Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry (GC–EI–TOF/MS) based plant metabolomics

The application of gas chromatography–mass spectrometry (GC–MS) to the ‘global’ analysis of metabolites in complex samples (i.e. metabolomics) has now become routine. The generation of these data-rich profiles demands new strategies in data mining and standardisation of experimental and reporting aspects across laboratories. As part of the META-PHOR project’s (METAbolomics for Plants Health and OutReach: ) priorities towards robust technology development, a GC–MS ring experiment based upon three complex matrices (melon, broccoli and rice) was launched. All sample preparation, data processing, multivariate analyses and comparisons of major metabolite features followed standardised protocols, identical models of GC (Agilent 6890N) and TOF/MS (Leco Pegasus III) were also employed. In addition comprehensive GC×GC–TOF/MS was compared with 1 dimensional GC–TOF/MS. Comparisons of the paired data from the various laboratories were made with a single data processing and analysis method providing an unbiased assessment of analytical method variants and inter-laboratory reproducibility. A range of processing and statistical methods were also assessed with a single exemplary dataset revealing near equal performance between them. Further investigations of long-term reproducibility are required, though the future generation of global and valid metabolomics databases offers much promise.     

Review of recent developments in GC–MS approaches to metabolomics-based research

Background

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC–MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and ‘in house’ metabolite databases available.

Aim of review

This review provides an overview of developments in GC–MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC–MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

Key scientific concepts of review

The purpose of this review is to both highlight and provide an update on GC–MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC–MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

     

Granger causality in integrated GC–MS and LC–MS metabolomics data reveals the interface of primary and secondary metabolism

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS). Each platform has a specific performance detecting subsets of metabolites. GC–MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC–MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC–MS and LC–MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC–LC–MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.     

Metabolite profiling of small cerebrospinal fluid sample volumes with gas chromatography–mass spectrometry: application to a rat model of multiple sclerosis

Analysis of metabolites in biofluids by gas chromatography–mass spectrometry (GC–MS) after oximation and silylation is a key method in metabolomics. The GC–MS method was modified by a modified vial design and sample work-up procedure in order to make the method applicable to small volumes of cerebrospinal fluid (CSF), i.e. 10 μL, with similar coverage compared to the standard procedure using ≥100 μL of CSF. The data quality of the modified GC–MS method was assessed by analyzing a study sample set in an animal model for multiple sclerosis, including repetitively analysed quality control rat CSF samples. Automated normalization and intra- and inter-batch correction significantly improved the data quality with the majority of metabolites showing a relative standard deviation <20 %. The modified GC–MS method was successfully applied in rat model of multiple sclerosis where statistical analysis of 93 metabolites, of which 73 were (tentatively) identified, in 10 μL of rat CSF showed statistically significant differences in metabolite profiles of rats at the onset and peak of experimental autoimmune encephalomyelitis compared to rats in the control group. The modified GC–MS method presented proved to be a valid and valuable metabolomics method when only limited sample volumes are available.     

The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry

Mass spectrometry (MS) has been a major driver for metabolomics, and gas chromatography (GC)-MS has been one of the primary techniques used for microbial metabolomics. The use of liquid chromatography (LC)-MS has however been limited, but electrospray ionization (ESI) is very well suited for ionization of microbial metabolites without any previous derivatization needed. To address the capabilities of ESI-MS in detecting the metabolome of Saccharomyces cerevisiae, the in silico metabolome of this organism was used as a template to present a theoretical metabolome. This showed that in combination with the specificity of MS up to 84% of the metabolites can be identified in a high mass accuracy ESI-spectrum. A total of 66 metabolites were systematically analyzed by positive and negative ESI-MS/MS with the aim of initiating a spectral library for ESI of microbial metabolites. This systematic analysis gave insight into the ionization and fragmentation characteristics of the different metabolites. With this insight, a small study of metabolic footprinting with ESI-MS demonstrated that biological information can be extracted from footprinting spectra. Statistical analysis of the footprinting data revealed discriminating ions, which could be assigned using the in silico metabolome. By this approach metabolic footprinting can advance from a classification method that is used to derive biological information based on guilt-by-association, to a tool for extraction of metabolic differences, which can guide new targeted biological experiments. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

An innovative approach to grape metabolomics: stilbene profiling by suspect screening analysis

