The Experts below are selected from a list of 10245 Experts worldwide ranked by ideXlab platform
Jeremy K Nicholson - One of the best experts on this subject based on the ideXlab platform.
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statistical spectroscopic tools for biomarker discovery and systems medicine
Analytical Chemistry, 2013Co-Authors: Steven L. Robinette, John C Lindon, Jeremy K NicholsonAbstract:Metabolic profiling based on comparative, statistical analysis of NMR spectroscopic and mass spectrometric data from complex biological samples has contributed to increased understanding of the role of small molecules in affecting and indicating biological processes. To enable this research, the development of statistical Spectroscopy has been marked by early beginnings in applying pattern recognition to nuclear magnetic resonance data and the introduction of statistical Total Correlation Spectroscopy (STOCSY) as a tool for biomarker identification in the past decade. Extensions of statistical Spectroscopy now compose a family of related tools used for compound identification, data preprocessing, and metabolic pathway analysis. In this Perspective, we review the theory and current state of research in statistical Spectroscopy and discuss the growing applications of these tools to medicine and systems biology. We also provide perspectives on how recent institutional initiatives are providing new platforms ...
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statistical Total Correlation Spectroscopy scaling for enhancement of metabolic information recovery in biological nmr spectra
Analytical Chemistry, 2012Co-Authors: Muireann Coen, Anthony D Maher, John C Lindon, Judith M Fonville, Caroline Rae, Jeremy K NicholsonAbstract:The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical Total Correlation Spectroscopy (STOCSY) can be used to edit NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this "STOCSY editing" concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSY(S)). STOCSY(S) exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (n = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (n = 67). In both cases significant biomarker discovery improvement was observed by using STOCSY(S): the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSY(S) to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSY(S) is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or important features. This approach is generally appropriate for many types of NMR-based complex mixture analyses and hence for wider applications in bioanalytical science.
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data driven approach for metabolite relationship recovery in biological 1h nmr data sets using iterative statistical Total Correlation Spectroscopy
Analytical Chemistry, 2011Co-Authors: Caroline Sands, Muireann Coen, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Statistical Total Correlation Spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite Correlations in NMR metabonomic data sets. Here, ...
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data driven approach for metabolite relationship recovery in biological 1h nmr data sets using iterative statistical Total Correlation Spectroscopy
Analytical Chemistry, 2011Co-Authors: Caroline Sands, Muireann Coen, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Statistical Total Correlation Spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite Correlations in NMR metabonomic data sets. Here, the STOCSY approach is extended in a novel Iterative-STOCSY (I-STOCSY) tool in which Correlations are calculated initially from a driver peak of interest and subsequently for all peaks identified as correlating with a Correlation coefficient greater than a set threshold. Consequently, in a single automated run, the majority of information contained in multiple STOCSY calculations from all peaks recursively correlated to the original user defined driver peak of interest are recovered. In addition, highly correlating peaks are clustered into putative structurally related sets, and the results are presented in a fully interactive plot where each set is represented by a node; node-to-node connections are plotted alongside corresponding spectral data colored by the strength of connection, thus allowing the intuitive exploration of both inter- and intrametabolite connections. The I-STOCSY approach has been here applied to a (1)H NMR data set of 24 h postdose aqueous liver extracts from rats treated with the model hepatotoxin galactosamine and has been shown both to recover the previously deduced major metabolic effects of treatment and to generate new hypotheses even on this well-studied model system. I-STOCSY, thus, represents a significant advance in Correlation based analysis and visualization, providing insight into inter- and intrametabolite relationships following metabolic perturbations.
