Advances in the application of comprehensive two-dimensional gas chromatography in metabolomics.
Ontology highlight
ABSTRACT:
SUBMITTER: Keppler EAH
PROVIDER: S-EPMC6333419 | biostudies-literature | 2018 Dec
REPOSITORIES: biostudies-literature
Publications
Advances in the application of comprehensive two-dimensional gas chromatography in metabolomics.
Keppler Emily A Higgins EAH Jenkins Carrie L CL Davis Trenton J TJ Bean Heather D HD
Trends in analytical chemistry : TRAC 20181024
Due to excellent separation capacity for complex mixtures of chemicals, comprehensive two-dimensional gas chromatography (GC × GC) is being utilized with increasing frequency for metabolomics analyses. This review describes recent advances in GC × GC method development for metabolomics, organismal sampling techniques compatible with GC × GC, metabolomic discoveries made using GC × GC, and recommendations and best practices for collecting and reporting GC × GC metabolomics data. ...[more]
Similar Datasets
Project description:The diverse characteristics and large number of entities make metabolite separation challenging in metabolomics. To date, there is not a singular instrument capable of analyzing all types of metabolites. In order to achieve a better separation for higher peak capacity and accurate metabolite identification and quantification, we integrated GC × GC-MS and parallel 2DLC-MS for analysis of polar metabolites. To test the performance of the developed system, 13 rats were fed different diets to form two animal groups. Polar metabolites extracted from rat livers were analyzed by GC × GC-MS, parallel 2DLC-MS (-) and parallel 2DLC-MS (+), respectively. By integrating all data together, 58 metabolites were detected with significant change in their abundance levels between groups (p≤ 0.05). Of the 58 metabolites, three metabolites were detected in two platforms and two in all three platforms. Manual examination showed that discrepancy of metabolite regulation measured by different platforms was mainly caused by the poor shape of chromatographic peaks resulting from low instrument response. Pathway analysis demonstrated that integrating the results from multiple platforms increased the confidence of metabolic pathway assignment.
| S-EPMC6677244 | biostudies-literature
Project description:MotivationDue to the high complexity of metabolome, the comprehensive 2D gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) is considered as a powerful analytical platform for metabolomics study. However, the applications of GC×GC-TOF MS in metabolomics are not popular owing to the lack of bioinformatics system for data analysis.ResultsWe developed a computational platform entitled metabolomics profiling pipeline (MetPP) for analysis of metabolomics data acquired on a GC×GC-TOF MS system. MetPP can process peak filtering and merging, retention index matching, peak list alignment, normalization, statistical significance tests and pattern recognition, using the peak lists deconvoluted from the instrument data as its input. The performance of MetPP software was tested with two sets of experimental data acquired in a spike-in experiment and a biomarker discovery experiment, respectively. MetPP not only correctly aligned the spiked-in metabolite standards from the experimental data, but also correctly recognized their concentration difference between sample groups. For analysis of the biomarker discovery data, 15 metabolites were recognized with significant concentration difference between the sample groups and these results agree with the literature results of histological analysis, demonstrating the effectiveness of applying MetPP software for disease biomarker discovery.AvailabilityThe source code of MetPP is available at http://metaopen.sourceforge.netContactxiang.zhang@louisville.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3702250 | biostudies-literature
Project description:BackgroundComprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful technique which has gained increasing attention over the last two decades. The GC × GC-MS provides much increased separation capacity, chemical selectivity and sensitivity for complex sample analysis and brings more accurate information about compound retention times and mass spectra. Despite these advantages, the retention times of the resolved peaks on the two-dimensional gas chromatographic columns are always shifted due to experimental variations, introducing difficulty in the data processing for metabolomics analysis. Therefore, the retention time variation must be adjusted in order to compare multiple metabolic profiles obtained from different conditions.ResultsWe developed novel peak alignment algorithms for both homogeneous (acquired under the identical experimental conditions) and heterogeneous (acquired under the different experimental conditions) GC × GC-MS data using modified Smith-Waterman local alignment algorithms along with mass spectral similarity. Compared with literature reported algorithms, the proposed algorithms eliminated the detection of landmark peaks and the usage of retention time transformation. Furthermore, an automated peak alignment software package was established by implementing a likelihood function for optimal peak alignment.ConclusionsThe proposed Smith-Waterman local alignment-based algorithms are capable of aligning both the homogeneous and heterogeneous data of multiple GC × GC-MS experiments without the transformation of retention times and the selection of landmark peaks. An optimal version of the SW-based algorithms was also established based on the associated likelihood function for the automatic peak alignment. The proposed alignment algorithms outperform the literature reported alignment method by analyzing the experiment data of a mixture of compound standards and a metabolite extract of mouse plasma with spiked-in compound standards.
