Advancing NMR sensitivity for LC-NMR-MS using a cryoflow probe: application to the analysis of acetaminophen metabolites in urine

Cryogenic cooling of the NMR radio frequency coils and electronics to give greatly enhanced sensitivity is arguably the most significant recent advance in NMR spectroscopy. Here we report the first cryogenic probe built in flow configuration and demonstrate the application to LC-NMR-MS studies. This probe provides superior sensitivity over conventional noncryogenic flow NMR probes, allowing the use of 100 microL of untreated urine (40% less material than previous studies that required preconcentration) and yet revealing drug metabolites hitherto undetected by LC-NMR-MS at 500 MHz. Besides the known sulfate and glucuronide metabolites, previously undetected metabolites of acetaminophen were directly observable in a 15-min on-flow experiment. Simultaneous MS data also provided knowledge on the NMR-silent functional moieties. Further, stop-flow LC-NMR-MS experiments were conducted for greater signal-to-noise ratios on minor metabolites. The cryoflow probe enables the NMR analysis of lower concentrations of metabolites than was previously possible for untreated biofluids. This strategy is generally applicable for samples containing mass-limited analytes, such as those from drug metabolism studies, biomarker and toxicity profiling, impurity analysis, and natural product analysis.     

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.     

"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.     

Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles

Metabolomics may have the capacity to revolutionize disease diagnosis through the identification of scores of metabolites that vary during environmental, pathogenic, or toxicological insult. NMR spectroscopy has become one of the main tools for measuring these changes since an NMR spectrum can accurately identify metabolites and their concentrations. The predominant approach in analyzing NMR data has been through the technique of spectral binning. However, identification of spectral areas in an NMR spectrum is insufficient for diagnostic evaluation, since it is unknown whether areas of interest are strictly caused by metabolic changes or are simply artifacts. In this paper, we explore differences in gender, diurnal variation, and age in a human population. We use the example of gender differences to compare traditional spectral binning techniques (NMR spectral areas) to novel targeted profiling techniques (metabolites and their concentrations). We show that targeted profiling produces robust models, generates accurate metabolite concentration data, and provides data that can be used to help understand metabolic differences in a healthy population. Metabolites relating to mitochondrial energy metabolism were found to differentiate gender and age. Dietary components and some metabolites related to circadian rhythms were found to differentiate time of day urine collection. The mechanisms by which these differences arise will be key to the discovery of new diagnostic tests and new understandings of the mechanism of disease.     

High-resolution capillary tube NMR. A miniaturized 5-microL high-sensitivity TXI probe for mass-limited samples,off-line LC NMR,and HT NMR

A new triple-resonance (TXI) (1H, 13C, 15N) high-resolution nuclear magnetic resonance (NMR) capillary probe with 2.5-microL NMR-active sample volume (V(obs)) was built and tested for applications with mass- and volume-limited samples and for coupling of microbore liquid chromatography to NMR. This is the first microliter probe with optimized coil geometry for use with individual capillary tubes with an outer diameter of 1 mm. The 90 degree pulse lengths of the 1-mm microliter probe were below 2 micros for proton, below 8 micros for carbon, and below 20 micros for nitrogen, and a spectral line width at signal half-height below 1 Hz was obtained. Compared to a conventional 5-mm probe, the new 600-MHz 1-mm TXI microliter probe with z-gradient shows an increase in mass sensitivity by a factor of 5, corresponding to a 25-fold reduction in measuring time. The consumption of costly deuterated solvent is reduced by at least 2 orders of magnitude. The 1-mm TXI microliter probe with z-gradient allows the measurement of one-dimensional 1H NMR and two-dimensional heteronuclear NMR spectra with a few nanomoles (micrograms) of compound with high sensitivity, speed, and quality. This is a breakthrough for discrete sample NMR spectroscopy with paramount importance for structure elucidation in natural compound chemistry and metabolic research. It offers also advantages for linking chromatographic methods to NMR in a nindustrial environment. Capillary tube NMR may find new applications in areas where high sample throughput is essential, e.g., in the quality control of large sample arrays from parallel chemistry, screening, and compound depositories. It has the potential to increase the sample throughput by 1 order of magnitude or more if new hardware for fast sample handling and exchange becomes available.     

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.     

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.     