Suspect screening analysis is a targeted metabolomics approach in which identification of compounds relies on specific available information such as their molecular formula and isotopic pattern. This method was applied to the study of grape metabolomics with an UPLC/MS high-resolution Q-TOF mass spectrometer (nominal resolution 40,000) coupled with a Jet Stream ionization source. The present paper describes the detailed qualitative and quantitative study of grape stilbenes, the principal polyphenols associated with the beneficial effects of drinking wine. For identification of compounds, a new database was expressly constructed from the molecular information of potential metabolites of grape and wine from the literature and other electronic databases. Currently, GrapeMetabolomics contains about a thousand putative grape compounds. If untargeted analysis of a sample provides identification of a new compound with a sufficiently confident score, it is added to the database. Thus, by increasing the number of samples studied, GrapeMetabolomics can be expanded. This method is effective for identification of the molecular formulae of several hundred metabolites in two runs (positive and negative ionization) with minimal sample preparation, and can also be used to analyse some single classes of compounds involved in cell and tissue metabolism. With this approach, a total of 18 stilbene derivatives was identified in two grape samples (Raboso Piave and Primitivo) on the basis of accurate mass measurements and isotopic patterns, and identification was confirmed by MS/MS analysis. The approach can also potentially be applied to the metabolomics of other plant varieties.     

Nanostructure Initiator Mass Spectrometry for tissue imaging in metabolomics: future prospects and perspectives

Mass spectrometry-based metabolomics provides a new approach to interrogate mechanistic biochemistry related to natural processes such as health and disease. Physiological and pathological conditions, however, are characterized not only by the identities and concentrations of metabolites present, but also by the location of metabolites within a tissue. Unfortunately, most relevant MS platforms in metabolomics can only measure samples in solution, therefore metabolites are typically extracted by tissue homogenization. Recent developments of imaging-MS technologies, however, have allowed particular metabolites to be spatially localized within biological tissues. In this context, Nanostructure-Initiator Mass Spectrometry (NIMS), a matrix-free technique for surface-based analysis, has proven an alternative approach for tissue imaging of metabolites. Here we review the basic principles of NIMS for tissue imaging and show applications that can complement LC/MS and GC/MS-based metabolomic studies investigating the mechanisms of fundamental biological processes. In addition, the new surface modifications and nanostructured materials herein presented demonstrate the versatility of NIMS surface to expand the range of detectable metabolites.     

Investigation of altered urinary metabolomic profiles of invasive ductal carcinoma of breast using targeted and untargeted approaches

Introduction

Invasive ductal carcinoma (IDC) is a type of breast cancer, usually detected in advanced stages due to its asymptomatic nature which ultimately leads to low survival rate. Identification of urinary metabolic adaptations induced by IDC to understand the disease pathophysiology and monitor therapy response would be a helpful approach in clinical settings. Moreover, its non-invasive and cost effective strategy better suited to minimize apprehension among high risk population.

Objective

This study aims toward investigating the urinary metabolic alterations of IDC by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches for the better understanding of the disease pathophysiology and monitoring therapy response.

Methods

Urinary metabolic alterations of IDC subjects (63) and control subjects (63) were explored by targeted (LC-MRM/MS) and untargeted (GC–MS) approaches. IDC specific urinary metabolomics signature was extracted by applying both univariate and multivariate statistical tools.

Results

Statistical analysis identified 39 urinary metabolites with the highest contribution to metabolomic alterations specific to IDC. Out of which, 19 metabolites were identified from targeted LC-MRM/MS analysis, while 20 were identified from the untargeted GC–MS analysis. Receiver operator characteristic (ROC) curve analysis evidenced 6 most discriminatory metabolites from each type of approach that could differentiate between IDC subjects and controls with higher sensitivity and specificity. Furthermore, metabolic pathway analysis depicted several dysregulated pathways in IDC including sugar, amino acid, nucleotide metabolism, TCA cycle etc.

Conclusions

Overall, this study provides valuable inputs regarding altered urinary metabolites which improved our knowledge on urinary metabolomic alterations induced by IDC. Moreover, this study identified several dysregulated metabolic pathways which offer further insight into the disease pathophysiology.

     

Metabolomics reveals stage-specific metabolic pathways of microbial communities in two-stage anaerobic fermentation of corn-stalk

Analysis of intracellular metabolites is essential to delineate metabolic pathways of microbial communities for evaluation and optimization of anaerobic fermentation processes. The metabolomics are reported for a microbial community during two stages of anaerobic fermentation of corn stalk in a biogas digester using GC–MS. Acetonitrile/methanol/water (2:2:1, by vol) was the best extraction solvent for microbial community analysis because it yielded the largest number of peaks (>200), the highest mean summed value of identified metabolites (23) and the best reproducibility with a coefficient of variation of 30 % among four different extraction methods. Inter-stage comparison of metabolite profiles showed increased levels of sugars and sugar alcohols during methanogenesis and fatty acids during acidogenesis. Identification of stage-specific metabolic pathways using metabolomics can therefore assist in monitoring and optimization of the microbial community for increased biogas production during anaerobic fermentation.     