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statistical Total Correlation Spectroscopy editing of 1h nmr spectra of biofluids application to drug metabolite profile identification and enhanced information recovery
Analytical Chemistry, 2009Co-Authors: Caroline Sands, Muireann Coen, Anthony D Maher, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Here we present a novel method for enhanced NMR spectral information recovery, utilizing a statistical Total Correlation Spectroscopy editing (STOCSY-E) procedure for the identification of drug metabolite peaks in biofluids and for deconvolution of drug and endogenous metabolite signals. Structurally correlated peaks from drug metabolites and those from closely related drug metabolite pathways are first identified using STOCSY. Subsequently, this Correlation information is utilized to scale the biofluid (1)H NMR spectra across these identified regions, producing a modified set of spectra in which drug metabolite contributions are reduced and, thus, facilitating analysis by pattern recognition methods without drug metabolite interferences. The application of STOCSY-E is illustrated with two exemplar (1)H NMR spectroscopic data sets, posing various drug metabolic, toxicological, and analytical challenges viz. 800 MHz (1)H spectra of human urine (n = 21) collected over 10 h following dosing with the antibiotic flucloxacillin and 600 MHz (1)H NMR spectra of rat urine (n = 27) collected over 48 h following exposure to the renal papillary toxin 2-bromoethanamine (BEA). STOCSY-E efficiently identified and removed the major xenobiotic metabolite peaks in both data sets, providing enhanced visualization of endogenous changes via orthogonal to projection filtered partial least-squares discriminant analysis (OPLS-DA). OPLS-DA of the STOCSY-E spectral data from the BEA-treated rats revealed the gut bacterial-mammalian co-metabolite phenylacetylglycine as a previously unidentified surrogate biomarker of toxicity. STOCSY-E has a wide range of potential applications in clinical, epidemiology, toxicology, and nutritional studies where multiple xenobiotic metabolic interferences may confound biological interpretation. Additionally, this tool could prove useful for applications outside of metabolic analysis, for example, in process chemistry for following chemical reactions and equilibria and detecting impurities.
Elaine Holmes - One of the best experts on this subject based on the ideXlab platform.
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data driven approach for metabolite relationship recovery in biological 1h nmr data sets using iterative statistical Total Correlation Spectroscopy
Analytical Chemistry, 2011Co-Authors: Caroline Sands, Muireann Coen, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Statistical Total Correlation Spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite Correlations in NMR metabonomic data sets. Here, the STOCSY approach is extended in a novel Iterative-STOCSY (I-STOCSY) tool in which Correlations are calculated initially from a driver peak of interest and subsequently for all peaks identified as correlating with a Correlation coefficient greater than a set threshold. Consequently, in a single automated run, the majority of information contained in multiple STOCSY calculations from all peaks recursively correlated to the original user defined driver peak of interest are recovered. In addition, highly correlating peaks are clustered into putative structurally related sets, and the results are presented in a fully interactive plot where each set is represented by a node; node-to-node connections are plotted alongside corresponding spectral data colored by the strength of connection, thus allowing the intuitive exploration of both inter- and intrametabolite connections. The I-STOCSY approach has been here applied to a (1)H NMR data set of 24 h postdose aqueous liver extracts from rats treated with the model hepatotoxin galactosamine and has been shown both to recover the previously deduced major metabolic effects of treatment and to generate new hypotheses even on this well-studied model system. I-STOCSY, thus, represents a significant advance in Correlation based analysis and visualization, providing insight into inter- and intrametabolite relationships following metabolic perturbations.
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data driven approach for metabolite relationship recovery in biological 1h nmr data sets using iterative statistical Total Correlation Spectroscopy
Analytical Chemistry, 2011Co-Authors: Caroline Sands, Muireann Coen, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Statistical Total Correlation Spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite Correlations in NMR metabonomic data sets. Here, ...
