Virtual chromatographic resolution enhancement in cryoflow LC-NMR experiments via statistical total correlation spectroscopy

A new approach to enhancing information recovery from cryogenic probe "on-flow" LC-NMR spectroscopic analyses of complex biological mixtures is demonstrated using a variation on the statistical total correlation spectroscopy (STOCSY) method. Cryoflow probe technology enables sensitive and efficient NMR detection of metabolites on-flow, and the rapid spectral scanning allows multiple spectra to be collected over chromatographic peaks containing several species with similar, but nonidentical, retention times. This enables 1H NMR signal connectivities between close-eluting metabolites to be identified resulting in a "virtual" chromatographic resolution enhancement visualized directly in the NMR spectral projection. We demonstrate the applicability of the approach for structure assignment of drug and endogenous metabolites in urine. This approach is of wide general applicability to any complex mixture analysis problem involving chromatographic peak overlap and with particular application in metabolomics and metabonomics.     

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

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

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.     

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.     

Deconvolution of two-dimensional NMR spectra by fast maximum likelihood reconstruction: application to quantitative metabolomics

We have developed an algorithm called fast maximum likelihood reconstruction (FMLR) that performs spectral deconvolution of 1D-2D NMR spectra for the purpose of accurate signal quantification. FMLR constructs the simplest time-domain model (e.g., the model with the fewest number of signals and parameters) whose frequency spectrum matches the visible regions of the spectrum obtained from identical Fourier processing of the acquired data. We describe the application of FMLR to quantitative metabolomics and demonstrate the accuracy of the method by analysis of complex, synthetic mixtures of metabolites and liver extracts. The algorithm demonstrates greater accuracy (0.5-5.0% error) than peak height analysis and peak integral analysis with greatly reduced operator intervention. FMLR has been implemented in a Java-based framework that is available for download on multiple platforms and is interoperable with popular NMR display and processing software. Two-dimensional (1)H-(13)C spectra of mixtures can be acquired with acquisition times of 15 min and analyzed by FMLR in the range of 2-5 min per spectrum to identify and quantify constituents present at concentrations of 0.2 mM or greater.     

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.     

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.     

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.     

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

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

Resolution of multicomponent fluorescence emission using frequency-dependent phase angle and modulation spectra

We describe a new fluorescence method that allows the resolution of both the decay times and emission spectra of mixtures of fluorophores. This method is completely general and does not require any assumptions or knowledge of the decay times or emission spectra of the individual fluorophores. We use the phase angle spectra and modulation spectra of the mixture, measured over a range of suitable light modulation frequencies and emission wavelengths. These data are analyzed by nonlinear least-squares analysis to recover the emission spectra and the associated decay times. The principle of the method and the nature of the data are illustrated by using two-component mixtures with increasing spectral overlap. We then demonstrate the recovery of minor components, of structure emission spectra, and of a three-component mixture with completed overlapping emission spectra. And finally, we describe the resolution of a two-component mixture with decay times of 0.8 and 1.4 ns using modulation frequencies up to 774 MHz.     

Cryogenic probe 13C NMR spectroscopy of urine for metabonomic studies

Cryogenic probe technology can significantly compensate for the inherently low sensitivity of natural abundance 13C NMR spectroscopy. This now permits its routine use in NMR spectroscopy of biofluids, such as urine or plasma, with acquisition times that enable a high throughput of samples. Metabonomic studies often generate numerous samples in order to characterize fully the time-dependent biochemical response to stimuli, but until now, they have been largely conducted using 1H NMR spectroscopy because of its high sensitivity and hence efficient data acquisition. Here, we demonstrate that information-rich 13C NMR spectra of rat urine can be obtained using appropriately short acquisition times suitable for biochemical samples when using a cryogenic probe. Furthermore, these data were amenable to automated pattern recognition analysis, which produced a profile of the metabolic response to the model hepatotoxin hydrazine that was consistent with earlier studies. Thus, a new source of detailed and complementary information is available to metabonomics using cryogenic probe 13C NMR spectroscopy.     

Impact of analytical bias in metabonomic studies of human blood serum and plasma

Concurrent with the explosion in the number of publications reporting biomarker discovery by profiling technologies, such as proteomics and pattern recognition, has been the increase in evidence highlighting the susceptibility of these approaches to analytical and experimental bias. The work presented here addresses these timely issues by delivering a detailed characterization of the effect of common sources of bias in clinical studies on serum and plasma profiles generated by a key technology in metabonomics, NMR spectroscopy. Specifically, differences in composition when blood samples were collected onto and in the absence of ice, over a series of serum-clot contact times, the stability of NMR-prepared samples over time and the effect on the metabolic profile of freeze-thawing were examined. While differences between individuals were far greater than variation from any other experimental factor, each of the conditions examined did cause slight alterations to the NMR profile that could produce a systematic bias. Variation due to clotting time caused changes in energy metabolites, which were delayed by ice with no other spectral effects. Room-temperature stability and hence NMR spectral repeatability were high (<1% intrasample variation). Higher molecular weight species such as lipoproteins were more susceptible to the variations present in the examined factors. These observations have implications for profiling study design, and hence, our results form a new and valuable resource for those attempting clinical metabolic profiling, for regulatory agencies involved in the licensing of clinical tests and in the generation of international reporting standards for metabonomics.     

