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.     

Short column gas chromatography–mass spectrometry and principal component analysis for the identification of coeluted substances in doping control analysis

The identification of four doping control substances in an artificial mixture, using short column gas chromatography–mass spectrometry (GC–MS) analysis was examined. Two chromatographic peaks were recorded in the chromatogram, using a short capillary column (1.8 m) at an oven temperature of 180°C. The first peak was associated with a mixture of a solvent derivative and an artifact. The second one corresponded to the mixture of four control substances. Principal component analysis was applied on a selected GC–MS data set of the latter peak to determine clear full spectra of pure substances from mixture spectra. The time of GC–MS analysis was significantly reduced to less than 1 min from 30 min which is a typical GC–MS analysis time, using standard methods of doping control analysis.     

Ryanodine receptors, a family of intracellular calcium ion channels, are expressed throughout early vertebrate development

Background

We developed a new version of the open source software package Peptrix that can yet compare large numbers of Orbitrap? LC-MS data. The peptide profiling results for Peptrix on MS1 spectra were compared with those obtained from a small selection of open source and commercial software packages: msInspect, Sieve? and Progenesis?. The properties compared in these packages were speed, total number of detected masses, redundancy of masses, reproducibility in numbers and CV of intensity, overlap of masses, and differences in peptide peak intensities. Reproducibility measurements were taken for the different MS1 software applications by measuring in triplicate a complex peptide mixture of immunoglobulin on the Orbitrap? mass spectrometer. Values of peptide masses detected from the high intensity peaks of the MS1 spectra by peptide profiling were verified with values of the MS2 fragmented and sequenced masses that resulted in protein identifications with a significant score.

Findings

Peptrix finds about the same number of peptide features as the other packages, but peptide masses are in some cases approximately 5 to 10 times less redundant present in the peptide profile matrix. The Peptrix profile matrix displays the largest overlap when comparing the number of masses in a pair between two software applications. The overlap of peptide masses between software packages of low intensity peaks in the spectra is remarkably low with about 50% of the detected masses in the individual packages. Peptrix does not differ from the other packages in detecting 96% of the masses that relate to highly abundant sequenced proteins. MS1 peak intensities vary between the applications in a non linear way as they are not processed using the same method.

Conclusions

Peptrix is capable of peptide profiling using Orbitrap? files and finding differential expressed peptides in body fluid and tissue samples. The number of peptide masses detected in Orbitrap? files can be increased by using more MS1 peptide profiling applications, including Peptrix, since it appears from the comparison of Peptrix with the other applications that all software packages have likely a high false negative rate of low intensity peptide peaks (missing peptides).     

GC–MS determination of creatinine in human biological fluids as pentafluorobenzyl derivative in clinical studies and biomonitoring: Inter-laboratory comparison in urine with Jaffé, HPLC and enzymatic assays

In consideration of its relatively constant urinary excretion rate, creatinine in urine is a useful biochemical parameter to correct the urinary excretion rate of endogenous and exogenous biomolecules. Assays based on the reaction of creatinine and picric acid first reported by Jaffé in 1886 still belong to the most frequently used laboratory approaches for creatinine measurement in urine. Further analytical methods for creatinine include HPLC–UV, GC–MS, and LC–MS and LC–MS/MS approaches. In the present article we report on the development, validation and biomedical application of a new GC–MS method for the reliable quantitative determination of creatinine in human urine, plasma and serum. This method is based on the derivatization of creatinine (d₀-Crea) and the internal standard [methyl-trideutero]creatinine (d₃-Crea) with pentafluorobenzyl (PFB) bromide in the biological sample directly or after dilution with phosphate buffered saline, extraction of the reaction products with toluene and quantification in 1-μl aliquots of the toluene extract by selected-ion monitoring of m/z 112 for d₀-Crea-PFB and m/z 115 for d₃-Crea-PFB in the electron-capture negative-ion chemical ionization mode. The limit of detection of the method is 100 amol of creatinine. In an inter-laboratory study on urine samples from 100 healthy subjects, the GC–MS method was used to test the reliability of currently used Jaffé, enzymatic and HPLC assays in clinical and occupational studies. The results of the inter-laboratory study indicate that all three tested methods allow for satisfactory quantification of creatinine in human urine. The GC–MS method is suitable for use as a reference method for urinary creatinine in humans. In serum, creatine was found to contribute to creatinine up to 20% when measured by the present GC–MS method. The application of the GC–MS method can be extended to other biological samples such as saliva.     

