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.     

Self-consistent metabolic mixture analysis by heteronuclear NMR. Application to a human cancer cell line

Elucidation of the chemical composition of biological samples is a main focus of systems biology and metabolomics. In order to comprehensively study these complex mixtures, reliable, efficient, and automatable methods are needed to identify and quantify the underlying metabolites and natural products. Because of its rich information content, nuclear magnetic resonance (NMR) spectroscopy has a unique potential for this task. Here we present a generalization of the recently introduced homonuclear TOCSY-based DemixC method to heteronuclear HSQC-TOCSY NMR spectroscopy. The approach takes advantage of the high resolution afforded along the (13)C dimension due to the narrow (13)C line widths for the identification of spin systems and compounds. The method combines information from both 1D (13)C and (1)H traces by querying them against an NMR spectral database using our COLMAR query web server. The complementarity of (13)C and (1)H spectral information improves the robustness of compound identification. The method is demonstrated for a metabolic model mixture and is then applied to an extract from DU145 human prostate cancer cells.     

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.     

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.     

Remote monitoring of multi-gas mixtures by passive standoff Fourier transform infrared radiometry

The passive remote monitoring of multi-gas mixtures was experimentally investigated using Fourier transform infrared (FT-IR) radiometry. The spectral radiance data were collected using a dual-port radiometrically balanced interferometer for a variety of multi-gas plumes at a standoff distance of 60 m. Two basic sets of mixtures were studied. The first set corresponded to mixtures consisting of three gases with no overlapping spectral bands (C(2)H(2), C(2)H(4), and R14). The second set corresponded to mixtures of three gases having significant spectral overlap (C(2)H(4), R114, and R134a). For each mixture the flow rates of individual constituents were adjusted to yield specific constituent optical-density (CL) ratios. These ratios were compared to the optical-density ratios retrieved from the measured infrared radiance spectra. Results of this study indicated that for both sets of multi-gas mixtures the optical-density ratios retrieved by the passive remote monitoring technique were in good agreement with those derived from the release flow rates, provided that a simple correction scheme was introduced to compensate for the limited accuracy of the fast radiance model implemented in the monitoring algorithm.     

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。     

Independent components in spectroscopic analysis of complex mixtures

We applied two methods of “blind” spectral decomposition (MILCA and SNICA) to quantitative and qualitative analyses of UV absorption spectra of several non-trivial mixture types. Both methods use the concept of statistical independence and aim at the reconstruction of minimally dependent components from a linear mixture. We examined mixtures of major ecotoxicants (aromatic and polyaromatic hydrocarbons), amino acids and complex mixtures of vitamins in a veterinary drug. Both MICLA and SNICA were able to recover concentrations and individual spectra with minimal errors comparable with instrumental noise. In most cases their performance was similar to or better than that of other chemometric methods such as MCR-ALS, SIMPLISMA, RADICAL, JADE and FastICA. These results suggest that the ICA methods used in this study are suitable for real life applications.     

Tailoring (1)H Spin Dynamics in Small Molecules via Supercooled Water: A Promising Approach for Metabolite Identification and Validation

Metabolic mixtures are often analyzed via NMR spectroscopy as it provides a metabolic profile without sample alteration in a noninvasive manner. These mixtures however tend to be very complex and demonstrate considerable spectral overlap resulting in assignments that are sometimes ambiguous given the range of current NMR methods available. De novo molecular identification in these mixtures is generally accomplished using chemical shift information and J-coupling based experiments to determine spin connectivity information, but these techniques fall short when a molecule of interest contains nonrelaying centers. A method is presented here that enhances intramolecular spatial interactions via supercooled water and uses the resulting spatial correlations to edit mixtures. This is accomplished by utilizing nuclear Overhauser effect spectroscopy (NOESY) at subzero temperatures in capillaries to enhance NOE and provide more complete spin systems. This technique is applied to a standard mixture of three known molecules in D(2)O with overlapping resonances and is further demonstrated to assign molecules in a worm tissue extract. The current method proves to be a powerful complement to existing methods such as total correlation spectroscopy (TOCSY) to expand the range of molecules that can be assigned in situ without physical separation of mixtures.     

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.     

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.     

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.     

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.     

Noise reduction methods applied to two-dimensional correlation spectroscopy (2D-COS) reveal complementary benefits of pre- and post-treatment

Two-dimensional correlation spectroscopy (2D-COS) is a powerful spectral analysis technique widely used in many fields of spectroscopy because it can reveal spectral information in complex systems that is not readily evident in the original spectral data alone. However, noise may severely distort the information and thus limit the technique's usefulness. Consequently, noise reduction is often performed before implementing 2D-COS. In general, this is implemented using one-dimensional (1D) methods applied to the individual input spectra, but, because 2D-COS is based on sets of successive spectra and produces 2D outputs, there is also scope for the utilization of 2D noise-reduction methods. Furthermore, 2D noise reduction can be applied either to the original set of spectra before performing 2D-COS ("pretreatment") or on the 2D-COS output ("post-treatment"). Very little work has been done on post-treatment; hence, the relative advantages of these two approaches are unclear. In this work we compare the noise-reduction performance on 2D-COS of pretreatment and post-treatment using 1D (wavelets) and 2D algorithms (wavelets, matrix maximum entropy). The 2D methods generally outperformed the 1D method in pretreatment noise reduction. 2D post-treatment in some cases was superior to pretreatment and, unexpectedly, also provided correlation coefficient maps that were similar to 2D correlation spectroscopy maps but with apparent better contrast.     