Chromatographic profiling of Pancharishta at different stages of its development using HPTLC,HPLC, GC–MS and UPLC–MS

Pancharishta is the traditional Ayurvedic polyherbal formulation prepared by decoction of plant materials followed by fermentation for preservation and facilitation of extraction due to the production of alcohol. Since the preparation of pancharishta involves various steps. The aim of the current investigation was to carry out comparative metabolomics profiling at different stages of preparation for the understanding impact of different steps and ingredients. A decoction of 21 plant materials are main components in pancharishta formulations followed by fermentation and addition of other ingredients with or without fermentation yielded eight different formulations. The vacuum concentration of pancharishta samples yielded a semisolid mass of different formulations ranging from 8 to 37% w/v. The HPTLC fingerprinting analysis of samples was carried out in butanol: ethanol: 0.5% v/v ammonia (5:4:0.5, v/v/v). Derivatization with anisaldehyde-sulphuric acid showed the presence of two major peaks at R_f 0.29 and 0.35. The peak at R_f 0.29 is intense in a formulation containing 12 extra plant materials. Quantification of gallic acid, ellagic acid, tannic acid, kaemferol and quercetin were carried out on newly developed HPLC method using acetonitrile and 0.5% v/v formic acid with a gradient elution. A significant difference in their content was found in different formulations. Further, polar and nonpolar metabolites of pancharishtha were analyzed using UPLC–MS and GC–MS, respectively. GC–MS profiling results in the identification of 144 metabolites among them 26 are common metabolites at different stages. The UPLC–MS analysis resulted in the tentative identification of 43 metabolites. The results of UPLC–MS and GC–MS analysis were used for multivariate analysis using XLSTAT. Principal Component Analysis plot distributed all samples into four different clusters with two formulations each.     

GC/MS based metabolomics: development of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA)

Background  

The goal of metabolomics analyses is a comprehensive and systematic understanding of all metabolites in biological samples. Many useful platforms have been developed to achieve this goal. Gas chromatography coupled to mass spectrometry (GC/MS) is a well-established analytical method in metabolomics study, and 200 to 500 peaks are routinely observed with one biological sample. However, only ~100 metabolites can be identified, and the remaining peaks are left as "unknowns".     

Metabolomic and elemental analysis of camel and bovine urine by GC–MS and ICP–MS

Recent studies from the author’s laboratory indicated that camel urine possesses antiplatelet activity and anti-cancer activity which is not present in bovine urine. The objective of this study is to compare the volatile and elemental components of bovine and camel urine using GC–MS and ICP–MS analysis. We are interested to know the component that performs these biological activities. The freeze dried urine was dissolved in dichloromethane and then derivatization process followed by using BSTFA for GC–MS analysis. Thirty different compounds were analyzed by the derivatization process in full scan mode. For ICP–MS analysis twenty eight important elements were analyzed in both bovine and camel urine. The results of GC–MS and ICP–MS analysis showed marked difference in the urinary metabolites. GC–MS evaluation of camel urine finds a lot of products of metabolism like benzene propanoic acid derivatives, fatty acid derivatives, amino acid derivatives, sugars, prostaglandins and canavanine. Several research reports reveal the metabolomics studies on camel urine but none of them completely reported the pharmacology related metabolomics. The present data of GC–MS suggest and support the previous studies and activities related to camel urine.     

Baitmet,a computational approach for GC–MS library-driven metabolite profiling

Introduction

Current computational tools for gas chromatography—mass spectrometry (GC–MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual data reviewing. Metabolomics advent has fostered the development of public metabolite repositories containing mass spectra and retention indices, two orthogonal properties needed for metabolite identification. Such libraries can be used for library-driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC–MS non-targeted data analysis solutions that can eventually help to assess metabolite identities more efficiently.

Results

This paper introduces Baitmet, an integrated open-source computational tool written in R enclosing a complete workflow to perform high-throughput library-driven GC–MS profiling in complex samples. Baitmet capabilities were assayed in a metabolomics study involving 182 human serum samples where a set of 61 metabolites were profiled given a reference library.

Conclusions

Baitmet allows high-throughput and wide scope interrogation on the metabolic composition of complex samples analyzed using GC–MS via freely available spectral data. Baitmet is freely available at http://CRAN.R-project.org/package=baitmet.

