Tile-Based Random Forest Analysis for Analyte Discovery in Balanced and Unbalanced GC × GC-TOFMS Data Sets.

In this study, we introduce a new nontargeted tile-based supervised analysis method that combines the four-grid tiling scheme previously established for the Fisher ratio (F-ratio) analysis (FRA) with the estimation of tile hit importance using the machine learning (ML) algorithm Random Forest (RF). This approach is termed tile-based RF analysis. As opposed to the standard tile-based F-ratio analysis, the RF approach can be extended to the analysis of unbalanced data sets, i.e., different numbers of samples per class. Tile-based RF computes out-of-bag (oob) tile hit importance estimates for every summed chromatographic signal within each tile on a per-mass channel basis (m/z). These estimates are then used to rank tile hits in a descending order of importance. In the present investigation, the RF approach was applied for a two-class comparison of stool samples collected from omnivore (O) subjects and stored using two different storage conditions: liquid (Liq) and lyophilized (Lyo). Two final hit lists were generated using balanced (8 vs Eight comparison) and unbalanced (8 vs Nine comparison) data sets and compared to the hit list generated by the standard F-ratio analysis. Similar class-distinguishing analytes (p < 0.01) were discovered by both methods. However, while the FRA discovered a more comprehensive hit list (65 hits), the RF approach strictly discovered hits (31 hits for the balanced data set comparison and 29 hits for the unbalanced data set comparison) with concentration ratios, [OLiq]/[OLyo], greater than 2 (or less than 0.5). This difference is attributed to the more stringent feature selection process used by the RF algorithm. Moreover, our findings suggest that the RF approach is a promising method for identifying class-distinguishing analytes in settings characterized by both high between-class variance and high within-class variance, making it an advantageous method in the study of complex biological matrices.

Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer.

INTRODUCTION: Modern comprehensive instrumentations provide an unprecedented coverage of complex matrices in the form of high-dimensional, information rich data sets. OBJECTIVES: In addition to the usual biomarker research that focuses on the detection of the studied condition, we aimed to define a proper strategy to conduct a correlation analysis on an untargeted colorectal cancer case study with a data set of 102 variables corresponding to metabolites obtained from serum samples analyzed with comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOF-MS). Indeed, the strength of association existing between the metabolites contains potentially valuable information about the molecular mechanisms involved and the underlying metabolic network associated to a global perturbation, at no additional analytical effort. METHODS: Following Anscombe's quartet, we took particular attention to four main aspects. First, the presence of non-linear relationships through the comparison of parametric and non-parametric correlation coefficients: Pearson's r, Spearman's rho, Kendall's tau and Goodman-Kruskal's gamma. Second, the visual control of the detected associations through scatterplots and their associated regressions and angles. Third, the effect and handling of atypical samples and values. Fourth, the role of the precision of the data on the attribution of the ranks through the presence of ties. RESULTS: Kendall's tau was found the method of choice for the data set at hand. Its application highlighted 17 correlations significantly altered in the active state of colorectal cancer (CRC) in comparison to matched healthy controls (HC), from which 10 were specific to this state in comparison to the remission one (R-CRC) investigated on distinct patients. 15 metabolites involved in the correlations of interest, on the 25 unique ones obtained, were annotated (Metabolomics Standards Initiative level 2). CONCLUSIONS: The metabolites highlighted could be used to better understand the pathology. The systematic investigation of the methodological aspects that we expose allows to implement correlation analysis to various fields and many specific cases.

Comprehensive Insight into Colorectal Cancer Metabolites and Lipids for Human Serum: A Proof-of-Concept Study.