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statistical Total Correlation Spectroscopy editing of 1h nmr spectra of biofluids application to drug metabolite profile identification and enhanced information recovery
Analytical Chemistry, 2009Co-Authors: Caroline Sands, Muireann Coen, Anthony D Maher, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Here we present a novel method for enhanced NMR spectral information recovery, utilizing a statistical Total Correlation Spectroscopy editing (STOCSY-E) procedure for the identification of drug metabolite peaks in biofluids and for deconvolution of drug and endogenous metabolite signals. Structurally correlated peaks from drug metabolites and those from closely related drug metabolite pathways are first identified using STOCSY. Subsequently, this Correlation information is utilized to scale the biofluid (1)H NMR spectra across these identified regions, producing a modified set of spectra in which drug metabolite contributions are reduced and, thus, facilitating analysis by pattern recognition methods without drug metabolite interferences. The application of STOCSY-E is illustrated with two exemplar (1)H NMR spectroscopic data sets, posing various drug metabolic, toxicological, and analytical challenges viz. 800 MHz (1)H spectra of human urine (n = 21) collected over 10 h following dosing with the antibiotic flucloxacillin and 600 MHz (1)H NMR spectra of rat urine (n = 27) collected over 48 h following exposure to the renal papillary toxin 2-bromoethanamine (BEA). STOCSY-E efficiently identified and removed the major xenobiotic metabolite peaks in both data sets, providing enhanced visualization of endogenous changes via orthogonal to projection filtered partial least-squares discriminant analysis (OPLS-DA). OPLS-DA of the STOCSY-E spectral data from the BEA-treated rats revealed the gut bacterial-mammalian co-metabolite phenylacetylglycine as a previously unidentified surrogate biomarker of toxicity. STOCSY-E has a wide range of potential applications in clinical, epidemiology, toxicology, and nutritional studies where multiple xenobiotic metabolic interferences may confound biological interpretation. Additionally, this tool could prove useful for applications outside of metabolic analysis, for example, in process chemistry for following chemical reactions and equilibria and detecting impurities.
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analytic properties of statistical Total Correlation Spectroscopy based information recovery in 1h nmr metabolic data sets
Analytical Chemistry, 2009Co-Authors: Alexessander Couto Alves, Mattias Rantalainen, Elaine Holmes, Jeremy K Nicholson, Timothy M D EbbelsAbstract:Structural assignment of resonances is an important problem in NMR Spectroscopy, and statistical Total Correlation Spectroscopy (STOCSY) is a useful tool aiding this process for small molecules in complex mixture analysis and metabolic profiling studies. STOCSY delivers intramolecular information (delineating structural connectivity) and in metabolism studies can generate information on pathway-related Correlations. To understand further the behavior of STOCSY for structural assignment, we analyze the statistical distribution of structural and nonstructural Correlations from 1050 1H NMR spectra of normal rat urine samples. We find that the distributions of structural/nonstructural Correlations are significantly different (p < 10−112). From the area under the curve of the receiver operating characteristic (ROC AUC) we show that structural Correlations exceed nonstructural Correlations with probability AUC = 0.98. Through a bootstrap resampling approach, we demonstrate that sample size has a surprisingly sm...
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dynamic biochemical information recovery in spontaneous human seminal fluid reactions via 1h nmr kinetic statistical Total Correlation Spectroscopy
Analytical Chemistry, 2009Co-Authors: Anthony D Maher, Olivier Cloarec, John C Lindon, Elaine Holmes, Prasad Patki, Michael Craggs, Jeremy K NicholsonAbstract:Human seminal fluid (HSF) is a complex mixture of reacting glandular metabolite and protein secretions that provides critical support functions in fertilization. We have employed 600-MHz 1H NMR Spectroscopy to compare and contrast the temporal biochemical and biophysical changes in HSF from infertile men with spinal cord injury compared to age-matched controls. We have developed new approaches to data analysis and visualization to facilitate the interpretation of the results, including the first application of the recently published K-STOCSY concept to a biofluid, enhancing the extraction of information on biochemically related metabolites and assignment of resonances from the major seminal protein, semenogelin. Principal components analysis was also applied to evaluate the extent to which macromolecules influence the overall variation in the metabolic data set. The K-STOCSY concept was utilized further to determine the relationships between reaction rates and metabolite levels, revealing that choline, N-...
Olivier Cloarec - One of the best experts on this subject based on the ideXlab platform.