     

Targeted metabolomic analysis of Escherichia coli by desorption electrospray ionization and extractive electrospray ionization mass spectrometry

Desorption electrospray ionization (DESI) was utilized to monitor the presence of targeted central carbon metabolites within bacterial cell extracts and the quench supernatant of Escherichia coli. The targeted metabolites were identified through tandem mass spectrometry (MS/MS) product ion scans using collision-induced dissociation in the negative ion mode. Picogram detection limits were achieved for a majority of the metabolites during MS/MS analysis of standard metabolite solutions. In a [U-(13)C]glucose pulse experiment, where uniformly labeled glucose was fed to E. coli, the corresponding fragment ions from labeled metabolites in extracts were generally observed. There was evidence of matrix effects including moderate suppression by other metabolites within the spectra of the labeled and unlabeled extracts. To improve the specificity and sensitivity of detection, optimized in situ ambient chemical reactions using DESI and extractive electrospray ionization (EESI) were carried out for targeted compounds. This study provides the first indication of the potential to perform in situ targeted metabolomics of a bacterial sample via ambient ionization mass spectrometry.     

Semi-automated non-target processing in GC?��?GC�CMS 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.     

Metabolic responses of Eucalyptus species to different temperature regimes

Species and hybrids of Eucalyptus are the world's most widely planted hardwood trees. They are cultivated across a wide range of latitudes and therefore environmental conditions. In this context, comprehensive metabolomics approaches have been used to assess how different temperature regimes may affect the metabolism of three species of Eucalyptus, E. dunnii, E. grandis and E. pellita.Young plants were grown for 53 d in the greenhouse and then transferred to growth chambers at 10°C, 20°C or30°C for another 7 d. In all three species the leaf chlorophyll content was positively correlated to temperature, and in E.pellita the highest temperature also resulted in a significant increase in stem biomass. Comprehensive metabolomics was performed using untargeted gas chromatography mass spectrometry(GC-MS) and liquid chromatography(LC)-MS.This approach enabled the comparison of the relativeabundance of 88 polar primary metabolites from GC-MS and625 semi-polar secondary metabolites from LC-MS. Using principal components analysis, a major effect of temperature was observed in each species which was larger than that resulting from the genetic background. Compounds mostly affected by temperature treatment were subsequently selected using partial least squares discriminant analysis and were further identified. These putative annotations indicated that soluble sugars and several polyphenols, including tannins, triterpenes and alkaloids were mostly influenced.     

Two birds with one stone: Doing metabolomics with your proteomics kit

Proteomic research facilities and laboratories are facing increasing demands for the integration of biological data from multiple ‘‐OMICS’ approaches. The aim to fully understand biological processes requires the integrated study of genomes, proteomes and metabolomes. While genomic and proteomic workflows are different, the study of the metabolome overlaps significantly with the latter, both in instrumentation and methodology. However, chemical diversity complicates an easy and direct access to the metabolome by mass spectrometry (MS). The present review provides an introduction into metabolomics workflows from the viewpoint of proteomic researchers. We compare the physicochemical properties of proteins and peptides with metabolites/small molecules to establish principle differences between these analyte classes based on human data. We highlight the implications this may have on sample preparation, separation, ionisation, detection and data analysis. We argue that a typical proteomic workflow (nLC‐MS) can be exploited for the detection of a number of aliphatic and aromatic metabolites, including fatty acids, lipids, prostaglandins, di/tripeptides, steroids and vitamins, thereby providing a straightforward entry point for metabolomics‐based studies. Limitations and requirements are discussed as well as extensions to the LC‐MS workflow to expand the range of detectable molecular classes without investing in dedicated instrumentation such as GC‐MS, CE‐MS or NMR.     

Gas chromatography-mass spectrometry (GC/MS) reveals urine metabolites associated to light and heavy infections by Schistosoma mansoni in mice

High-throughput profiling of metabolites has been used to identify metabolic changes in murine models as a response to the infection by the parasitic trematode Schistosoma. These investigations have contributed to our understanding on the pathogenesis of this tropical neglected disease, with a potential of helping diagnosis. Here, our study aimed to investigate the application of gas chromatography–mass spectrometry (GC/MS) on the profiling of urine metabolites from mice carrying infections by Schistosoma mansoni. Two larval infection doses created distinctive infection intensities in mice, whereby the heavily infected animals were found to release 25 times more eggs in faeces than lightly infected animals. Over 200 urine metabolites were identified from these animals by GC/MS, following two complementary derivatisation methods. A list of 14 individual metabolites with altered relative abundances between groups were identified. Most of the altered metabolites showed a trend of increased abundances in response to infection intensity, indicating host-specific metabolic alterations as a result of the disease. Hippurate, a metabolite which concentration is intimately modulated by the gut microbiota, was found to be highly correlated to infection intensity. Our study showed that urine metabolic profiling by GC/MS can distinguish non-infected animals from those carrying light and heavy infections by S. mansoni, revealing metabolites associated to the infection and providing insights on the pathogenesis of schistosomiasis.     

The MetabolomeExpress Project: enabling web-based processing,analysis and transparent dissemination of GC/MS metabolomics datasets

Background  

Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis.