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.     

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.     

Metabolite projection analysis for fast identification of metabolites in metabonomics. Application in an amiodarone study

The assignment of significantly changed NMR signals, which were identified with the help of multivariate models, to individual metabolites in biofluids is a manual and tedious task requiring knowledge in chemometrics and NMR spectroscopy. Metabolite projection analysis, introduced in this work, allows automatic linking of multivariate models with metabolites by skipping the level of manual NMR signal identification. The method depends on the projection of sets of metabolite NMR spectra from a database into PCA or PLS models of NMR spectra of biofluid samples. Metabolites that are significantly changed can be identified graphically in metabolite projection plots or numerically as projected virtual concentration. The method is demonstrated together with a newly introduced algorithm for refined nonequidistant binning using a metabonomics study with amiodarone as administered drug. Amiodarone can induce phospholipidosis in the lung and liver, which is accompanied by associated organ toxicity in these organs. It is shown how metabolite projection analysis allows easy and fast tentative assignment of all structures of metabolites whose concentrations in the urine samples significantly changed upon dosage. These metabolites had also been identified previously by manually interpreting the multivariate models and spectra. Among these metabolites, phenylacetylglycine was also identified as being significantly increased. This metabolite has recently been proposed as urinary biomarker for phospholipidosis.     

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.     

"Add to subtract": a simple method to remove complex background signals from the 1H nuclear magnetic resonance spectra of mixtures

Because of its highly reproducible and quantitative nature and minimal requirements for sample preparation or separation, (1)H nuclear magnetic resonance (NMR) spectroscopy is widely used for profiling small-molecule metabolites in biofluids. However (1)H NMR spectra contain many overlapped peaks. In particular, blood serum/plasma and diabetic urine samples contain high concentrations of glucose, which produce strong peaks between 3.2 ppm and 4.0 ppm. Signals from most metabolites in this region are overwhelmed by the glucose background signals and become invisible. We propose a simple "Add to Subtract" background subtraction method and show that it can reduce the glucose signals by 98% to allow retrieval of the hidden information. This procedure includes adding a small drop of concentrated glucose solution to the sample in the NMR tube, mixing, waiting for an equilibration time, and acquisition of a second spectrum. The glucose-free spectra are then generated by spectral subtraction using Bruker Topspin software. Subsequent multivariate statistical analysis can then be used to identify biomarker candidate signals for distinguishing different types of biological samples. The principle of this approach is generally applicable for all quantitative spectral data and should find utility in a variety of NMR-based mixture analyses as well as in metabolite profiling.     

Measurement of absolute concentrations of individual compounds in metabolite mixtures by gradient-selective time-zero 1H-13C HSQC with two concentration references and fast maximum likelihood reconstruction analysis

Time-zero 2D (13)C HSQC (HSQC(0)) spectroscopy offers advantages over traditional 2D NMR for quantitative analysis of solutions containing a mixture of compounds because the signal intensities are directly proportional to the concentrations of the constituents. The HSQC(0) spectrum is derived from a series of spectra collected with increasing repetition times within the basic HSQC block by extrapolating the repetition time to zero. Here we present an alternative approach to data collection, gradient-selective time-zero (1)H-(13)C HSQC(0) in combination with fast maximum likelihood reconstruction (FMLR) data analysis and the use of two concentration references for absolute concentration determination. Gradient-selective data acquisition results in cleaner spectra, and NMR data can be acquired in both constant-time and non-constant-time mode. Semiautomatic data analysis is supported by the FMLR approach, which is used to deconvolute the spectra and extract peak volumes. The peak volumes obtained from this analysis are converted to absolute concentrations by reference to the peak volumes of two internal reference compounds of known concentration: DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) at the low concentration limit (which also serves as chemical shift reference) and MES (2-(N-morpholino)ethanesulfonic acid) at the high concentration limit. The linear relationship between peak volumes and concentration is better defined with two references than with one, and the measured absolute concentrations of individual compounds in the mixture are more accurate. We compare results from semiautomated gsHSQC(0) with those obtained by the original manual phase-cycled HSQC(0) approach. The new approach is suitable for automatic metabolite profiling by simultaneous quantification of multiple metabolites in a complex mixture.     

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.     