Analysis of 1H/2H exchange kinetics using model infrared spectra

This paper investigates the different approaches that best retrieve band shape parameters and kinetic time constants from series of protein Fourier transform infrared (FT-IR) spectra recorded in the course of 1H/2H exchange. In this first approach, synthetic spectra were used. It is shown that 1H/2H exchange kinetic measurements can help resolve spectral features otherwise hidden because of the overlap of various spectral contributions. We evaluated the efficiency of Fourier self-deconvolution, synchronous/asynchronous correlation, difference spectroscopy, principal component analysis, inverse Laplace transform, and determination of the underlying spectra by global analysis assuming first-order kinetics with either known or unknown time constants. It is demonstrated that some strategies allow the extraction of both the time dependence and the spectral shape of the underlying contributions.     

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.     

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.     

Study of complexation in methanol/water mixtures by infrared and Raman spectroscopy and multivariate curve resolution-alternating least-squares analysis

The structure of the mobile phase in liquid chromatography plays an important role in the determination of retention behavior on reversed-phase stationary materials. One of the most commonly employed mobile phases is a mixture of methanol and water. In this work, infrared and Raman spectroscopic methods were used to investigate the structure of species formed in methanol/water mixtures. Chemometric methods using multivariate curve resolution by alternating least-squares analysis were used to resolve the overlapped spectra and to determine concentration profiles as a function of composition. The results showed that the structure of these mixtures could be described by a mixture model consisting of four species, namely, methanol, water, and two complexes, methanol/water (1:1) and methanol/water (1:4). The spectral frequencies and concentration profiles found from the Raman and infrared measurements were consistent with one another and with theoretical calculations.     

Flow cytometric analysis to detect pathogens in bacterial cell mixtures using semiconductor quantum dots

Compared to a common green organic dye, semiconductor quantum dots (QDs) composed of CdSe/ZnS core/shell bioconjugates display brighter fluorescence intensities, lower detection thresholds, and better accuracy in analyzing bacterial cell mixtures composed of pathogenic E. coli O157:H7 and harmless E. coli DH5alpha using flow cytometry. For the same given bacterial mixture, QDs display fluorescence intensity levels that are approximately 1 order of magnitude brighter compared to the analogous experiments that utilize the standard dye fluorescein isothiocyanate. Detection limits are lowest when QDs are used as the fluorophore label for the pathogenic E. coli O157:H7 serotype: limits of 1% O157:H7 in 99% DH5alpha result, corresponding to 106 cells/mL, which is comparable to other developing fluorescence-based techniques for pathogen detection. Finally, utilizing QDs to label E. coli O157:H7 in cell mixtures results in greater accuracy and more closely approaches the ideal fluorophore for pathogen detection using flow cytometry. With their broader absorption spectra and narrower emission spectra than organic dyes, QDs can make vast improvements in the field of flow cytometry, where single-source excitation and simultaneous detection of multicolor species without complicating experimental setups or data analysis is quite advantageous for analyzing heterogeneous cell mixtures, both for prokaryotic pathogen detection and for studies on eukaryotic cell characteristics.     

Broadband D2 coherent anti-stokes Raman spectroscopy for single-shot pressure and temperature determination with a Fabry-Perot etalon

A method is demonstrated that employs a Fabry-Perot etalon to modulate a broadband coherent anti-Stokes Raman spectroscopy signal beam spatially to obtain enhanced resolution and spectral information for single-shot measurements of pressure and temperature. Resulting images are analyzed by; first, fits to Fabry-Perot patterns for single rovibrational lines; second, a line-shape analysis for a single rovibrational line; and third, a mapping of the Fabry-Perot channel spectra to a linear spectrum. Measurements of the D(2) Raman Q-branch lines were made for a D(2) in Ar mixture to take advantage of the large pressure shift and rovibrational line spacing. Peaks are located to better than 0.5% of the free spectral range of the etalon (approximately 0.01 cm(-1)) and a quantitative analysis of the pressure shifting and broadening is determined for the 1-10-MPa range. Finally, temperature and pressure determination using a band-fitting analysis is demonstrated.     

Development of 2D band-target entropy minimization and application to the deconvolution of multicomponent 2D nuclear magnetic resonance spectra

Spectral reconstruction from multicomponent spectroscopic data is the frequent primary goal in chemical system identification and exploratory chemometric studies. Various methods and techniques have been reported in the literature. However, few algorithms/methods have been devised for spectral recovery without the use of any a priori information. In the present studies, a higher dimensional entropy minimization method based on the BTEM algorithm (Widjaja, E.; Li, C.; Garland, M. Organometallics 2002, 21, 1991-1997.) and related techniques were extended to large-scale arrays, namely, 2D NMR spectroscopy. The performance of this novel method had been successfully verified on various real experimental mixture spectra from a series of randomized 2D NMR mixtures (COSY NMR and HSQC NMR). With the new algorithm and raw multicomponent NMR alone, it was possible to reconstruct the pure spectroscopic patterns and calculate the relative concentration of each species without recourse to any libraries or any other a priori information. The potential advantages of this novel algorithm and its implications for general chemical system identification of unknown mixtures are discussed.