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dynamic biochemical information recovery in spontaneous human seminal fluid reactions via 1h nmr kinetic statistical Total Correlation Spectroscopy
Analytical Chemistry, 2009Co-Authors: Anthony D Maher, Olivier Cloarec, John C Lindon, Elaine Holmes, Prasad Patki, Michael Craggs, Jeremy K NicholsonAbstract:Human seminal fluid (HSF) is a complex mixture of reacting glandular metabolite and protein secretions that provides critical support functions in fertilization. We have employed 600-MHz 1H NMR Spectroscopy to compare and contrast the temporal biochemical and biophysical changes in HSF from infertile men with spinal cord injury compared to age-matched controls. We have developed new approaches to data analysis and visualization to facilitate the interpretation of the results, including the first application of the recently published K-STOCSY concept to a biofluid, enhancing the extraction of information on biochemically related metabolites and assignment of resonances from the major seminal protein, semenogelin. Principal components analysis was also applied to evaluate the extent to which macromolecules influence the overall variation in the metabolic data set. The K-STOCSY concept was utilized further to determine the relationships between reaction rates and metabolite levels, revealing that choline, N-...
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magic angle spinning nmr and 1h 31p heteronuclear statistical Total Correlation Spectroscopy of intact human gut biopsies
Analytical Chemistry, 2008Co-Authors: Yulan Wang, Olivier Cloarec, Huiru Tang, John C Lindon, Elaine Holmes, Sunil Kochhar, Jeremy K NicholsonAbstract: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 phosphlipid/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...
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heteronuclear 1h 31p statistical Total Correlation nmr Spectroscopy of intact liver for metabolic biomarker assignment application to galactosamine induced hepatotoxicity
Analytical Chemistry, 2007Co-Authors: Muireann Coen, Olivier Cloarec, John C Lindon, Elaine Holmes, Youngshick Hong, Cindy M Rhode, Michael D Reily, Donald G Robertson, Jeremy K NicholsonAbstract: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 mol...
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statistical Correlation and projection methods for improved information recovery from diffusion edited nmr spectra of biological samples
Analytical Chemistry, 2007Co-Authors: Leon M Smith, Anthony D Maher, Olivier Cloarec, Mattias Rantalainen, Huiru Tang, Paul Elliott, Jeremiah Stamler, John C Lindon, Elaine HolmesAbstract:Although NMR spectroscopic techniques coupled with multivariate statistics can yield much useful information for classifying biological samples based on metabolic profiles, biomarker identification remains a time-consuming and complex procedure involving separation methods, two-dimensional NMR, and other spectroscopic tools. We present a new approach to aid complex biomixture analysis that combines diffusion ordered (DO) NMR Spectroscopy with statistical Total Correlation Spectroscopy (STOCSY) and demonstrate its application in the characterization of urinary biomarkers and enhanced information recovery from plasma NMR spectra. This method relies on calculation and display of the covariance of signal intensities from the various nuclei on the same molecule across a series of spectra collected under different pulsed field gradient conditions that differentially attenuate the signal intensities according to translational molecular diffusion rates. We term this statistical diffusion-ordered Spectroscopy (S-DOSY). We also have developed a new visualization tool in which the apparent diffusion coefficients from DO spectra are projected onto a 1D NMR spectrum (diffusion-ordered projection Spectroscopy, DOPY). Both methods either alone or in combination have the potential for general applications to any complex mixture analysis where the sample contains compounds with a range of diffusion coefficients.
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detection of urinary drug metabolite xenometabolome signatures in molecular epidemiology studies via statistical Total Correlation nmr Spectroscopy
Analytical Chemistry, 2007Co-Authors: Elaine Holmes, Muireann Coen, Olivier Cloarec, Huiru Tang, Paul Elliott, Ruey Leng Loo, Elaine Maibaum, Stephen J Bruce, Queenie Chan, Jeremiah StamlerAbstract:Western populations use prescription and nonprescription drugs extensively, but large-scale population usage is rarely assessed objectively in epidemiological studies. Here we apply statistical methods to characterize structural pathway connectivities of metabolites of commonly used drugs detected routinely in H-1 NMR spectra of urine in a human population study. H-1 NMR spectra were measured for two groups of urine samples obtained from U.S. participants in a known population study. The novel application of a statistical Total Correlation Spectroscopy (STOCSY) approach enabled rapid identification of the major and certain minor drug metabolites in common use in the population, in particular, from acetaminophen and ibuprofen metabolites. This work shows that statistical connectivities between drug metabolites can be established in routine "high-throughput" NMR screening of human samples from participants who have randomly self-administered drugs. This approach should be of value in considering interpopulation patterns of drug metabolism in epidemiological and pharmacogenetic studies.