Quantification and identification of components in solution mixtures from 1D proton NMR spectra using singular value decomposition

One-dimensional proton NMR spectra of complex solutions provide rich molecular information, but limited chemical shift dispersion creates peak overlap that often leads to difficulty in peak identification and analyte quantification. Modern high-field NMR spectrometers provide high digital resolution with improved peak dispersion. We took advantage of these spectral qualities and developed a quantification method based on linear least-squares fitting using singular value decomposition (SVD). The linear least-squares fitting of a mixture spectrum was performed on the basis of reference spectra from individual small-molecule analytes. Each spectrum contained an internal quantitative reference (e.g., DSS-d6 or other suitable small molecules) by which the intensity of the spectrum was scaled. Normalization of the spectrum facilitated quantification based on peak intensity using linear least-squares fitting analysis. This methodology provided quantification of individual analytes as well as chemical identification. The analysis of small-molecule analytes over a wide concentration range indicated the accuracy and reproducibility of the SVD-based quantification. To account for the contribution from residual protein, lipid or polysaccharide in solution, a reference spectrum showing the macromolecules or aggregates was obtained using a diffusion-edited 1D proton NMR analysis. We demonstrated this approach with a mixture of small-molecule analytes in the presence of macromolecules (e.g., protein). The results suggested that this approach should be applicable to the quantification and identification of small-molecule analytes in complex biological samples.     

Web server based complex mixture analysis by NMR

Comprehensive metabolite identification and quantification of complex biological mixtures are central aspects of metabolomics. NMR shows excellent promise for these tasks. An automated fingerprinting strategy is presented, termed COLMAR query, which screens NMR chemical shift lists or raw 1D NMR cross sections taken from covariance total correlation spectroscopy (TOCSY) spectra or other multidimensional NMR spectra against an NMR spectral database. Cross peaks are selected using local clustering to avoid ambiguities between chemical shifts and scalar J-coupling effects. With the use of three different algorithms, the corresponding chemical shift list is then screened against chemical shift lists extracted from 1D spectra of a NMR database. The resulting query scores produced by forward assignment, reverse assignment, and bipartite weighted-matching algorithms are combined into a consensus score, which provides a robust means for identifying the correct compound. The approach is demonstrated for a metabolite model mixture that is screened against the metabolomics BioMagResDatabank (BMRB). This NMR-based compound identification approach has been implemented in a public Web server that allows the efficient analysis of a wide range of metabolite mixtures.     

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.     

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.     

Between-person comparison of metabolite fitting for NMR-based quantitative metabolomics

Nuclear magnetic resonance (NMR) spectroscopy is widely used as an analytical platform for metabolomics. Many studies make use of 1D spectra, which have the advantages of relative simplicity and rapid acquisition times. The spectral data can then be analyzed either with a chemometric workflow or by an initial deconvolution or fitting step to generate a list of identified metabolites and associated sample concentrations. Various software tools exist to simplify the fitting process, but at least for 1D spectra, this still requires a degree of skilled operator input. It is of critical importance that we know how much person-to-person variability affects the results, in order to be able to judge between different studies. Here we tested a commercially available software package (Chenomx' NMR Suite) for fitting metabolites to a set of NMR spectra of yeast extracts and compared the output of five different people for both metabolite identification and quantitation. An initial comparison showed good agreement for a restricted set of common metabolites with characteristic well-resolved resonances but wide divergence in the overall identities and number of compounds fitted; refitting according to an agreed set of metabolites and spectral processing approach increased the total number of metabolites fitted but did not dramatically increase the quality of the metabolites that could be fitted without prior knowledge about peak identity. Hence, robust peak assignments are required in advance of manual deconvolution, when the widest range of metabolites is desired. However, very low concentration metabolites still had high coefficients of variation even with shared information on peak assignment. Overall, the effect of the person was less than the experimental group (in this case, sampling method) for almost all of the metabolites.     

Statistical correlation and projection methods for improved information recovery from diffusion-edited NMR spectra of biological samples

Although NMR spectroscopic techniques coupled with multivariate statistics can yield much useful information for classifying biological samples based on metabolic profiles, biomarker identification remains a time-consuming and complex procedure involving separation methods, two-dimensional NMR, and other spectroscopic tools. We present a new approach to aid complex biomixture analysis that combines diffusion ordered (DO) NMR spectroscopy with statistical total correlation spectroscopy (STOCSY) and demonstrate its application in the characterization of urinary biomarkers and enhanced information recovery from plasma NMR spectra. This method relies on calculation and display of the covariance of signal intensities from the various nuclei on the same molecule across a series of spectra collected under different pulsed field gradient conditions that differentially attenuate the signal intensities according to translational molecular diffusion rates. We term this statistical diffusion-ordered spectroscopy (S-DOSY). We also have developed a new visualization tool in which the apparent diffusion coefficients from DO spectra are projected onto a 1D NMR spectrum (diffusion-ordered projection spectroscopy, DOPY). Both methods either alone or in combination have the potential for general applications to any complex mixture analysis where the sample contains compounds with a range of diffusion coefficients.     