NMR detection with multiple solenoidal microcoils for continuous-flow capillary electrophoresis

Nuclear magnetic resonance (NMR) spectroscopy represents a promising on-line detector for capillary electrophoresis (CE). The inherent poor sensitivity of NMR mandates the use of NMR probes with the highest mass sensitivity, such as those containing solenoidal microcoils, for CE/NMR hyphenation. However, electrophoretic current degrades the resolution of NMR spectra obtained from solenoidal coils. A new method to avoid microcoil NMR spectral degradation during continuous-flow CE is demonstrated using a unique multiple solenoidal coil NMR probe. The electrophoretic flow from a single separation capillary is split into multiple outlets, each possessing its own NMR detection coil. While the CE electrophoretic flow is directed through one outlet, stopped-flow, high-resolution NMR spectra are obtained from the coil at the other outlet. The electrophoretic flow and NMR measurements are cycled between the outlets to allow a continuous CE separation with "stopped-flow" detection. As a new approach for improving multiple coil probe performance, the magnetic field homogeneity is automatically adjusted (via the shim coils of the magnet) for the active coil. The multiple microcoil CE/NMR coupling has been used to analyze a <3 nmole mixture of amines while obtaining between 1 and 2 Hz line width, demonstrating the ability to avoid electrophoretic current-induced line broadening.     

Micromixer-based time-resolved NMR: applications to ubiquitin protein conformation

Time-resolved NMR spectroscopy is used to studychanges in protein conformation based on the elapsed time after a change in the solvent composition of a protein solution. The use of a micromixer and a continuous-flow method is described where the contents of two capillary flows are mixed rapidly, and then the NMR spectra of the combined flow are recorded at precise time points. The distance after mixing the two fluids and flow rates define the solvent-protein interaction time; this method allows the measurement of NMR spectra at precise mixing time points independent of spectral acquisition time. Integration of a micromixer and a microcoil NMR probe enables low-microliter volumes to be used without losing significant sensitivity in the NMR measurement. Ubiquitin, the model compound, changes its conformation from native to A-state at low pH and in 40% or higher methanol/water solvents. Proton NMR resonances of the His-68 and the Tyr-59 of ubiquitin are used to probe the conformational changes. Mixing ubiquitin and methanol solutions under low pH at microliter per minute flow rates yields both native and A-states. As the flow rate decreases, yielding longer reaction times, the population of the A-state increases. The micromixer-NMR system can probe reaction kinetics on a time scale of seconds.     

Affinity NMR.

Because binding ligands are directly detected and identified, the diffusion-based NMR method and NOE pumping approach promise to greatly simplify deconvolution in drug screening. An additional advantage of these techniques is that low-affinity ligands, which might be missed by high-throughput screening, can be detected and could serve as synthetic precursors for higher affinity ligands. The biggest challenge to NMR methodology lies in its sensitivity. Compared with other techniques, such as MS (25), NMR methods for screening mixtures are limited by their relative insensitivity. Because of issues such as solubility, stability, and mass limitation, it is not in general judicious to simply increase the concentration of the mixture. Improvements in hardware and software are necessary to extend the applicability of the affinity NMR method to the screening of larger and more complex mixtures. A boost in sensitivity and screening capacity of NMR technique is possible by the implementation of microcoil (26) and flow probe techniques. An upsurge in the capabilities of mixture analysis could be achieved with a combination of independent and complementary techniques (e.g., HPLC, MS) (27). As a unique, nondestructive, and versatile tool, NMR will continue on its fast track of development in the support of drug discovery.     

NMR analysis on microfluidic devices by remote detection

We present a novel approach to perform high-sensitivity NMR imaging and spectroscopic analysis on microfluidic devices. The application of NMR, the most information-rich spectroscopic technique, to microfluidic devices remains a challenge because the inherently low sensitivity of NMR is aggravated by small fluid volumes leading to low NMR signal and geometric constraints resulting in poor efficiency for inductive detection. We address the latter by physically separating signal detection from encoding of information with remote detection. Thereby, we use a commercial imaging probe with sufficiently large diameter to encompass the entire device, enabling encoding of NMR information at any location on the chip. Because large-diameter coils are too insensitive for detection, we store the encoded information as longitudinal magnetization and flow it into the outlet capillary. There, we detect the signal with optimal sensitivity, using a solenoidal microcoil, and reconstruct the information encoded in the fluid. We present a generally applicable design for a detection-only microcoil probe that can be inserted into the bore of a commercial imaging probe. Using hyperpolarized 129Xe gas, we show that this probe enables sensitive reconstruction of NMR spectroscopic information encoded by the large imaging probe while keeping the flexibility of a large coil.     