| S-EPMC3133553 | biostudies-literature
Project description:Microbial metabolomics is a challenge strategy that allows a comprehensive analysis of metabolites within a microorganism and may support a new approach in microbial research, including the microbial diagnosis. Thus, the aim of this research was to in-depth explore a metabolomics strategy based on the use of an advanced multidimensional gas chromatography for the comprehensive mapping of cellular metabolites of C. albicans and non-C. albicans (C. glabrata and C. tropicalis) and therefore contributing for the development of a comprehensive platform for fungal detection management and for species distinction in early growth times (6 h). The volatile fraction comprises 126 putatively identified metabolites distributed over several chemical families: acids, alcohols, aldehydes, hydrocarbons, esters, ketones, monoterpenic and sesquiterpenic compounds, norisoprenoids, phenols and sulphur compounds. These metabolites may be related with different metabolic pathways, such as amino acid metabolism and biosynthesis, fatty acids metabolism, aromatic compounds degradation, mono and sesquiterpenoid synthesis and carotenoid cleavage. These results represent an enlargement of ca. 70% of metabolites not previously reported for C. albicans, 91% for C. glabrata and 90% for C. tropicalis. This study represents the most detailed study about Candida species exometabolome, allowing a metabolomic signature of each species, which signifies an improvement towards the construction of a Candida metabolomics platform whose application in clinical diagnostics can be crucial to guide therapeutic interventions.
| S-EPMC7760324 | biostudies-literature
Project description:Candida antarctica lipase A (CALA) was applied for the chemo-selective enzymatic transesterification of terpene and phenyl alcohols in 35 different essential oil samples. Comprehensive two-dimensional gas chromatography with mass spectrometry (GC×GC‒MS) analysis enabled the separation and tentative identification of a cohort of 125 compounds, allowing the instant visualisation of the reaction process changes, amid the complex chemical background of the samples. The results indicate that 42 out of 79 alcohols so-identified were fully or partially esterified within 48 h of reaction, with primary alcohols being the substrates of preference of the enzyme (90-100% conversion), followed by secondary alcohols (mostly ~ 80-100% conversion). No significant conversion of tertiary alcohols and phenols was observed using the tested conditions. Overall, the enzyme's performance was consistent for primary alcohol substrates identified in multiple samples of different compositions. The observed selectivity, efficiency, robustness, scalability (enzyme/substrate working concentration ratio > 1:160), potential reusability, mild reaction conditions, and other factors make this process a greener and more sustainable alternative for industry applications, particularly for the manufacture of novel flavours and fragrances.
| S-EPMC10184066 | biostudies-literature
Project description:Two-dimensional (2D) gas chromatography (GC) provides enhanced vapor separation capabilities in contrast to conventional one-dimensional GC and is useful for the analysis of highly complex chemical samples. We developed a microfabricated flow-restricted pneumatic modulator (FRPM) for portable comprehensive 2D micro-GC (μGC), which enables rapid 2D injection and separation without compromising the 1D separation speed and eluent peak profiles. 2D injection characteristics such as injection peak width and peak height were fully characterized by using flow-through micro-photoionization detectors (μPIDs) at the FRPM inlet and outlet. A 2D injection peak width of ~25 ms could be achieved with a 2D/1D flow rate ratio over 10. The FRPM was further integrated with a 0.5-m long 2D μcolumn on the same chip, and its performance was characterized. Finally, we developed an automated portable comprehensive 2D μGC consisting of a 10 m OV-1 1D μcolumn, an integrated FRPM with a built-in 0.5 m polyethylene glycol 2D μcolumn, and two μPIDs. Rapid separation of 40 volatile organic compounds in ~5 min was demonstrated. A hybrid 2D contour plot was constructed by using both 1D and 2D chromatograms obtained with the two μPIDs at the end of the 1D and 2D μcolumns, which was enabled by the presence of the flow resistor in the FRPM.