Molecular factor analysis applied to collections of NMR spectra

It is often useful to identify and quantify mixture components by analyzing collections of NMR spectra. Such collections arise in metabonomics and many other applications. Many mixtures studied by NMR can contain hundreds of compounds, and it is challenging to analyze the resulting complex spectra. We have approached the problem of separating signals from different molecules in complex mixtures by using self-modeling curve resolution as implemented by the alternating least-squares algorithm. Alternating least squares uses nonnegativity criteria to generate spectra and concentrations from a collection of mixture spectra. Compared to previous applications of alternating least squares, NMR spectra of complex mixtures possess unique features, such as large numbers of components and sample-to-sample variability in peak positions. To deal with these features, we developed a set of data preprocessing methods, and we made modifications to the alternating least-squares algorithm. We use the term "molecular factor analysis" to refer to the preprocessing and modified alternating least-squares methods. Molecular factor analysis was tested using an artificial data set and spectra from a metabonomics study. The results show that the tools can extract valuable information on sample composition from sets of NMR spectra.     

A model for random sampling and estimation of relative protein abundance in shotgun proteomics

Proteomic analysis of complex protein mixtures using proteolytic digestion and liquid chromatography in combination with tandem mass spectrometry is a standard approach in biological studies. Data-dependent acquisition is used to automatically acquire tandem mass spectra of peptides eluting into the mass spectrometer. In more complicated mixtures, for example, whole cell lysates, data-dependent acquisition incompletely samples among the peptide ions present rather than acquiring tandem mass spectra for all ions available. We analyzed the sampling process and developed a statistical model to accurately predict the level of sampling expected for mixtures of a specific complexity. The model also predicts how many analyses are required for saturated sampling of a complex protein mixture. For a yeast-soluble cell lysate 10 analyses are required to reach a 95% saturation level on protein identifications based on our model. The statistical model also suggests a relationship between the level of sampling observed for a protein and the relative abundance of the protein in the mixture. We demonstrate a linear dynamic range over 2 orders of magnitude by using the number of spectra (spectral sampling) acquired for each protein.     

Spectral pattern recognition of in situ FT-IR spectroscopic reaction data using minimization of entropy and spectral similarity (MESS): application to the homogeneous rhodium catalyzed hydroformylation of isoprene

An improved algorithm using minimization of entropy and spectral similarity (MESS) was tested to recover pure component spectra from in situ experimental Fourier transform infrared (FT-IR) reaction spectral data, which were collected from a homogeneous rhodium catalyzed hydroformylation of isoprene. The experimental spectra are complicated and highly overlapping because of the presence of multiple intermediate products in this reaction system. The traditional entropy minimization method fails to resolve real reaction mixture spectra, but MESS can successfully reconstruct pure component spectra of unknown intermediate products for real reaction systems by the addition of minimization of spectral similarity. The quantitative measure of spectral similarity between two spectra was given by their inner products. The results indicate that MESS is a stable and useful algorithm for spectral pattern recognition of highly overlapped experimental reaction spectra. Comparison is also made between MESS, entropy minimization, simple-to-use interactive self-modeling mixture analysis (SIMPLISMA), interactive principle component analysis (IPCA), and orthogonal projection approach-alternating least squares (OPA-ALS).     

Effects of binary encoding on pattern recognition and library matching of spectral data

Scott, D.R., 1988. Effects of binary encoding on pattern recognition and library matching of spectral data. Chemometrics and Intelligent Laboratory Systems, 4: 47–63.Binary encoding is frequently employed in pattern recognition studies and matching of unknown against library spectral data. In this study the effect of binary encoding on pattern recognition and library searches is determined using the Hamming and Euclidean distance metrics. The effect on a full intensity spectrum is to compress the total information into a qualitative spectral data vector, the most basic information in the full spectrum. Geometrically, binary encoding of unit normalized spectral data can be visualized as shifting spectral points on the faces of the measurement space hypercube to the corners of a Hamming hypercube. A new classification scheme for comparison of analytical spectra based on their binary encoded spectra is introduced. Quantitative expressions for the effect of binary encoding on general Euclidean distances between spectral points are derived and shown to depend upon their spectral classification. Generally binary encoding increases the interclass distances in pattern recognition and may decrease the intraclass distances. This effect is illustrated with a mass spectral pattern recognition example. The effect of binary encoding on library searches is to produce possible false compound identification in identity searches and to flag spectrally similar compounds in structure searches. A scheme which uses both the Hamming and the Euclidean metrics is proposed for improved library searches. This scheme is illustrated with searches of a small mass spectral library for benzene and p-dioxane spectra.     

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.     

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.     

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.