Multiple solenoidal microcoil probes for high-sensitivity, high-throughput nuclear magnetic resonance spectroscopy

Two designs for incorporating multiple solenoidal microcoils into a single probe head are presented to increase the throughput of high-resolution NMR. Through a combination of radio frequency switches and low-noise amplifiers, multiple NMR spectra can be acquired in the same time as a single spectrum from a conventional probe consisting of one coil. Since this method does not compromise sensitivity with regard to the individual microcoils, throughput increases linearly with the number of coils. Only one receiver is needed, and data acquisition parameters can be optimized for each sample. Specifically, a four-coil system has been implemented for proton NMR at 250 MHz using a wide-bore magnet, with an observe volume of 28 nL for each microcoil. Signal cross-contamination was approximately 0.2% between individual coils, and simultaneous one- and two-dimensional spectra have been obtained from samples of fructose, galactose, adenosine triphosphate, and chloroquine (7 nmol of each compound). A more compact two-coil configuration has also been designed for operation at 500 MHz, with observe volumes of 5 and 31 nL for the two coils. One- and two-dimensional spectra were acquired from samples of 1-butanol (55 nmol) and ethylbenzene (250 nmol).     

Development of ultrahigh-throughput NMR spectroscopic analysis utilizing capillary flow NMR technology

An ultrahigh-throughput method for acquiring 1H NMR spectra is described. By constructing a continuous flow system utilizing an HPLC pump, autosampler, and a capillary flow NMR probe, it was possible to inject samples into the NMR spectrometer every 30 s using a continuous flow rate of 30 microL/min. 1H NMR spectroscopic data were acquired continuously into a pseudo-2D data file, with a 96-well-plate completed in <50 min. Spectra in continuous flow mode were readily obtained from approximately 3.4 mug (500 MHz), while the LOD was <850 ng. There was found to be little variation in either sample broadening within the flow system or signal intensities between multiple injections. This system offers several advantages over more conventional NMR spectroscopic analyses, notably the limited solvent required, high sensitivity, high speed, and improved spectral quality as a result of reduced spectral "dead" regions resulting from residual solvent levels.     

Heteronuclear 19F-1H statistical total correlation spectroscopy as a tool in drug metabolism: study of flucloxacillin biotransformation

We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional 19F/1H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz 1H and 753 MHz 19F{1H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1D spectral resolution, generating 1H-1H STOCSY plots, and novel 19F-1H HET-STOCSY, 19F-19F STOCSY, and 19F-edited 1H-1H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of approximately 0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., 13C, 15N, 31P) label present.     

"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.     

Analysis of metabolomic data using support vector machines

Metabolomics is an emerging field providing insight into physiological processes. It is an effective tool to investigate disease diagnosis or conduct toxicological studies by observing changes in metabolite concentrations in various biofluids. Multivariate statistical analysis is generally employed with nuclear magnetic resonance (NMR) or mass spectrometry (MS) data to determine differences between groups (for instance diseased vs healthy). Characteristic predictive models may be built based on a set of training data, and these models are subsequently used to predict whether new test data falls under a specific class. In this study, metabolomic data is obtained by doing a (1)H NMR spectroscopy on urine samples obtained from healthy subjects (male and female) and patients suffering from Streptococcus pneumoniae. We compare the performance of traditional PLS-DA multivariate analysis to support vector machines (SVMs), a technique widely used in genome studies on two case studies: (1) a case where nearly complete distinction may be seen (healthy versus pneumonia) and (2) a case where distinction is more ambiguous (male versus female). We show that SVMs are superior to PLS-DA in both cases in terms of predictive accuracy with the least number of features. With fewer number of features, SVMs are able to give better predictive model when compared to that of PLS-DA.     

Measurement of dilute 29Si species in solution using a large volume coil and DEFT NMR

A large-sample-volume nuclear magnetic resonance (NMR) spectroscopy probehead has been developed for the detection and characterization of low concentrations of 29Si species in aqueous solution. The approach described entails the use of a large-diameter radio frequency solenoid coil that permits substantially larger sample volumes to be investigated at moderate magnetic field strengths, compared with conventional NMR probehead configurations. In addition, difficulties presented by long 29Si T1 relaxation times have been circumvented by using the DEFT NMR pulse sequence, which permits more rapid signal averaging. Through a combination of these hardware and methodological improvements, high-resolution 29Si NMR spectra have been obtained at 4.2 T (29Si resonance frequency = 36.8 MHz) for an 800 microM solution of 96% 29Si-enriched silicic acid, H4SiO4 (pH approximately 8), with a signal-to-noise ratio of 16 and a line width of 31 Hz after 3 h of total measurement time.     

