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.     

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.     

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.     

Kinetic and J-resolved statistical total correlation NMR spectroscopy approaches to structural information recovery in complex reacting mixtures: application to acyl glucuronide intramolecular transacylation reactions

We demonstrate here a new variant on a statistical spectroscopic method for recovering structural information on unstable intermediates formed in reaction mixtures. We exemplify this approach with respect to the internal acyl migration reactions of 1-beta-O-acyl glucuronides (AGs), which rearrange at neutral or slightly alkaline pH on a minute to hour time scale to yield a series of positional glucuronide ring isomers and alpha/beta anomers from the 1-beta (starting material), i.e. 2-beta, 2-alpha, 1-alpha, 3-beta, 3-alpha, and 4-beta, 4-alpha isomers together with the aglycon and alpha- and beta-glucuronic acid hydrolysis products. Multiple sequential 800 MHz cryoprobe (1)H NMR spectra (1D and 2D J-resolved, JRES) were collected on a 5.1 mM solution of a synthetic model drug glucuronide, 1-beta-O-acyl (S)-alpha-methyl phenylacetyl glucuronide (MPG) in 0.1 M sodium phosphate buffer in D2O at pD 7.4 over 18 h to monitor the reaction which leads to the formation of the eight positional isomers and hydrolysis products. As the reaction proceeds and new isomers form, the NMR signal intensities vary accordingly allowing the application of a novel kinetic variant on statistical total correlation spectroscopy (K-STOCSY) method to recover the connectivities between proton signals on the same reacting molecule based on their intensity covariance through time. We performed K-STOCSY analysis on both the standard 1D NMR spectra and the skyline projected singlets of the (1)H-(1)H JRES NMR spectra through time, i.e. the K-JRES-STOCSY experimental variant, which increases the effective spectral dispersion and is ideally suited for the analysis of heavily overlapped spin systems. High statistical correlations were observed between mutarotated alpha- and beta-anomers of individual positional isomers, as well as directly acyl migrated products and anticorrelation observed between signals from compounds that were being depleted as others increased, e.g. between the 1-beta and 2-alpha/2-beta isomers. This statistical kinetic approach enabled the recovery of structural connectivity information on all isomers allowing unequivocal resonance assignment, and this approach to spectroscopic information recovery has wider potential uses in the study of reactions that occur on the second-to-minute time scale in conditions where multiple sequential NMR spectra can be collected. JRES-STOCSY is also of potential use as a method for recovering spectroscopic information in highly overlapped NMR signals and spin systems in other types of complex mixture analysis.     

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.     

Metabonomic study of host-phage interaction by nuclear magnetic resonance- and statistical total correlation spectroscopy-based analysis

We present a method for the qualitative and quantitative study of transient metabolic flux of phage infection at the molecular level. The method is based on statistical total correlation spectroscopy (STOCSY) and partial least squares discriminant analysis (PLS-DA) applied to nuclear magnetic resonance (NMR) metabonomic data sets. An algorithm for this type of study is developed and demonstrated. The method has been implemented on (1)H NMR data sets of growth media in planktonic cultures of Pseudomonas aeruginosa infected with bacteriophage pf1. Transient metabolic flux of various important metabolites, identified by STOCSY and PLS-DA analysis applied to the NMR data set, are estimated at various stages of growth. The opportunistic and nosocomial pathogen P. aeruginosa is one of the best-studied model organism for bacterial biofilms. Complete information regarding metabolic connectivity of this system is not possible by conventional spectroscopic approach. Our study presents temporal comparative (1)H NMR metabonomic analyses of filamentous phage pf1 infection in planktonic cultures of P. aeruginosa K strain (PAK). We exemplify here the potential of STOCSY and PLS-DA tools to gain mechanistic insight into subtle changes and to determine the transient flux associated with metabolites following metabolic perturbations resulting from phage infection. Our study has given new avenues in correlating existing postgenomic data with current metabonomic results in P. aeruginosa biofilms research.     