| S-EPMC9622416 | biostudies-literature
Project description:The absolute need to improve the separating power of liquid chromatography, especially for multi-constituent biological samples, is becoming increasingly evident. In response, over the past few years, there has been a great deal of interest in the development of two-dimensional liquid chromatography (2DLC). Just as 1DLC is preferred to 1DGC based on its compatibility with biological materials we believe that ultimately 2DLC will be preferred to the much more highly developed 2DGC for such samples. The huge advantage of 2D chromatographic techniques over 1D methods is inherent in the tremendous potential increase in peak capacity (resolving power). This is especially true of comprehensive 2D chromatography wherein it is possible, under ideal conditions, to obtain a total peak capacity equal to the product of the peak capacities of the first and second dimension separations. However, the very long timescale (typically several hours to tens of hours) of comprehensive 2DLC is clearly its chief drawback. Recent advances in the use of higher temperatures to speed up isocratic and gradient elution liquid chromatography have been used to decrease the time needed to do the second dimension LC separation of 2DLC to about 20s for a full gradient elution run. Thus, fast, high temperature LC is becoming a very promising technique. Peak capacities of over 2000 and rates of peak capacity production of nearly 1 peak/s have been achieved. In consequence, many real samples showing more than 200 peaks with signal to noise ratios of better than 10:1 have been run in total times of under 30 min. This report is not intended to be a comprehensive review of 2DLC, but is deliberately focused on the issues involved in doing fast 2DLC by means of elevating the column temperature; however, many issues of broader applicability will be discussed.
| S-EPMC3205947 | biostudies-literature
Project description:The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses.
| S-EPMC4448113 | biostudies-literature
Project description:Comprehensive two-dimensional (2D) gas chromatography (GC×GC) coupled to mass spectrometry (MS, GC×GC-MS), which enhances selectivity compared to GC-MS analysis, can be used for non-directed analysis (non-target screening) of environmental samples. Additional tools that aid in identifying unknown compounds are needed to handle the large amount of data generated. These tools include retention indices for characterizing relative retention of compounds and prediction of such. In this study, two quantitative structure-retention relationship (QSRR) approaches for prediction of retention times (1tR and 2tR) and indices (linear retention indices (LRIs) and a new polyethylene glycol-based retention index (PEG-2I)) in GC × GC were explored, and their predictive power compared. In the first method, molecular descriptors combined with partial least squares (PLS) analysis were used to predict times and indices. In the second method, the commercial software package ChromGenius (ACD/Labs), based on a "federation of local models," was employed. Overall, the PLS approach exhibited better accuracy than the ChromGenius approach. Although average errors for the LRI prediction via ChromGenius were slightly lower, PLS was superior in all other cases. The average deviations between the predicted and the experimental value were 5% and 3% for the 1tR and LRI, and 5% and 12% for the 2tR and PEG-2I, respectively. These results are comparable to or better than those reported in previous studies. Finally, the developed model was successfully applied to an independent dataset and led to the discovery of 12 wrongly assigned compounds. The results of the present work represent the first-ever prediction of the PEG-2I. Graphical abstract ᅟ.
| S-EPMC6244764 | biostudies-literature
Project description:MotivationComprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS) brings much increased separation capacity, chemical selectivity and sensitivity for metabolomics and provides more accurate information about metabolite retention times and mass spectra. However, there is always a shift of retention times in the two columns that makes it difficult to compare metabolic profiles obtained from multiple samples exposed to different experimental conditions.ResultsThe existing peak alignment algorithms for GC × GC-MS data use the peak distance and the spectra similarity sequentially and require predefined either distance-based window and/or spectral similarity-based window. To overcome the limitations of the current alignment methods, we developed an optimal peak alignment using a novel mixture similarity by employing the peak distance and the spectral similarity measures simultaneously without any variation windows. In addition, we examined the effect of the four different distance measures such as Euclidean, Maximum, Manhattan and Canberra distances on the peak alignment. The performance of our proposed peak alignment algorithm was compared with the existing alignment methods on the two sets of GC × GC-MS data. Our analysis showed that Canberra distance performed better than other distances and the proposed mixture similarity peak alignment algorithm prevailed against all literature reported methods.AvailabilityThe data and software mSPA are available at http://stage.louisville.edu/faculty/x0zhan17/software/software-development.
| S-EPMC3106184 | biostudies-literature