Data-processing strategies for metabolomics studies

Metabolomics studies aim at a better understanding of biochemical processes by studying relations between metabolites and between metabolites and other types of information (e.g., sensory and phenotypic features). The objectives of these studies are diverse, but the types of data generated and the methods for extracting information from the data and analysing the data are similar. Besides instrumental analysis tools, various data-analysis tools are needed to extract this relevant information. The entire data-processing workflow is complex and has many steps. For a comprehensive overview, we cover the entire workflow of metabolomics studies, starting from experimental design and sample-size determination to tools that can aid in biological interpretation. We include illustrative examples and discuss the problems that have to be dealt with in data analysis in metabolomics. We also discuss where the challenges are for developing new methods and tailor-made quantitative strategies.     

Evidence of vintage effects on grape wines using H NMR-based metabolomic study

The chemical composition of grape wines varies with grape variety, environmental factors of climate and soil, and bacterial strains, which can each affect the wine quality. Using ¹H NMR analysis coupled with multivariate statistical data sets, we investigated the effects of grape vintage on metabolic profiles of wine and the relationship between wine metabolites and meteorological data. Principal component analysis (PCA) showed a clear differentiation between Meoru wines that were vinified with the same yeast strain and Meoru grapes harvested from the same vineyard but with a different vintage. The metabolites contributing to the differentiation were identified as 2,3-butandiol, lactic acid, alanine, proline, γ-aminobutyric acid (GABA), choline, and polyphenols, by complementary PCA loading plot. Markedly higher levels of proline, lactic acid and polyphenols were observed in the 2006 vintage wines compared to those of 2007 vintage, showing excellent agreement with the meteorological data that the sun-exposed time and rainfall in 2006 were approximately two times more and four times less, respectively, than those in 2007. These results revealed the important role of climate during ripening period in the chemical compositions of the grape. This study highlights the reliability of NMR-based metabolomic data by integration with meteorological data in characterizing wine or grape.     

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry (GC × GC-TOFMS) coupled with pattern recognition methods was applied to analyze plasma from diabetic patients and healthy controls. After sample preparation and GC × GC-TOFMS analysis, collected data were transformed, the peak alignment between different chromatograms was performed to generate the metabolites’ peak table, then orthogonal signal correction filtered partial least-squares discriminant analysis (OSC-PLSDA) was carried out to model the data and discover metabolites with a significant concentration change in diabetic patients. With the method above, diabetic patients and healthy controls could be correctly distinguished based on the metabolic abnormity in plasma. Five potential biomarkers including glucose, 2-hydroxyisobutyric acid, linoleic acid, palmitic acid and phosphate were identified. It was found that elevated free fatty acids were essential pathophysiological factors in diabetes mellitus which reflected either the hyperglycemia or the deregulation of fatty acids metabolism. These potential biomarkers in plasma, e.g. palmitic acid, linoleic acid and 2-hydroxybutyric acid might be helpful in the diagnosis or further study of diabetes mellitus. This study shows the practicability and advantage of GC × GC-TOFMS coupled with data analysis and mining for metabonomics in biomarker discovery.     

Capillary zone electrophoresis with field enhanced sample stacking as a tool for targeted metabolome analysis of adenine nucleotides and coenzymes in Paracoccus denitrificans

The main aim of this work was to demonstrate the applicability of capillary zone electrophoresis in combination with field enhanced sample stacking in targeted metabolome analyses of adenine nucleotides – AMP, ADP, ATP, coenzymes NAD⁺, NADP⁺ and their reduced forms in Paracoccus denitrificans. Sodium carbonate/hydrogencarbonate buffer (100 mM, pH 9.6) with the addition of β‐CD at a concentration of 10 mM was found to be an effective BGE for their separation within 20 min. Besides this, special attention was paid to the development of the procedure for the extraction of specific metabolites from the bacterium P. denitrificans. This procedure was not only optimised to achieve the highest metabolite yields but also to obtain a sample that was fully compatible with the online preconcetration strategy used. The developed methodology was finally applied in a study of the bacterium P. denitrificans at various stages of the active respiratory chain.     

