Metabolomics by Gas Chromatography–Mass Spectrometry: Combined Targeted and Untargeted Profiling (original) (raw)
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Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry
TrAC Trends in Analytical Chemistry, 2008
Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most frequently used tools for profiling primary metabolites. Instruments are mature enough to run large sequences of samples; novel advancements increase the breadth of compounds that can be analyzed, and improved algorithms and databases are employed to capture and utilize biologically relevant information. Around half the published reports on metabolite profiling by GC-MS focus on biological problems rather than on methodological advances. Applications span from comprehensive analysis of volatiles to assessment of metabolic fluxes for bioengineering. Method improvements emphasize extraction procedures, evaluations of quality control of GC-MS in comparison to other techniques and approaches to data processing. Two major challenges remain: rapid annotation of unknown peaks; and, integration of biological background knowledge aiding data interpretation.
Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives
Metabolomics, 2010
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.
Talanta
Two-dimensional gas chromatography mass spectrometry (GCxGC-MS) is utilized to an increasing extent in biomedical metabolomics. Here, we established and adapted metabolite extraction and derivatization protocols for cell/tissue biopsy, serum and urine samples according to their individual properties. GCxGC-MS analysis revealed detection of~600 molecular features from which 165 were characterized representing different classes such as amino acids, fatty acids, lipids, carbohydrates, nucleotides and small polar components of glycolysis and the Krebs cycle using electron impact (EI) spectrum matching and validation using external standard compounds. Advantages of two-dimensional gas chromatography based resolution were demonstrated by optimizing gradient length and separation through modulation between the first and second column, leading to a marked increase in metabolite identification due to improved separation as exemplified for lactate versus pyruvate, talopyranose versus methyl palmitate and inosine versus docosahexaenoic acid. Our results demonstrate that GCxGC-MS represents a robust metabolomics platform for discovery and targeted studies that can be used with samples derived from the clinic.
Journal of Chromatography B, 2008
The metabolome is characterized by a large number of molecules exhibiting a high diversity of chemical structures and abundances, requiring complementary analytical platforms to reach its extensive coverage. Among them, atmospheric pressure ionization mass spectrometry (API-MS)-based technologies, and especially those using electrospray ionization are now very popular. In this context, this review deals with strengths, limitations and future trends in the identification of signals highlighted by API-MS-based metabolomics. It covers the identification process from the determination of the molecular mass and/or its elemental composition to the confirmation of structural hypotheses. Furthermore, some tools that were developed in order to address the MS signal redundancy and some approaches that could facilitate identification by improving the visualization and organization of complex data sets are also reported and discussed.
Organization of GC/MS and LC/MS metabolomics data into chemical libraries
Journal of Cheminformatics, 2010
Background: Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MS n detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Quantify Individual Components in a Sample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. Results: LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. Conclusion: The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.
From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics
2007
Mass spectrometry (MS) is an established technology in drug metabolite analysis and is now expanding into endogenous metabolite research. Its utility derives from its wide dynamic range, reproducible quantitative analysis, and the ability to analyze biofluids with extreme molecular complexity. The aims of developing mass spectrometry for metabolomics range from understanding basic biochemistry to biomarker discovery and the structural characterization of physiologically important metabolites. In this review, we will discuss the techniques involved in this exciting area and the current and future applications of this field. . Ultrahigh performance liquid chromatography (UPLC) utilizes columns with smaller particle size packing material (1.4-1.7 µm) than traditional columns and can enhance several aspects of chromatography in a metabolomics context. (1) Separation of metabolites is improved, decreasing ion suppression and in turn improving data interpretability (2) Signal to Noise (S/N) is improved due to narrower peak widths allowing for increased peak capacity and improved accuracy and sensitivity. (3) Sample run time is decreased dramatically allowing for faster sample throughput.
Identifying and quantifying metabolites by scoring peaks of GC-MS data
BMC Bioinformatics, 2014
Background: Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS. Results: Here we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice. Conclusions: Identification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in a R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.
Automated Pipeline for De Novo Metabolite Identification Using Mass-Spectrometry-Based Metabolomics
Analytical Chemistry, 2013
Metabolite identification is one of the biggest bottlenecks in metabolomics. Identifying human metabolites poses experimental, analytical, and computational challenges. Here we present a pipeline of previously developed cheminformatic tools and demonstrate how it facilitates metabolite identification using solely LC/MS n data. These tools process, annotate, and compare MS n data, and propose candidate structures for unknown metabolites either by identity assignment of identical mass spectral trees or by de novo identification using substructures of similar trees. The working and performance of this metabolite identification pipeline is demonstrated by applying it to LC/MS n data of urine samples. From human urine, 30 MS n trees of unknown metabolites were acquired, processed, and compared to a reference database containing MS n data of known metabolites. From these 30 unknowns, we could assign a putative identity for 10 unknowns by finding identical fragmentation trees. For 11 unknowns no similar fragmentation trees were found in the reference database. On the basis of elemental composition only, a large number of candidate structures/identities were possible, so these unknowns remained unidentified. The other 9 unknowns were also not found in the database, but metabolites with similar fragmentation trees were retrieved. Computer assisted structure elucidation was performed for these 9 unknowns: for 4 of them we could perform de novo identification and propose a limited number of candidate structures, and for the other 5 the structure generation process could not be constrained far enough to yield a small list of candidates. The novelty of this work is that it allows de novo identification of metabolites that are not present in a database by using MS n data and computational tools. We expect this pipeline to be the basis for the computer-assisted identification of new metabolites in future metabolomics studies, and foresee that further additions will allow the identification of even a larger fraction of the unknown metabolites.