Optimization of human plasma 1H NMR spectroscopic data processing for high-throughput metabolic phenotyping studies and detection of insulin resistance related to type 2 diabetes

Optimizing NMR experimental parameters for high-throughput metabolic phenotyping requires careful examination of the total biochemical information obtainable from (1)H NMR data, which includes concentration and molecular dynamics information. Here we have applied two different types of mathematical transformation (calculation of the first derivative of the NMR spectrum and Gaussian shaping of the free-induction decay) to attenuate broad spectral features from macromolecules and enhance the signals of small molecules. By application of chemometric methods such as principal component analysis (PCA), orthogonal projections to latent structures discriminant analysis (O-PLS-DA) and statistical spectroscopic tools such as statistical total correlation spectroscopy (STOCSY), we show that these methods successfully identify the same potential biomarkers as spin-echo (1)H NMR spectra in which broad lines are suppressed via T2 relaxation editing. Finally, we applied these methods for identification of the metabolic phenotype of patients with type 2 diabetes. This "virtual" relaxation-edited spectroscopy (RESY) approach can be particularly useful for high-throughput screening of complex mixtures such as human plasma and may be useful for extraction of latent biochemical information from legacy or archived NMR data sets for which only standard 1D data sets exist.     

Statistical total correlation spectroscopy scaling for enhancement of metabolic information recovery in biological NMR spectra

The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical total correlation spectroscopy (STOCSY) can be used to edit NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this "STOCSY editing" concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSY(S)). STOCSY(S) exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (n = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (n = 67). In both cases significant biomarker discovery improvement was observed by using STOCSY(S): the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSY(S) to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSY(S) is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or important features. This approach is generally appropriate for many types of NMR-based complex mixture analyses and hence for wider applications in bioanalytical science.     

Data-driven approach for metabolite relationship recovery in biological 1H NMR data sets using iterative statistical total correlation spectroscopy

Statistical total correlation spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite correlations in NMR metabonomic data sets. Here, the STOCSY approach is extended in a novel Iterative-STOCSY (I-STOCSY) tool in which correlations are calculated initially from a driver peak of interest and subsequently for all peaks identified as correlating with a correlation coefficient greater than a set threshold. Consequently, in a single automated run, the majority of information contained in multiple STOCSY calculations from all peaks recursively correlated to the original user defined driver peak of interest are recovered. In addition, highly correlating peaks are clustered into putative structurally related sets, and the results are presented in a fully interactive plot where each set is represented by a node; node-to-node connections are plotted alongside corresponding spectral data colored by the strength of connection, thus allowing the intuitive exploration of both inter- and intrametabolite connections. The I-STOCSY approach has been here applied to a (1)H NMR data set of 24 h postdose aqueous liver extracts from rats treated with the model hepatotoxin galactosamine and has been shown both to recover the previously deduced major metabolic effects of treatment and to generate new hypotheses even on this well-studied model system. I-STOCSY, thus, represents a significant advance in correlation based analysis and visualization, providing insight into inter- and intrametabolite relationships following metabolic perturbations.     

Two-dimensional fluorescence correlation in capillary electrophoresis for peak resolution and species identification

A new spectroscopic dimension-fluorescence intensity correlation--is introduced to enhance peak resolution and species identification in capillary electrophoresis. In two-dimensional correlation CE, a conventional electropherogram is spread into two dimensions through cross-correlation analysis of fluorescence time response. A laser that is sinusoidally modulated in intensity is used as the excitation source. Three channels of information are collected during a CE run: the steady-state intensity, the ac amplitude, and the phase-resolved fluorescence intensity. The correlation between two chosen channels is then evaluated. A two-dimensional correlation electropherogram consists of a plot of the correlation intensity versus two axes of migration time. Through correlation analysis, species discrimination and peak resolution are significantly enhanced without having to physically separate the solutes. Two-dimensional correlation CE showed complete resolution between two overlapping sample peaks with a resolution of 0.28 in the conventional one-dimensional electropherogram. In separations of polycyclic aromatic hydrocarbons by micellar electrokinetic chromatography (MEKC), two-dimensional correlation analysis resolved all overlapping elution peaks unseparable by one-dimensional MEKC, demonstrating the utility of 2D correlation in separation method development. The capability of 2D correlation CE in species identification is demonstrated with a sequence of 39 consecutively injected peaks containing four fluorescent dyes. Species identification in sequencing is achieved without complex data treatment in two-dimensional correlation CE.     

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.     