John C Lindon - One of the best experts on this subject based on the ideXlab platform.
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statistical spectroscopic tools for biomarker discovery and systems medicine
Analytical Chemistry, 2013Co-Authors: Steven L. Robinette, John C Lindon, Jeremy K NicholsonAbstract:Metabolic profiling based on comparative, statistical analysis of NMR spectroscopic and mass spectrometric data from complex biological samples has contributed to increased understanding of the role of small molecules in affecting and indicating biological processes. To enable this research, the development of statistical Spectroscopy has been marked by early beginnings in applying pattern recognition to nuclear magnetic resonance data and the introduction of statistical Total Correlation Spectroscopy (STOCSY) as a tool for biomarker identification in the past decade. Extensions of statistical Spectroscopy now compose a family of related tools used for compound identification, data preprocessing, and metabolic pathway analysis. In this Perspective, we review the theory and current state of research in statistical Spectroscopy and discuss the growing applications of these tools to medicine and systems biology. We also provide perspectives on how recent institutional initiatives are providing new platforms ...
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statistical Total Correlation Spectroscopy scaling for enhancement of metabolic information recovery in biological nmr spectra
Analytical Chemistry, 2012Co-Authors: Muireann Coen, Anthony D Maher, John C Lindon, Judith M Fonville, Caroline Rae, Jeremy K NicholsonAbstract:The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical Total Correlation Spectroscopy (STOCSY) can be used to edit NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this "STOCSY editing" concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSY(S)). STOCSY(S) exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (n = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (n = 67). In both cases significant biomarker discovery improvement was observed by using STOCSY(S): the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSY(S) to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSY(S) is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or important features. This approach is generally appropriate for many types of NMR-based complex mixture analyses and hence for wider applications in bioanalytical science.
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data driven approach for metabolite relationship recovery in biological 1h nmr data sets using iterative statistical Total Correlation Spectroscopy
Analytical Chemistry, 2011Co-Authors: Caroline Sands, Muireann Coen, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Statistical Total Correlation Spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite Correlations in NMR metabonomic data sets. Here, ...
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data driven approach for metabolite relationship recovery in biological 1h nmr data sets using iterative statistical Total Correlation Spectroscopy
Analytical Chemistry, 2011Co-Authors: Caroline Sands, Muireann Coen, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Statistical Total Correlation Spectroscopy (STOCSY) is a well-established and valuable method in the elucidation of both inter- and intrametabolite Correlations in NMR metabonomic data sets. Here, the STOCSY approach is extended in a novel Iterative-STOCSY (I-STOCSY) tool in which Correlations are calculated initially from a driver peak of interest and subsequently for all peaks identified as correlating with a Correlation coefficient greater than a set threshold. Consequently, in a single automated run, the majority of information contained in multiple STOCSY calculations from all peaks recursively correlated to the original user defined driver peak of interest are recovered. In addition, highly correlating peaks are clustered into putative structurally related sets, and the results are presented in a fully interactive plot where each set is represented by a node; node-to-node connections are plotted alongside corresponding spectral data colored by the strength of connection, thus allowing the intuitive exploration of both inter- and intrametabolite connections. The I-STOCSY approach has been here applied to a (1)H NMR data set of 24 h postdose aqueous liver extracts from rats treated with the model hepatotoxin galactosamine and has been shown both to recover the previously deduced major metabolic effects of treatment and to generate new hypotheses even on this well-studied model system. I-STOCSY, thus, represents a significant advance in Correlation based analysis and visualization, providing insight into inter- and intrametabolite relationships following metabolic perturbations.