Simultaneous gas chromatography-tandem mass spectrometry quantification of symmetric and asymmetric dimethylarginine in human urine

Previously, we demonstrated the utility of a gas chromatography–tandem mass spectrometry (GC–MS/MS) method for the quantitative determination of asymmetric dimethylarginine (ADMA) in biological samples. Here we report the extension of this method to symmetric dimethylarginine (SDMA) in human urine. SDMA and ADMA were simultaneously quantitated in urine by using their in situ prepared trideuteromethyl esters as internal standards. The GC–MS/MS method was validated for SDMA and ADMA in spot urine samples of 19 healthy adults. In these samples, the creatinine-corrected excretion rate was 3.23 ± 0.63 μmol/mmol for SDMA and 3.14 ± 0.98 μmol/mmol for ADMA.     

Comparison of gas chromatography–mass spectrometry and gas chromatography–combustion–isotope ratio mass spectrometry analysis for in vivo estimates of metabolic fluxes

Gas chromatography–mass spectrometry (GC–MS) was compared with gas chromatography–combustion–isotope ratio mass spectrometry (GC–C–IRMS) for measurements of cholesterol ¹³C enrichment after infusion of labeled precursor ([¹³C_1,2]acetate). Paired results were significantly correlated, although GC–MS was less accurate than GC–C–IRMS for higher enrichments. Nevertheless, only GC–MS was able to provide information on isotopologue distribution, bringing new insights to lipid metabolism. Therefore, we assessed the isotopologue distribution of cholesterol in humans and dogs known to present contrasted cholesterol metabolic pathways. The labeled tracer incorporation was different in both species, highlighting the subsidiarity of GC–MS and GC–C–IRMS to analyze in vivo stable isotope studies.     

Allelematch: an R package for identifying unique multilocus genotypes where genotyping error and missing data may be present

We present allelematch, an R package, to automate the identification of unique multilocus genotypes in data sets where the number of individuals is unknown, and where genotyping error and missing data may be present. Such conditions commonly occur in noninvasive sampling protocols. Output from the software enables a comparison of unique genotypes and their matches, and facilitates the review of differences between profiles. The software has a variety of applications in molecular ecology, and may be valuable where a large number of samples must be processed, unique genotypes identified, and repeated observations made over space and time. We used simulations to assess the performance of allelematch and found that it can reliably and accurately determine the correct number of unique genotypes (± 3%) across a broad range of data set properties. We found that the software performs with highest accuracy when genotyping error is below 4%. The R package is available from the Comprehensive R Archive Network (http://cran.r-project.org/). Supplementary documentation and tutorials are provided.     

Identifying and quantifying metabolites by scoring peaks of GC-MS data

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0374-2) contains supplementary material, which is available to authorized users.     

SIMSI-Transfer: Software-Assisted Reduction of Missing Values in Phosphoproteomic and Proteomic Isobaric Labeling Data Using Tandem Mass Spectrum Clustering

Isobaric stable isotope labeling techniques such as tandem mass tags (TMTs) have become popular in proteomics because they enable the relative quantification of proteins with high precision from up to 18 samples in a single experiment. While missing values in peptide quantification are rare in a single TMT experiment, they rapidly increase when combining multiple TMT experiments. As the field moves toward analyzing ever higher numbers of samples, tools that reduce missing values also become more important for analyzing TMT datasets. To this end, we developed SIMSI-Transfer (Similarity-based Isobaric Mass Spectra 2 [MS2] Identification Transfer), a software tool that extends our previously developed software MaRaCluster (© Matthew The) by clustering similar tandem MS2 from multiple TMT experiments. SIMSI-Transfer is based on the assumption that similarity-clustered MS2 spectra represent the same peptide. Therefore, peptide identifications made by database searching in one TMT batch can be transferred to another TMT batch in which the same peptide was fragmented but not identified. To assess the validity of this approach, we tested SIMSI-Transfer on masked search engine identification results and recovered >80% of the masked identifications while controlling errors in the transfer procedure to below 1% false discovery rate. Applying SIMSI-Transfer to six published full proteome and phosphoproteome datasets from the Clinical Proteomic Tumor Analysis Consortium led to an increase of 26 to 45% of identified MS2 spectra with TMT quantifications. This significantly decreased the number of missing values across batches and, in turn, increased the number of peptides and proteins identified in all TMT batches by 43 to 56% and 13 to 16%, respectively.     