Pattern recognition analysis for <Superscript>1</Superscript>H NMR spectra of plasma from hemodialysis patients

¹H NMR spectroscopic and pattern recognition-based methods (NMR-PR) were applied to the metabolic profiling studies on hemodialysis (HD). Plasma samples were collected from 37 patients before and after HD and measured by 600 MHz NMR spectroscopy. Each spectrum was data-processed and subjected to principal component analysis for pattern recognition. Spectral patterns of plasma between pre- and post-dialyses were clearly discriminated, together with significant fluctuations in the levels of creatinine, trimethylamine-N-oxide, glucose, lactate, and acetate, which were quantitated. We have first observed the significant elevation of lactate levels in post-dialysis plasma. The present study has demonstrated the high feasibility of NMR-PR method for monitoring the dialysis condition and comprehensive profiling of the change of low-molecular-weight metabolites in HD. Figure PCA for 1H NMR spectra of plasma from HD patients     

From differentiating metabolites to biomarkers

The current developments in metabolomics and metabolic profiling technologies have led to the discovery of several new metabolic biomarkers. Finding metabolites present in significantly different levels between sample sets, however, does not necessarily make these metabolites useful biomarkers. The route to valid and applicable biomarkers (biomarker qualification) is long and demands a significant amount of work. In this overview, we critically discuss the current state-of-the-art of metabolic biomarker discovery, with highlights and shortcomings, and suggest a pathway to clinical usefulness.

Design, construction, and validation of a 1-mm triple-resonance high-temperature-superconducting probe for NMR

We report a 600-MHz 1-mm triple-resonance high-temperature-superconducting (HTS) probe for nuclear magnetic resonance spectroscopy. The probe has a real sample volume of about 7.5 microl, an active volume of 6.3 microl, and appears to have the highest mass sensitivity at any field strength. The probe is constructed with four sets of HTS coils that are tuned to 1H, 2H, 13C, and 15N, and there is a z-axis gradient. The coils are cooled with a conventional Bruker CryoPlatform to about 20 K, and the sample chamber can be regulated above or below room temperature over a moderate range using a Bruker variable temperature unit. The absolute S/N for 0.1% ethylbenzene is approximately 1/3 that of a conventional 5mm probe with just 1/70 of the sample volume. We demonstrate the utility of this probe for small molecules and proteins with 2D spectra of just 1.7 microg of ibuprofen and 400 microM 15N-labeled ubiquitin.     

Development of a sheathless CE‐ESI‐MS interface

A sheath‐flow interface is the most common ionization technique in CE‐ESI‐MS. However, this interface dilutes the analytes with the sheath liquid and decreases the sensitivity. In this study, we developed a sheathless CE‐MS interface to improve sensitivity. The interface was fabricated by making a small crack approximately 2 cm from the end of a capillary column fixed on a plastic plate, and then covering the crack with a dialysis membrane to prevent metabolite loss during separation. A voltage for CE separation was applied between the capillary inlet and the buffer reservoir. Under optimum conditions, 52 cationic metabolite standards were separated and selectively detected using MS. With a pressure injection of 5 kPa for 15 s (ca. 1.4 nL), the detection limits for the tested compounds were between 0.06 and 1.7 μmol/L (S/N = 3). The method was applied to analysis of cationic metabolites extracted from a small number (12 000) of cancer cells, and the number of peaks detected was about 2.5 times higher than when using conventional sheath‐flow CE‐MS. Because the interface is easy to construct, it is cost‐effective and can be adapted to any commercially available capillaries. This method is a powerful new tool for highly sensitive CE‐MS‐based metabolomic analysis.     

Effects of the ambient fine particulate matter (PM2.5) exposure on urinary metabolic profiles in rats using UPLC-Q-TOF-MS

The pathogenesis of PM2.5 was evaluated on rats in model groups using a metabonomic method by UPLC-Q-TOF-MS and 17 potential endogenous metablites were identified. The primary metabolism pathways involved pentose and glucuronate interconversions, starch and sucrose metabolism, tryptophan metabolism, tyrosine metabolism, phenylalanine metabolism, purine metabolism, acetaminophen metabolism pathway, retinol metabolism and valproic acid metabolism pathway.     

Robust baseline correction algorithm for signal dense NMR spectra

This paper outlines a fully automated algorithm for baseline correction. Based on our experience with NMR spectra of complex mixtures, this algorithm is designed to automatically differentiate signal points from baseline points. The algorithm's strength is its ability to accurately determine baseline points in very dense spectra, without destroying the line shapes of prominent peaks. The algorithm described is implemented in Chenomx NMR Suite 4.6. It is demonstrated here using two separate spectra acquired on two different NMR spectrometers.