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statistical Total Correlation Spectroscopy editing of 1h nmr spectra of biofluids application to drug metabolite profile identification and enhanced information recovery
Analytical Chemistry, 2009Co-Authors: Caroline Sands, Muireann Coen, Anthony D Maher, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Here we present a novel method for enhanced NMR spectral information recovery, utilizing a statistical Total Correlation Spectroscopy editing (STOCSY-E) procedure for the identification of drug metabolite peaks in biofluids and for deconvolution of drug and endogenous metabolite signals. Structurally correlated peaks from drug metabolites and those from closely related drug metabolite pathways are first identified using STOCSY. Subsequently, this Correlation information is utilized to scale the biofluid (1)H NMR spectra across these identified regions, producing a modified set of spectra in which drug metabolite contributions are reduced and, thus, facilitating analysis by pattern recognition methods without drug metabolite interferences. The application of STOCSY-E is illustrated with two exemplar (1)H NMR spectroscopic data sets, posing various drug metabolic, toxicological, and analytical challenges viz. 800 MHz (1)H spectra of human urine (n = 21) collected over 10 h following dosing with the antibiotic flucloxacillin and 600 MHz (1)H NMR spectra of rat urine (n = 27) collected over 48 h following exposure to the renal papillary toxin 2-bromoethanamine (BEA). STOCSY-E efficiently identified and removed the major xenobiotic metabolite peaks in both data sets, providing enhanced visualization of endogenous changes via orthogonal to projection filtered partial least-squares discriminant analysis (OPLS-DA). OPLS-DA of the STOCSY-E spectral data from the BEA-treated rats revealed the gut bacterial-mammalian co-metabolite phenylacetylglycine as a previously unidentified surrogate biomarker of toxicity. STOCSY-E has a wide range of potential applications in clinical, epidemiology, toxicology, and nutritional studies where multiple xenobiotic metabolic interferences may confound biological interpretation. Additionally, this tool could prove useful for applications outside of metabolic analysis, for example, in process chemistry for following chemical reactions and equilibria and detecting impurities.
Anthony D Maher - One of the best experts on this subject based on the ideXlab platform.
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statistical Total Correlation Spectroscopy scaling for enhancement of metabolic information recovery in biological nmr spectra
Analytical Chemistry, 2012Co-Authors: Muireann Coen, Anthony D Maher, John C Lindon, Judith M Fonville, Caroline Rae, Jeremy K NicholsonAbstract:The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical Total Correlation Spectroscopy (STOCSY) can be used to edit NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this "STOCSY editing" concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSY(S)). STOCSY(S) exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (n = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (n = 67). In both cases significant biomarker discovery improvement was observed by using STOCSY(S): the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSY(S) to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSY(S) is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or important features. This approach is generally appropriate for many types of NMR-based complex mixture analyses and hence for wider applications in bioanalytical science.
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statistical Total Correlation Spectroscopy editing of 1h nmr spectra of biofluids application to drug metabolite profile identification and enhanced information recovery
Analytical Chemistry, 2009Co-Authors: Caroline Sands, Muireann Coen, Anthony D Maher, John C Lindon, Elaine Holmes, Timothy M D Ebbels, Jeremy K NicholsonAbstract:Here we present a novel method for enhanced NMR spectral information recovery, utilizing a statistical Total Correlation Spectroscopy editing (STOCSY-E) procedure for the identification of drug metabolite peaks in biofluids and for deconvolution of drug and endogenous metabolite signals. Structurally correlated peaks from drug metabolites and those from closely related drug metabolite pathways are first identified using STOCSY. Subsequently, this Correlation information is utilized to scale the biofluid (1)H NMR spectra across these identified regions, producing a modified set of spectra in which drug metabolite contributions are reduced and, thus, facilitating analysis by pattern recognition methods without drug metabolite interferences. The application of STOCSY-E is illustrated with two exemplar (1)H NMR spectroscopic data sets, posing various drug metabolic, toxicological, and analytical challenges viz. 800 MHz (1)H spectra of human urine (n = 21) collected over 10 h following dosing with the antibiotic flucloxacillin and 600 MHz (1)H NMR spectra of rat urine (n = 27) collected over 48 h following exposure to the renal papillary toxin 2-bromoethanamine (BEA). STOCSY-E efficiently identified and removed the major xenobiotic metabolite peaks in both data sets, providing enhanced visualization of endogenous changes via orthogonal to projection filtered partial least-squares discriminant analysis (OPLS-DA). OPLS-DA of the STOCSY-E spectral data from the BEA-treated rats revealed the gut bacterial-mammalian co-metabolite phenylacetylglycine as a previously unidentified surrogate biomarker of toxicity. STOCSY-E has a wide range of potential applications in clinical, epidemiology, toxicology, and nutritional studies where multiple xenobiotic metabolic interferences may confound biological interpretation. Additionally, this tool could prove useful for applications outside of metabolic analysis, for example, in process chemistry for following chemical reactions and equilibria and detecting impurities.