Automated preparation and analysis of barbiturates in human urine using the combined system of PrepStation and gas chromatography–mass spectrometry

A system for an automatic sample preparation procedure followed by on-line injection of the sample extract into a gas chromatography–mass spectrometry (GC–MS) system was developed for the simultaneous analysis of seven barbiturates in human urine. Sample clean-up was performed by a solid-phase extraction (SPE) on a C₁₈ disposable cartridge. A SPE cartridge was preconditioned with methanol and 0.1 M phosphate buffer. After loading a 1.5 ml volume of a urine sample into the SPE cartridge, the cartridge was washed with 2.5 ml of methanol–water (1:9, v/v). Barbiturates were eluted with 1.0 ml of chloroform–isopropanol (3:1, v/v) from the cartridge. The eluate (1 μl) was injected into a GC–MS system. The calibration curves, using an internal standard method, demonstrated a good linearity throughout the concentration range from 0.02 to 10 μg/ml for all barbiturates extracted. The proposed method was applied to several clinical cases. The total analysis time for 20 samples was approximately 14 h.     

Simultaneous determination of amitraz and its metabolite residue in food animal tissues by gas chromatography-electron capture detector and gas chromatography–mass spectrometry with accelerated solvent extraction

A new method has been developed for determination and confirmation of amitraz and its main metabolite, 2,4-dimethylaniline, in food animal tissues using gas chromatography-electron capture detector (GC-ECD) and gas chromatography–mass spectrometry detector (GC–MS). This method is based on a new extraction procedure using accelerated solvent extraction (ASE). It consists of an n-hexane/methanol extraction step, a cleaning-up step by BakerBond octadecyl C₁₈ silica bonded cartridge, hydrolysis and derivatization to 2,4-dimethyl-7-F-butyramide for GC-ECD analysis. For confirmation using GC–MS, hydrolysis and derivatization were not needed. Parameters for extraction pressure, temperature and cycle of ASE, clean-up, derivatization and analysis procedure have been optimized. Spike recoveries from 50 to 300 μg/kg levels were found to be between 72.4 and 101.3% with relative standard deviation less than 11.5% in GC-ECD, from 5 to 20 μg/kg levels were found to be between 77.4 and 107.1% with relative standard deviation less than 11.6% in GC–MS. The LOD and LOQ are 5 and 10 μg/kg, respectively, for these two analytes using GC-ECD. For GC–MS, LOD and LOQ were 2 and 5 μg/kg, respectively. The rapid and reliable method can be used for characterization and quantification of residues of amitraz and its main metabolite, 2,4-dimethylaniline, in liver and kidney samples of swine, sheep and bovine.     

Essential oil of Ayapana triplinervis from Reunion Island: A good natural source of thymohydroquinone dimethyl ether

Three specimens of Ayapana triplinervis (Vahl) R.M. King & H. Rob from Reunion Island (Indian Ocean) collected at two distant locations (North of the island; samples 1 and 2, South of the island; sample 3), in different growth phases (flowering; samples 1 and 3, vegetative; sample 2) were investigated for their leaf essential oil composition. This study reports the chemical character of this species on the island and investigates the relationship between essential oil composition, developmental stage and geographic location. Analysis by GC–FID and GC–MS enabled us to identify and quantify a total of 39 constituents accounting for 97.1–98.0% of the oils. The three essential oil samples, all obtained by hydrodistillation, showed a high percentage of the aromatic compound thymohydroquinone dimethyl ether (89.9–92.8%). All other minor components remained more or less unchanged both qualitatively and quantitatively with respect to the stage of growth. On the contrary, variations were observed with geographic distribution. The geographical variation of the chemical composition of the volatile oil of A. triplinervis from several sites in the world is also briefly discussed.     