Sample classification based on Bayesian spectral decomposition of metabonomic NMR data sets

1H NMR spectra of biofluids provides a wealth of biochemical information on the metabolic status of an organism. Through the application of pattern recognition and classification algorithms, the data have been shown to provide information on disease diagnosis and the beneficial and adverse effects of potential therapeutics. Here, a novel approach is described for identifying subsets of spectral patterns in databases of NMR spectra, and it is shown that the intensities of these spectral patterns can be related to the onset and recovery from a toxic lesion in both a time-related and dose-related fashion. These patterns form a new type of combination biomarker for the biological effect under study. The approach is illustrated with a study of liver toxicity in rats using NMR spectra of urine following administration of a model hepatotoxin hydrazine.     

Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies

Statistical heterospectroscopy (SHY) is a new statistical paradigm for the coanalysis of multispectroscopic data sets acquired on multiple samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecules measured by different techniques across cohorts of samples. The potential of SHY is illustrated using both 600-MHz 1H NMR and UPLC-TOFMS data obtained from control rat urine samples (n = 54) and from a corresponding hydrazine-treated group (n = 58). We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/z data from MS, is readily achievable for a variety of metabolites, which leads to improved efficiency of molecular biomarker identification. In addition to structure, higher level biological information can be obtained on metabolic pathway activity and connectivities by examination of different levels of the NMR to MS correlation and anticorrelation matrixes. The SHY approach is of general applicability to complex mixture analysis, if two or more independent spectroscopic data sets are available for any sample cohort. Biological applications of SHY as demonstrated here show promise as a new systems biology tool for biomarker recovery.     

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.     

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.     

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.     

Diels-alder adducts of 3,6-dibromophencyclone with short-chain N-n-alkylmaleimides: 1H and 13C nuclear magnetic resonance studies of hindered rotations and magnetic anisotropy, and ab initio calculations for optimized structures

3,6-Dibromophencyclone, 2, reacted with N-ethylmaleimide, 3a; N-n-propylmaleimide, 3b; and N-n-butylmaleimide, 3c; to form the corresponding Diels-Alder adducts, 4a, 4b, and 4c. The nuclear magnetic resonance (NMR) spectra of the adducts were studied at ambient temperatures at 300 MHz for proton and 75 MHz for carbon-13. Full proton assignments were achieved by high-resolution COSY45 spectra for the aryl proton regions. Rigorous assignments for protonated carbons were obtained with the heteronuclear chemical shift correlation spectra (HETCOR). Slow exchange limit (SEL) spectra were observed for both proton and carbon-13 NMR for each adduct, with slow rotation on the NMR timescales for the unsubstituted bridgehead phenyl groups. Endo Diels-Alder adduct stereochemistry was supported by substantial magnetic anisotropic shielding effects in the 1H NMR spectra of the alkyl groups. Proton NMR shifts are compared with those previously reported for the corresponding adducts, 5b and 5c, obtained from 3b and 3c, respectively, with the parent compound, phencyclone, 1. Results of ab initio molecular modeling calculations at the Hartree-Fock level using the LACVP* basis set for conformers of the dibrominated adducts, 4a-4c, are presented, together with HF/6-31G* results for the non-brominated adducts, 5a, 5b, and 5c. Novel aspects of this present work include: (a) attempts to quantitatively evaluate alkyl proton NMR shielding magnitudes in the adducts, relative to maleimide precursors, and (b) use of ab initio Hartree-Fock level calculations to try to reconcile adduct geometries with the observed shielding magnitudes. Our results here complement and extend studies of: (a) adducts of the parent phencyclone with straight-chain N-n-alkylmaleimides, and (b) adducts of 3,6-dibromophencyclone with other symmetrical dienophiles.     

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.     

