-dimensional (1D) (1)H nuclear magnetic resonance (NMR) spectroscopy is used extensively for high-throughput analysis of metabolites in biological fluids and tissue extracts. Typically, such spectra are treated as multivariate statistical objects rather than as collections of quantifiable metabolites. We report here a two-dimensional (2D) (1)H-(13)C NMR strategy (fast metabolite quantification, FMQ, by NMR) for identifying and quantifying the approximately 40 most abundant metabolites in biological samples. To validate this technique, we prepared mixtures of synthetic compounds and extracts from Arabidopsis thaliana, Saccharomyces cerevisiae, and Medicago sativa. We show that accurate (technical error 2.7%) molar concentrations can be determined in 12 min using our quantitative 2D (1)H-(13)C NMR strategy. In contrast, traditional 1D (1)H NMR analysis resulted in 16.2% technical error under nearly ideal conditions. We propose FMQ by NMR as a practical alternative to 1D (1)H NMR for metabolomics studies in which 50-mg (extract dry weight) samples can be obtained.     

Molecular factor analysis applied to collections of NMR spectra

It is often useful to identify and quantify mixture components by analyzing collections of NMR spectra. Such collections arise in metabonomics and many other applications. Many mixtures studied by NMR can contain hundreds of compounds, and it is challenging to analyze the resulting complex spectra. We have approached the problem of separating signals from different molecules in complex mixtures by using self-modeling curve resolution as implemented by the alternating least-squares algorithm. Alternating least squares uses nonnegativity criteria to generate spectra and concentrations from a collection of mixture spectra. Compared to previous applications of alternating least squares, NMR spectra of complex mixtures possess unique features, such as large numbers of components and sample-to-sample variability in peak positions. To deal with these features, we developed a set of data preprocessing methods, and we made modifications to the alternating least-squares algorithm. We use the term "molecular factor analysis" to refer to the preprocessing and modified alternating least-squares methods. Molecular factor analysis was tested using an artificial data set and spectra from a metabonomics study. The results show that the tools can extract valuable information on sample composition from sets of NMR spectra.     

Metabolic assessment of human liver transplants from biopsy samples at the donor and recipient stages using high-resolution magic angle spinning 1H NMR spectroscopy

This work presents the first application of high-resolution magic angle spinning (HR-MAS) 1H NMR spectroscopy to human liver biopsy samples, allowing a determination of their metabolic profiles before removal from donors, during cold perfusion, and after implantation into recipients. The assignment of peaks observed in the 1H HR-MAS NMR spectra was aided by the use of two-dimensional J-resolved, TOCSY and 1H-13C HMQC spectra. The spectra were dominated by resonances from triglycerides, phospholipids, and glycogen and from a variety of small molecules including glycerophosphocholine (GPC), glucose, lactate, creatine, acetate, amino acids, and nucleoside-related compounds such as uridine and adenosine. In agreement with histological data obtained on the same biopsies, two of the six livers were found to contain high amounts of triglycerides by NMR spectroscopy, which also indicated that these tissues contained a higher degree of unsaturated lipids and a lower proportion of phospholipids and low molecular weight compounds. Additionally, proton T2 relaxation times indicated two populations of lipids, a higher mobility triglyceride fraction and a lower mobility phospholipid fraction, the proportions of which changed according to the degree of fat content. GPC was found to decrease from the pretransplant to the posttransplant biopsy of all livers except for one with a histologically confirmed high lipid content, and this might represent a biomarker of liver function posttransplantation. NMR signals produced by the liver preservation solution were clearly detected in the cold perfusion stage biopsies of all livers but remained in the posttransplant spectra of only the two livers with a high lipid content and were prominent mainly in the graft that later developed primary graft dysfunction. This study has shown biochemical differences between livers used for transplants that can be related to the degree and type of lipid composition. This technology might therefore provide a novel screening approach for donor organ quality and a means to assess function in the recipient after transplantation.     

Monitoring diet effects via biofluids and their implications for metabolomics studies

The effect of diet on metabolites found in rat urine samples has been investigated using nuclear magnetic resonance (NMR) and a new ambient ionization mass spectrometry experiment, extractive electrospray ionization mass spectrometry (EESI-MS). Urine samples from rats with three different dietary regimens were readily distinguished using multivariate statistical analysis on metabolites detected by NMR and MS. To observe the effect of diet on metabolic pathways, metabolites related to specific pathways were also investigated using multivariate statistical analysis. Discrimination is increased by making observations on restricted compound sets. Changes in diet at 24-h intervals led to predictable changes in the spectral data. Principal component analysis was used to separate the rats into groups according to their different dietary regimens using the full NMR, EESI-MS data or restricted sets of peaks in the mass spectra corresponding only to metabolites found in the urea cycle and metabolism of amino groups pathway. By contrast, multivariate analysis of variance from the score plots showed that metabolites of purine metabolism obscure the classification relative to the full metabolite set. These results suggest that it may be possible to reduce the number of statistical variables used by monitoring the biochemical variability of particular pathways. It should also be possible by this procedure to reduce the effect of diet in the biofluid samples for such purposes as disease detection.     

Quantification of high-resolution (1)H NMR spectra from rat brain extracts

Extracting quantitative information about absolute concentrations from high-resolution (1)H NMR spectra of complex mixtures such as brain extracts remains challenging. Partial overlap of resonances complicates integration, whereas simple line fitting algorithms cannot accommodate the spectral complexity of coupled spin systems. Here, it is shown that high-resolution (1)H NMR spectra of rat brain extracts from 11 distinct brain regions can be reproducibly quantified using a basis set of 29 compounds. The basis set is simulated with the density matrix formalism using complete prior knowledge of chemical shifts and scalar couplings. A crucial aspect to obtain reproducible results was the inclusion of a line shape distortion common among all 73 resonances of the 29 compounds. All metabolites could be quantified with <10% and <3% inter- and intrasubject variation, respectively.     

Two-dimensional correlation analysis of nuclear magnetic resonance metabonomics data

Two-dimensional (2D) correlation analysis has been used in this study to identify changes in complex nuclear magnetic resonance (NMR) metabonomics spectra of rat urine samples obtained during a study in which vasculitis (vascular injury), an important safety element in preclinical trials, was induced. Two types of correlation analysis were performed, along the variables and along the samples, and both 2D covariance and correlation coefficient maps were calculated. The binned and 'raw' NMR spectra were analyzed (0.04 and 0.001 ppm resolution, respectively). Good correlation was found among the major peaks of the binned spectra, and two groups of samples were identified using sample-sample 2D correlation maps. Much more complex correlation features were obtained from the 'raw' spectra, in which the specific, butterfly-like patterns were obtained in the covariance map but with only a few significant correlation coefficients in the corresponding 2D correlation maps. In terms of classification, the same group of the last nine spectra that indicated the end of the process and clustered in the 2D sample-sample covariance map of the binned data was also found in the 2D sample-sample covariance map of the raw NMR spectra but, again, not in the 2D correlation coefficient map. A discussion is given on the details of the application of the correlation analysis with regard to spectral data resolution, alignment, the effect of actual intensities of the NMR signal, and reference to various results from 2D correlation analysis of vibrational spectra.