Evaluation of a semipolar solvent system as a step toward heteronuclear multidimensional NMR-based metabolomics for 13C-labeled bacteria, plants, and animals

Nuclear magnetic resonance (NMR) has become a key technology in metabolomics, with the use of stable isotope labeling and advanced heteronuclear multidimensional NMR techniques. In this paper, we focus on the evaluation of extraction solvents to improve NMR-based methodologies for metabolomics. Line broadening is a serious barrier to detecting signals and the annotation of metabolites using multidimensional NMR. We evaluated a series of NMR solvents for easy and versatile single-step extraction using the (13)C-labeled photosynthetic bacterium Rhodobacter sphaeroides, which shows pronounced broadening of NMR signals. The performance of each extraction solvent was judged using 2D (1)H-(13)C heteronuclear single quantum coherence (HSQC) spectra, considering three metrics: (1) distribution of the line width at half height, (2) number of observed signals, and (3) the total observed signal intensity. Considering the total rank values for the three metrics, we chose methanol-d(4) (MeOD) as a semipolar extraction solvent that can sufficiently sharpen the line width and affords better-quality NMR spectra. We also evaluated the series of extraction solvents by means of inductively coupled plasma optical emission spectroscopy (ICP-OES) based ionomics approach. It was also indicated that MeOD is useful for excluding paramagnetic ions as well as macromolecules in an easy single-step extraction. MeOD extraction also appeared to be effective for other bacterial and animal samples. An additional advantage of this semipolar solvent is that it supplements the aqueous (polar) buffer system reported by many groups. The flexible, appropriate application of polar and semipolar extraction should contribute to the large-scale analysis of metabolites.     

Quantitative 1H NMR spectroscopy of blood plasma metabolites

The absolute quantification of blood plasma metabolites by proton NMR spectroscopy is complicated by the presence of a baseline and broad resonances originating from serum macromolecules and lipoproteins. A method for spectral simplification of proton NMR spectra of blood plasma is presented. Serum macromolecules and metabolites are completely separated by utilizing the large difference in translational diffusion coefficients in combination with diffusion-sensitized proton NMR spectroscopy. The concentration of blood plasma metabolites can be quantified by using formate as an internal concentration reference. The results are compared with those obtained with ultrafiltration, a traditional method for separating macromolecules and metabolites, and demonstrate an excellent correlation between the two methods. The general nature of diffusion-sensitized NMR spectroscopy allows application on a wide range of biological fluids.     

Microscale LC-MS-NMR platform applied to the identification of active cyanobacterial metabolites

An LC-MS-NMR platform is demonstrated, which combines two innovations in microscale analysis, nanoSplitter LC-MS and microdroplet NMR, for the identification of unknown compounds found at low concentrations in complex sample matrixes as frequently encountered in metabolomics or natural products discovery. The nanoSplitter provides the high sensitivity of nanoelectrospray MS while allowing 98% of the HPLC effluent from a large-bore LC column to be collected and concentrated for NMR. Microdroplet NMR is a droplet microfluidic NMR loading method providing severalfold higher sample efficiency than conventional flow injection methods. Performing NMR offline from LC-UV-MS accommodates the disparity between MS and NMR in their sample mass and time requirements, as well as allowing NMR spectra to be requested retrospectively, after review of the LC-MS data. Interpretable 1D NMR spectra were obtained from analytes at the 200-ng level, in 1 h/well automated NMR data acquisitions. The system also showed excellent intra- and interdetector reproducibility with retention time RSD values less than 2% and sample recovery on the order of 93%. When applied to a cyanobacterial extract showing antibacterial activity, the platform recognized several previously known metabolites, down to the 1% level, in a single 30-mug injection, and prioritized one unknown for further study.     

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.     

Deconvolution of chemical mixtures with high complexity by NMR consensus trace clustering

Identification and quantification of analytes in complex solution-state mixtures are critical procedures in many areas of chemistry, biology, and molecular medicine. Nuclear magnetic resonance (NMR) is a unique tool for this purpose providing a wealth of atomic-detail information without requiring extensive fractionation of the samples. We present three new multidimensional-NMR based approaches that are geared toward the analysis of mixtures with high complexity at natural (13)C abundance, including approaches that are encountered in metabolomics. Common to all three approaches is the concept of the extraction of one-dimensional (1D) consensus spectral traces or 2D consensus planes followed by clustering, which significantly improves the capability to identify mixture components that are affected by strong spectral overlap. The methods are demonstrated for covariance (1)H-(1)H TOCSY and (13)C-(1)H HSQC-TOCSY spectra and triple-rank correlation spectra constructed from pairs of (13)C-(1)H HSQC and (13)C-(1)H HSQC-TOCSY spectra. All methods are first demonstrated for an eight-compound metabolite model mixture before being applied to an extract from E. coli cell lysate.     

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.