Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives.

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided.

Semi-automated non-target processing in GC × GC-MS metabolomics analysis: applicability for biomedical studies.

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC-MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC-MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC-MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC-MS and GC-MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC-MS and GC × GC-MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC-MS processing compared to targeted GC-MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC-MS were somewhat higher than with GC-MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC-MS was demonstrated; many additional candidate biomarkers were found with GC × GC-MS compared to GC-MS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0219-6) contains supplementary material, which is available to authorized users.

Metabolic profiling of ultrasmall sample volumes with GC/MS: from microliter to nanoliter samples.

Profiling of metabolites is increasingly used to study the functioning of biological systems. For some studies the volume of available samples is limited to only a few microliters or even less, for fluids such as cerebrospinal fluid (CSF) of small animals like mice or the analysis of individual oocytes. Here we present an analytical method using in-liner silylation coupled to gas chromatography/mass spectrometry (GC/MS), that is suitable for metabolic profiling in ultrasmall sample volumes of 2 microL down to 10 nL. Method performance was assessed in various biosamples. Derivatization efficiencies for sugars, organic acids, and amino acids were satisfactory (105-120%), and repeatabilities were generally better than 15%, except for amino acids that had repeatabilities up to about 35-40%. For endogenous sugars and organic acids in fetal bovine serum, the response was linear for aliquots from 10 nL up to at least 1 microL. The developed GC/MS method was applied for the analysis of different sample matrixes, i.e., fetal bovine serum, mouse CSF, and aliquots of the intracellular content of Xenopus laevis oocytes. To the best of our knowledge, we present here the first comprehensive GC/MS metabolite profiles from mouse CSF and from the intracellular content of a single X. laevis oocyte.

Comprehensive analysis of the metabolome of Pseudomonas putida S12 grown on different carbon sources.

Metabolomics is an emerging, powerful, functional genomics technology that involves the comparative non-targeted analysis of the complete set of metabolites in an organism. We have set-up a robust quantitative metabolomics platform that allows the analysis of 'snapshot' metabolomes. In this study, we have applied this platform for the comprehensive analysis of the metabolite composition of Pseudomonas putida S12 grown on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. This paper focuses on the microbial aspects of analyzing comprehensive metabolomes, and demonstrates that metabolomes can be analyzed reliably. The technical (i.e. sample work-up and analytical) reproducibility was on average 10%, while the biological reproducibility was approximately 40%. Moreover, the energy charge values of the microbial samples generated were determined, and indicated that no biotic or abiotic changes had occurred during sample work-up and analysis. In general, the metabolites present and their concentrations were very similar after growth on the different carbon sources. However, specific metabolites showed large differences in concentration, especially the intermediates involved in the degradation of the carbon sources studied. Principal component discriminant analysis was applied to identify metabolites that are specific for, i.e. not necessarily the metabolites that show those largest differences in concentration, cells grown on either of these four carbon sources. For selected enzymatic reactions, i.e. the glucose-6-phosphate isomerase, triosephosphate isomerase and phosphoglyceromutase reactions, the apparent equilibrium constants (K(app)) were calculated. In several instances a carbon source-dependent deviation between the apparent equilibrium constant (K(app)) and the thermodynamic equilibrium constant (K(eq)) was observed, hinting towards a potential point of metabolic regulation or towards bottlenecks in biosynthesis routes. For glucose-6-phosphate isomerase and phosphoglyceromutase, the K(app) was larger than K(eq), and the results suggested that the specific enzymatic activities of these two enzymes were too low to reach the thermodynamic equilibrium in growing cells. In contrast, with triosephosphate isomerase the K(app) was smaller than K(eq), and the results suggested that this enzyme is kinetically controlled.

Higher mass loadability in comprehensive two-dimensional gas chromatography-mass spectrometry for improved analytical performance in metabolomics analysis.

A major challenge in metabolomics analysis is the accurate quantification of metabolites in the presence of (extremely) high abundant metabolites. Quantification of metabolites at low concentrations can be complicated by co-elution and/or peak distortion when these metabolites elute close to high abundant metabolites. To increase the separation efficiency a comprehensive two-dimensional gas chromatographic-mass spectrometric method (GC x GC-MS) was set up, in which a polar first dimension column and an apolar second dimension column were used to maximize the peak capacity. The feasibility of using wider bore, thicker film columns in the second dimension to improve the mass loadability and inertness of the analytical system was investigated. Several column combinations with varying second dimension column dimensions were compared with a setup with a narrow bore column (0.1mm I.D.) in the second dimension. With a wider bore column (0.32 mm I.D.) in the second dimension the mass loadability was improved 10-fold, and the more inert column surface of the thicker film second dimension column resulted in a more accurate (automated) quantification and improved linearity in the presence of high concentrations of matrix compounds or metabolites. These benefits amply compensated the observed decrease in peak capacity of 40% compared to the narrow bore (0.1mm I.D.) thin film second dimension column. Compared to GC-MS and conventional GC x GC-MS, better performance for quantification of metabolites for typical metabolomics samples was achieved.

Effects of growth conditions and processing on Rehmannia glutinosa using fingerprint strategy.

Metabolite profiling in combination with multivariate statistics is a sophisticated method for quality assessment of natural products. For the development of a quality control strategy in Traditional Chinese Medicine (TCM), we have measured the metabolite fingerprints of Rehmannia glutinosa by GC-MS. Plants were grown under different climate and soil conditions in a phytotron and were processed by a variable number of repetitive steps to investigate the effects on both growth conditions and processing for material medica of R. glutinosa. The GC-MS data have been analyzed by principal component analysis (PCA) and the new approach of the ANOVA-simultaneous component analysis (ASCA) which can combine the information from a structured data design with multivariate analysis. The results clearly show the effect of the different factors and indicate directions for process improvement. When plants were grown under various temperatures, humidity and light intensities for a short period (3 weeks), no significant changes on studied metabolites were observed. However, significant changes were found between different processing cycles. The present data clearly indicate the importance of strictly controlling processing in R. glutinosa and illustrate the impact of growth conditions. This is the first report on the metabolite profile of R. glutinosa that provides a base for the establishment of a quality control strategy.

Microbial metabolomics with gas chromatography/mass spectrometry.

An analytical method was set up suitable for the analysis of microbial metabolomes, consisting of an oximation and silylation derivatization reaction and subsequent analysis by gas chromatography coupled to mass spectrometry. Microbial matrixes contain many compounds that potentially interfere with either the derivatization procedure or analysis, such as high concentrations of salts, complex media or buffer components, or extremely high substrate and product concentrations. The developed method was extensively validated using different microorganisms, i.e., Bacillus subtilis, Propionibacterium freudenreichii, and Escherichia coli. Many metabolite classes could be analyzed with the method: alcohols, aldehydes, amino acids, amines, fatty acids, (phospho-) organic acids, sugars, sugar acids, (acyl-) sugar amines, sugar phosphate, purines, pyrimidines, and aromatic compounds. The derivatization reaction proved to be efficient (>50% transferred to derivatized form) and repeatable (relative standard deviations <10%). Linearity for most metabolites was satisfactory with regression coefficients better than 0.996. Quantification limits were 40-500 pg on-column or 0.1-0.7 mmol/g of microbial cells (dry weight). Generally, intrabatch precision (repeatability) and interbatch precision (reproducibility) for the analysis of metabolites in cell extracts was better than 10 and 15%, respectively. Notwithstanding the nontargeted character of the method and complex microbial matrix, analytical performance for most metabolites fit the requirements for target analysis in bioanalysis. The suitability of the method was demonstrated by analysis of E. coli samples harvested at different growth phases.