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dynamic biochemical information recovery in spontaneous human seminal fluid reactions via 1h nmr kinetic statistical Total Correlation Spectroscopy
Analytical Chemistry, 2009Co-Authors: Anthony D Maher, Olivier Cloarec, John C Lindon, Elaine Holmes, Prasad Patki, Michael Craggs, Jeremy K NicholsonAbstract:Human seminal fluid (HSF) is a complex mixture of reacting glandular metabolite and protein secretions that provides critical support functions in fertilization. We have employed 600-MHz 1H NMR Spectroscopy to compare and contrast the temporal biochemical and biophysical changes in HSF from infertile men with spinal cord injury compared to age-matched controls. We have developed new approaches to data analysis and visualization to facilitate the interpretation of the results, including the first application of the recently published K-STOCSY concept to a biofluid, enhancing the extraction of information on biochemically related metabolites and assignment of resonances from the major seminal protein, semenogelin. Principal components analysis was also applied to evaluate the extent to which macromolecules influence the overall variation in the metabolic data set. The K-STOCSY concept was utilized further to determine the relationships between reaction rates and metabolite levels, revealing that choline, N-...
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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
Analytical Chemistry, 2008Co-Authors: Anthony D Maher, Derek J Crockford, Henrik Toft, Daniel Malmodin, Johan H Faber, Mark I Mccarthy, Amy Barrett, Maxine Allen, M Walker, Elaine HolmesAbstract:Optimizing NMR experimental parameters for high-throughput metabolic phenotyping requires careful examination of the Total biochemical information obtainable from 1H 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 1H NMR spectra in which broad lines are suppressed via T2 relaxation editing. Finally, we applied these methods for identification of the metabolic phe...
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statistical Correlation and projection methods for improved information recovery from diffusion edited nmr spectra of biological samples
Analytical Chemistry, 2007Co-Authors: Leon M Smith, Anthony D Maher, Olivier Cloarec, Mattias Rantalainen, Huiru Tang, Paul Elliott, Jeremiah Stamler, John C Lindon, Elaine HolmesAbstract:Although NMR spectroscopic techniques coupled with multivariate statistics can yield much useful information for classifying biological samples based on metabolic profiles, biomarker identification remains a time-consuming and complex procedure involving separation methods, two-dimensional NMR, and other spectroscopic tools. We present a new approach to aid complex biomixture analysis that combines diffusion ordered (DO) NMR Spectroscopy with statistical Total Correlation Spectroscopy (STOCSY) and demonstrate its application in the characterization of urinary biomarkers and enhanced information recovery from plasma NMR spectra. This method relies on calculation and display of the covariance of signal intensities from the various nuclei on the same molecule across a series of spectra collected under different pulsed field gradient conditions that differentially attenuate the signal intensities according to translational molecular diffusion rates. We term this statistical diffusion-ordered Spectroscopy (S-DOSY). We also have developed a new visualization tool in which the apparent diffusion coefficients from DO spectra are projected onto a 1D NMR spectrum (diffusion-ordered projection Spectroscopy, DOPY). Both methods either alone or in combination have the potential for general applications to any complex mixture analysis where the sample contains compounds with a range of diffusion coefficients.