Enhancement of ovarian tumor classification by improved reproducibility in matrix-assisted laser desorption/ionization time-of-flight mass spectrometry of serum glycans

The serum N-glycome is a promising source of biomarker discovery. Matrix-assisted laser desorption/ionization time-of-flight (MALDI–TOF) mass spectrometry (MS) profiling of serum N-glycans was attempted for differentiating borderline ovarian tumor from benign cases, for which a low data spread is essential. An experimental protocol using matrix-prespotted MALDI plates and fast vacuum drying of the loaded N-glycan samples was developed, thereby minimizing the intensity variations in the replicates to an average relative standard deviation (RSD) of 3.96% for the highest N-glycan peak (m/z 1485.53) of the Sigma–Aldrich serum standard. When applied to sera of ovarian tumors, this procedure exhibited an average RSD of 5.74% for m/z 1485.53 and of 7.28% for all MS peaks. This improved reproducibility combined with the OVA-Beyond^® screening software resulted in 75.1% and 79.4% correct classification for benign and borderline tumor samples, respectively, while the classification rates by the conventional ovarian tumor marker CA-125 were 54.4% and 53.1%, respectively. Both true positive rate and true negative rate fluctuated with small numbers of markers and converged as the number of markers increased. Cross-validations were performed in comparison with CA-125. These results suggest that our optimized process for MALDI–TOF MS of the serum glycome has a great potential for the screening of early stage ovarian cancer.     

Quantitative analysis of polychlorinated biphenyls, organochlorine insecticides, polycyclic aromatic hydrocarbons, polychlorinated hydrocarbons and polynitrohydrocarbons in spiked samples of soil, water and plasma by selected-ion monitoring gas chromatography–mass spectrometry

A broad range of pollutants such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated hydrocarbons (PCHs), polynitrohydrocarbons (PNHs), polychlorinated biphenyls (PCBs) and organochlorine (OCs) insecticides were simultaneously analyzed in spiked soil, water or plasma samples by using gas chromatography–mass spectrometry (GC–MS). Water and plasma samples containing the pollutants were extracted by a solid-phase extraction (SPE) method using florisil columns. The soil samples, fortified with the toxicants, were extracted with water, methanol or dichloromethane (DCM). The water extract was processed by the SPE method. The methanol and DCM samples were dried, dissolved in acetonitrile and subjected to the SPE extraction. The extracted samples were analyzed by GC–MS programmed to monitor selected ions. The deuterium labelled compounds were used as the internal standards. The chromatographic profile of total ions indicated complete separation of some compounds such as isophorone, naphthalene, all PCBs, most OC insecticides and PNHs; high M_r PAHs and some PCHs were partially or incompletely separated. The chromatographic profile of individual ion indicated good separation of each ion. The minimum detection limit ranged from 1 to 4 pg injected when 1 or 2 ions were monitored or from 20 to 200 pg injected when 20 ions were monitored. The SPE method that provided 60–105% recovery of pollutants from water samples, provided only 2–60% recovery from plasma samples. This may be due to the binding of pollutants to plasma proteins. Water recovered 1–30%, while methanol or DCM recovered 65–100% of the pollutants added to the soil samples. The use of internal standards corrected for the loss of pollutants from plasma or soil.     

A targeted metabolomics approach toward understanding metabolic variations in rice under pesticide stress

Diazinon insecticide is widely applied throughout rice (Oryza sativa L.) fields in Iran. However, concerns are now being raised about its potential adverse impacts on rice fields. In this study, a time-course metabolic change in rice plants was investigated after diazinon treatment using gas chromatography–mass spectrometry (GC–MS), and subsequently the statistical strategy of random forest (RF) was performed in order to find the stress-associated effects. According to the results, a wide range of metabolites were dynamically varied as a result of the plant response to diazinon such as biosynthesis and metabolism of sugars, amino acids, organic acids, and phenylpropanoids, all correlating with the exposure time. Plant response was involved in multiple metabolic pathways, most of which were correlated with the exposure time. In this study, RF was explored as a potential multivariate method for GC–MS analysis of metabolomics data of rice (O. sativa L.) plants under diazinon stress; more than 31 metabolites were quantitatively determined, and time-course metabolic response of the plant during different days after treatment was measured. Results demonstrated RF as a potential multivariate method for GC–MS analysis of changes in plant metabolome under insecticide stress.     