Raman spectroscopy-based metabolomics for differentiating exposures to triazole fungicides using rat urine

Normal Raman spectroscopy was evaluated as a metabolomic tool for assessing the impacts of exposure to environmental contaminants, using rat urine collected during the course of a toxicological study. Specifically, one of three triazole fungicides, myclobutanil, propiconazole, or triadimefon, was administered daily via oral gavage to male Sprague-Dawley rats at doses of 300, 300, or 175 mg/kg, respectively. Urine was collected from all three treatment groups and also from vehicle control rats on day six, following five consecutive days of exposure. Spectra were acquired with a CCD-based dispersive Raman spectrometer, using 785-nm diode laser excitation. To optimize the signal-to-noise ratio, urine samples were filtered through a stirred ultrafiltration cell with a 500 nominal molecular weight limit filter to remove large, unwanted urine components that can degrade the spectrum via fluorescence. However, a subsequent investigation suggested that suitable spectra can be obtained in a high-throughput fashion, with little or no Raman-specific sample preparation. For the sake of comparison, a parallel 1H NMR-based metabolomic analysis was also conducted on the unfiltered samples. Results from multivariate data analysis demonstrated that the Raman method compares favorably with NMR in regard to the ability to differentiate responses from these three contaminants.     

Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics

For the analysis of the spectra of complex biofluids, preprocessing methods play a crucial role in rendering the subsequent data analyses more robust and accurate. Normalization is a preprocessing method, which accounts for different dilutions of samples by scaling the spectra to the same virtual overall concentration. In the field of 1H NMR metabonomics integral normalization, which scales spectra to the same total integral, is the de facto standard. In this work, it is shown that integral normalization is a suboptimal method for normalizing spectra from metabonomic studies. Especially strong metabonomic changes, evident as massive amounts of single metabolites in samples, significantly hamper the integral normalization resulting in incorrectly scaled spectra. The probabilistic quotient normalization is introduced in this work. This method is based on the calculation of a most probable dilution factor by looking at the distribution of the quotients of the amplitudes of a test spectrum by those of a reference spectrum. Simulated spectra, spectra of urine samples from a metabonomic study with cyclosporin-A as the active compound, and spectra of more than 4000 samples of control animals demonstrate that the probabilistic quotient normalization is by far more robust and more accurate than the widespread integral normalization and vector length normalization.     

Indirect Susceptibility Mapping of Thin-Layer Samples Using Nuclear Magnetic Resonance Imaging

We measured and imaged magnetic field distributions of thin layers (2-D objects with negligible thickness) of biological and physical samples, by using nuclear magnetic resonance (NMR). The image represents the magnetic susceptibility distribution in the sample. We used a standard gradient echo imaging method, susceptible to magnetic field homogeneity, for detection. Since the physical and biological samples we investigated do not generate any NMR signal, we used a homogeneous phantom reference - a container filled with water - as a medium. The image acquired by this method is actually a projection of the sample properties onto the homogeneous phantom. The method can be applied in nanotechnology, microelectronics, and especially in the biological and medical sciences.     

Nanoliter-volume 1H NMR detection using periodic stopped-flow capillary electrophoresis.

Recent advances in the analysis of nanoliter volumes using 1H NMR microcoils have led to the application of microcoils as detectors for capillary electrophoresis (CE). Custom NMR probes consisting of 1-mm-long solenoidal microcoils are fabricated from 50-micron diameter wire wrapped around capillaries to create nanoliter-volume detection cells. For geometries in which the capillary and static magnetic field are not parallel, the electrophoretic current induces a magnetic field gradient which degrades the spectroscopic information obtainable from CE/NMR. To reduce this effect and allow longer analyte observation times, the electrophoretic voltage is periodically interrupted so that 1-min high-resolution NMR spectra are obtained for every 15 s of applied voltage. The limits of detection (LODs; based on S/N = 3) for CE/NMR for arginine are 57 ng (330 pmol; 31 mM) and for triethylamine (TEA) are 9 ng (88 pmol; 11 mM). Field-amplified stacking is used for sample preconcentration. As one example, a 290-nL injection of a mixture of arginine and TEA both at 50 mM (15 nmol of each injected) is stacked severalfold for improved concentration LODs while achieving a separation efficiency greater than 50,000. Dissolving a sample in a mixture of 10% H2O/90% D2O allows H2O to serve as the nearly ideal neutral tracer and allows direct observation of the parabolic and flat flow profiles associated with gravimetric and electrokinetic injection, respectively. The unique capabilities of CE and the rich spectral information provided by NMR spectroscopy combine to yield a valuable analytical tool, especially in the study of mass-limited samples.     

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.     

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.