苯乙烯乙烯/丁烯嵌段共聚物的二维核磁方法

利用二维核磁方法对苯乙烯-乙烯/丁烯嵌段共聚物的链段微结构进行了定性。利用普通一维1H和13C核磁得到了共聚物分子的基本结构信息;利用1H-1H-COSY二维核磁确定了共聚物中同核氢质子之间的偶合关系;利用13C-1H-HSQC和13C-1H-HMBC二维核磁确定了共聚物异核碳氢原子的单键相关性,明确了直接相连接的C-H化学位移,为微结构的确定提供了依据。归属了苯乙烯嵌段结构中的亚甲基和次甲基的化学位移;同时归属了乙烯-丁烯嵌段链段结构中的甲基、亚甲基和次甲基的化学位移,并确定了重复单元的链段结构状态。二维核磁方法可以得到很多在普通一维核磁中难以解析的信息。二维核磁是对苯乙烯-乙烯/丁烯嵌段共聚物链结构定性的有效手段。     

Multivariate curve resolution applied to the analysis and resolution of two-dimensional [1H,15N] NMR reaction spectra

Multivariate curve resolution is proposed for the study of complex chemical reactions monitored by two-dimensional (2D) NMR spectroscopy. In particular, in this work, multivariate curve resolution is applied to the study of the reaction between (15)N-labeled cisplatin and the amino acid-nucleotide hybrid (Phac-Met-linker-p(5')dG). At several stages of the reaction, 2D [(1)H,(15)N] HSQC NMR spectra were acquired and stored in data matrices. In a first step, multivariate curve resolution was applied to analyze individually each one of these 2D spectra, allowing the resolution of the corresponding (1)H and (15)N one-dimensional correlation spectra. In a second step, the whole set of 2D spectra recorded along the reaction were simultaneously analyzed by multivariate curve resolution, allowing the resolution of the kinetic concentration profiles and of the pure 2D NMR spectra of each of the species detected along the reaction. Results finally obtained confirmed previously postulated reaction mechanisms involving the existence of two monofunctional adducts and of two bifunctional adducts, with the structure of one of them not completely resolved.     

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.     

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.     

Trace level detection of compounds related to the chemical weapons convention by 1H-detected 13C NMR spectroscopy executed with a sensitivity-enhanced, cryogenic probehead

Two-dimensional 1H-13C HSQC (heteronuclear single quantum correlation) and fast-HMQC (heteronuclear multiple quantum correlation) pulse sequences were implemented using a sensitivity-enhanced, cryogenic probehead for detecting compounds relevant to the Chemical Weapons Convention present in complex mixtures. The resulting methods demonstrated exceptional sensitivity for detecting the analytes at trace level concentrations. 1H-13C correlations of target analytes at < or = 25 microg/mL were easily detected in a sample where the 1H solvent signal was approximately 58,000-fold more intense than the analyte 1H signals. The problem of overlapping signals typically observed in conventional 1H spectroscopy was essentially eliminated, while 1H and 13C chemical shift information could be derived quickly and simultaneously from the resulting spectra. The fast-HMQC pulse sequences generated magnitude mode spectra suitable for detailed analysis in approximately 4.5 h and can be used in experiments to efficiently screen a large number of samples. The HSQC pulse sequences, on the other hand, required roughly twice the data acquisition time to produce suitable spectra. These spectra, however, were phase-sensitive, contained considerably more resolution in both dimensions, and proved to be superior for detecting analyte 1H-13C correlations. Furthermore, a HSQC spectrum collected with a multiplicity-edited pulse sequence provided additional structural information valuable for identifying target analytes. The HSQC pulse sequences are ideal for collecting high-quality data sets with overnight acquisitions and logically follow the use of fast-HMQC pulse sequences to rapidly screen samples for potential target analytes. Use of the pulse sequences considerably improves the performance of NMR spectroscopy as a complimentary technique for the screening, identification, and validation of chemical warfare agents and other small-molecule analytes present in complex mixtures and environmental samples.     

Binder distribution in Si3N4 ceramic green bodies studied by stray-field NMR imaging

Nuclear magnetic resonance (NMR) imaging is an emerging technology which provides a unique material-diagnostic technique by in situ internal mapping. It can provide information not only on material distribution, but also on the chemical and physical characteristics of materials. However, due to the nuclear dipole dipole interaction in solid state materials, NMR spectroscopic signals are normally very broad. NMR imaging based on these unresolved broad lines is extremely difficult, and resolution is poor. The binder distribution was studied in ceramic green bodies with a stray-field NMR imaging facility at a proton frequency of 163 MHz near the edge of a 9.394 T superconducting magnet. The ¹H nuclear spin echo signal from silicon nitride green bodies containing 10 wt% of either polyethylene glycol or polyvinyl alcohol as a binder was detected at 163 MHz. NMR images show a good homogeneity of the binder distribution in the cross-sections of the samples. Overall results show that the distribution of polyethylene glycol in Si₃N₄ green bodies is more homogeneous than that of polyvinyl alcohol under similar processing parameters. NMR spectroscopic results also indicate a higher moisture content in the green bodies containing a polyvinyl alcohol binder.