Identification of metabolites from liquid chromatography–coulometric array detection profiling: Gas chromatography–mass spectrometry and refractionation provide essential information orthogonal to LC–MS/microNMR

Liquid chromatography–coulometric array detection (LC–EC) is a sensitive, quantitative, and robust metabolomics profiling tool that complements the commonly used mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based approaches. However, LC–EC provides little structural information. We recently demonstrated a workflow for the structural characterization of metabolites detected by LC–EC profiling combined with LC–electrospray ionization (ESI)–MS and microNMR. This methodology is now extended to include (i) gas chromatography (GC)–electron ionization (EI)–MS analysis to fill structural gaps left by LC–ESI–MS and NMR and (ii) secondary fractionation of LC-collected fractions containing multiple coeluting analytes. GC–EI–MS spectra have more informative fragment ions that are reproducible for database searches. Secondary fractionation provides enhanced metabolite characterization by reducing spectral overlap in NMR and ion suppression in LC–ESI–MS. The need for these additional methods in the analysis of the broad chemical classes and concentration ranges found in plasma is illustrated with discussion of four specific examples: (i) characterization of compounds for which one or more of the detectors is insensitive (e.g., positional isomers in LC–MS, the direct detection of carboxylic groups and sulfonic groups in ¹H NMR, or nonvolatile species in GC–MS), (ii) detection of labile compounds, (iii) resolution of closely eluting and/or coeluting compounds, and (iv) the capability to harness structural similarities common in many biologically related, LC–EC-detectable compounds.     

Urinary organic acid screening by solid-phase microextraction of the methyl esters

We developed a new sample preparation method for profiling organic acids in urine by GC or GC–MS. The method includes derivatisation of the organic acids directly in the aqueous urine using trimethyloxonium tetrafluoroborate as a methylating agent, extraction of the organic acid methyl esters from the urine by solid-phase microextraction, using a polyacrylate fiber with a thickness of 85 μm and transfer of the methyl esters into the GC or the GC–MS instrument. Desorption of the analytes takes place in the heated injection port. The proposed sample preparation is very simple. There is no need for any evaporation step and for the use of an organic solvent. The risk of contamination and the loss of analytes are minimized. The total sample preparation time prior to GC or GC–MS analysis is about 40 min, and therefore more rapid than other sample preparation procedures. The urinary organic acids are well separated by GC and 29 substances are identified by GC–MS.     

Methyl vinyl ketone—Identification and quantification of adducts to N-terminal valine in human hemoglobin

Adducts to N-terminal valines in Hb have been shown useful as biomarkers of exposure to electrophilic compounds. Adducts from many compounds have earlier been measured with a modified Edman degradation method using a GC–MS/MS method. A recently developed method, the adduct FIRE procedure™, adopted for analysis by LC–MS/MS, has been applied in this study. With this method a fluorescein isothiocyanate (FITC) reagent is used to measure adducts (R) from electrophiles with a modified Edman procedure. By using LC–MS/MS in product ion scan mode, a new peak was identified and the obtained MS data indicated that this adduct could originate from methyl vinyl ketone (MVK). Incubation of human-, sheep- and bovine blood with MVK increased the signal of the identified peak. By comparing the LC–MS/MS data from the unknown background peak with data obtained from synthesized fluorescein thiohydantoin (FTH) standards of the MVK adduct to valine and d₈-valine, the identity of this adduct was confirmed. The MVK adduct was shown present in human blood (∼35 pmol/g globin, n = 3) and only just above LOD in bovine blood, n = 1 (LOD = 2 pmol/g globin). MVK reacts, in similarity with acrylamide, via Michael addition. MVK is known to occur in the environment and has earlier been observed in biological samples, which means that there are possible natural and anthropogenic exposure sources. Analysis of an Hb adduct from MVK in humans has to our knowledge not been described before.     

Gas chromatography–mass spectrometric method for quantitative determination in human urine of dicarboxylic (dioic) acids produced in the body as a consequence of cholesterol biosynthesis inhibition

A capillary gas chromatography–mass spectrometric (GC–MS) method in human urine has been developed and validated for the quantitative determination of dicarboxylic acids (dioic acids) which are produced in the body as a consequence of the administration of an inhibitor of the enzyme squalene synthase, which is involved in the biosynthesis of cholesterol. The standards and quality control (QC) samples were prepared by adding dioic acids into human urine. Internal standard (sebacic acid) was added to each urine sample (0.1 ml) and then dried by evaporation under nitrogen. The dried sample was reacted with pentafluorobenzyl (PFB) bromide under conditions that maximized the formation of the di-PFB ester (at the expense of the mono-PFB ester) of the dioic acids. After drying by evaporation, each sample residue was reconstituted in mesitylene and injected into a capillary GC–MS system via a splitless injection. The detection was by negative ion chemical ionization mass spectrometry with selected ion monitoring (SIM) of the [M−PFB]⁻ of the analytes and the internal standard.