A chromatographic approach for fluorescence reduction in liquid Raman analysis

In this study a chromatographic approach for fluorescence reduction in liquid Raman analysis has been evaluated. The idea behind the approach is to apply a chromatographic separation step prior to Raman analysis in order to separate fluorescing compounds from other components of interest, thus facilitating better quantitative and qualitative analysis of the latter components. A real-time liquid-core Raman waveguide detector designed for chromatographic applications was used in the study, thus providing real-time chemical pretreatment of liquid samples for Raman analysis. Twenty aqueous mixtures of additives frequently found in beverages were analyzed, and for comparative purposes the mixtures were also analyzed in the Raman waveguide detector without chromatographic separation and with a conventional immersion probe. Both qualitatively and quantitatively satisfying results were obtained using the chromatographic Raman approach, and the technique provided possibilities for quantitative and qualitative assessments superior to the two other instrumental setups. The technique may provide additional benefits through sensitivity enhancements, and the approach is simple, inexpensive, and easy to implement in the average applied Raman laboratory. The analysis of various chemical systems and factors such as system stability over time need further evaluation in order to confirm the general applicability of the approach.     

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.     

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.     

Metabolite identification using automated comparison of high-resolution multistage mass spectral trees

Multistage mass spectrometry (MS(n)) generating so-called spectral trees is a powerful tool in the annotation and structural elucidation of metabolites and is increasingly used in the area of accurate mass LC/MS-based metabolomics to identify unknown, but biologically relevant, compounds. As a consequence, there is a growing need for computational tools specifically designed for the processing and interpretation of MS(n) data. Here, we present a novel approach to represent and calculate the similarity between high-resolution mass spectral fragmentation trees. This approach can be used to query multiple-stage mass spectra in MS spectral libraries. Additionally the method can be used to calculate structure-spectrum correlations and potentially deduce substructures from spectra of unknown compounds. The approach was tested using two different spectral libraries composed of either human or plant metabolites which currently contain 872 MS(n) spectra acquired from 549 metabolites using Orbitrap FTMS(n). For validation purposes, for 282 of these 549 metabolites, 765 additional replicate MS(n) spectra acquired with the same instrument were used. Both the dereplication and de novo identification functionalities of the comparison approach are discussed. This novel MS(n) spectral processing and comparison approach increases the probability to assign the correct identity to an experimentally obtained fragmentation tree. Ultimately, this tool may pave the way for constructing and populating large MS(n) spectral libraries that can be used for searching and matching experimental MS(n) spectra for annotation and structural elucidation of unknown metabolites detected in untargeted metabolomics studies.     

NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm

As with every -omics technology, metabolomics requires new methodologies for data processing. Due to the large spectral size, a standard approach in NMR-based metabolomics implies the division of spectra into equally sized bins, thereby simplifying subsequent data analysis. Yet, disadvantages are the loss of information and the occurrence of artifacts caused by peak shifts. Here, a new binning algorithm, Adaptive Intelligent Binning (AI-Binning), which largely circumvents these problems, is presented. AI-Binning recursively identifies bin edges in existing bins, requires only minimal user input, and avoids the use of arbitrary parameters or reference spectra. The performance of AI-Binning is demonstrated using serum spectra from 40 hypertensive and 40 matched normotensive subjects from the Asklepios study. Hypertension is a major cardiovascular risk factor characterized by a complex biochemistry and, in most cases, an unknown origin. The binning algorithm resulted in an improved classification of hypertensive status compared with that of standard binning and facilitated the identification of relevant metabolites. Moreover, since the occurrence of noise variables is largely avoided, AI-Binned spectra can be unit-variance scaled. This enables the detection of relevant, low-intensity metabolites. These results demonstrate the power of AI-Binning and suggest the involvement of alpha-1 acid glycoproteins and choline biochemistry in hypertension.     

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.