Colorectal cancer (CRC) ranks as the third most frequently diagnosed cancer and the second leading cause of cancer-related deaths. The current endoscopic-based or stool-based diagnostic techniques are either highly invasive or lack sufficient sensitivity. Thus, there is a need for less invasive and more sensitive screening approaches. We, therefore, conducted a study on 64 human serum samples representing three different groups (adenocarcinoma, adenoma, and control) using cutting-edge GC×GC-LR/HR-TOFMS (comprehensive two-dimensional gas chromatography coupled with low/high-resolution time-of-flight mass spectrometry). We analyzed samples with two different specifically tailored sample preparation approaches for lipidomics (fatty acids) (25 µL serum) and metabolomics (50 µL serum). In-depth chemometric screening with supervised and unsupervised approaches and metabolic pathway analysis were applied to both datasets. A lipidomics study revealed that specific PUFA (ω-3) molecules are inversely associated with increased odds of CRC, while some PUFA (ω-6) analytes show a positive correlation. The metabolomics approach revealed downregulation of amino acids (alanine, glutamate, methionine, threonine, tyrosine, and valine) and myo-inositol in CRC, while 3-hydroxybutyrate levels were increased. This unique study provides comprehensive insight into molecular-level changes associated with CRC and allows for a comparison of the efficiency of two different analytical approaches for CRC screening using same serum samples and single instrumentation.

Top-Down Approach to Retention Time Prediction in Comprehensive Two-Dimensional Gas Chromatography-Mass Spectrometry.

In this contribution, we describe a novel modeling approach to predicting retention times (tr) in comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-ToF-MS) with a particular emphasis on the second-dimension (2D) retention time predictions (2tr). This approach is referred to as a "top-down" approach in that it breaks down the complete GC × GC separation into two independent one-dimensional gas chromatography separations (1D-GC). In this regard, both dimensions, that is, first dimension (1D) and second dimension (2D) are treated separately, and the cryogenic modulator is simply considered as a second consecutive injection device. Separate 1D-GC tr predictions are performed on both dimensions using the same flow rate as the one deployed in the conventional GC × GC system. The separate tr predictions are then combined to account for the two-dimensional separation. This model was applied to 24 analytes from 2 standard mixtures (Grob Test Mix and Fragrance Materials Test Mix) and assessed across 9 GC × GC chromatographic conditions. The experimental and predicted chromatographic retention space occupations were assessed by using the convex hull approach defined by the Delaunay triangulation. The predicted percentage of space occupation corresponded favorably with the experimental values. Furthermore, the top-down approach enabled an accurate prediction of the 2tr of all investigated analytes, providing an average 2tr modeling error of 0.26 ± 0.01 s.

Comprehensive two-dimensional gas chromatographic platforms comparison for exhaled breath metabolites analysis.

The high potential of exhaled breath for disease diagnosis has been highlighted in numerous studies. However, exhaled breath analysis is suffering from a lack of standardized sampling and analysis procedures, impacting the robustness of inter-laboratory results, and thus hampering proper external validation. The aim of this work was to verify compliance and validate the performance of two different comprehensive two-dimensional gas chromatography coupled to mass spectrometry platforms in different laboratories by monitoring probe metabolites in exhaled breath following the Peppermint Initiative guidelines. An initial assessment of the exhaled breath sampling conditions was performed, selecting the most suitable sampling bag material and volume. Then, a single sampling was performed using Tedlar bags, followed by the trapping of the volatile organic compounds into thermal desorption tubes for the subsequent analysis using two different analytical platforms. The thermal desorption tubes were first analyzed by a (cryogenically modulated) comprehensive two-dimensional gas chromatography system coupled to high-resolution time-of-flight mass spectrometry. The desorption was performed in split mode and the split part was recollected in the same tube and further analyzed by a different (flow modulated) comprehensive two-dimensional gas chromatography system with a parallel detection, specifically using a quadrupole mass spectrometer and a vacuum ultraviolet detector. Both the comprehensive two-dimensional gas chromatography platforms enabled the longitudinal tracking of the peppermint oil metabolites in exhaled breath. The increased sensitivity of comprehensive two-dimensional gas chromatography enabled to successfully monitor over a 6.5 h period a total of 10 target compounds, namely α-pinene, camphene, ß-pinene, limonene, cymene, eucalyptol, menthofuran, menthone, isomenthone, and neomenthol.

The relevance of European Biota Quality Standards on the ecological water quality as determined by the multimetric macro-invertebrate index: A Flemish case study.

European Biota Quality Standards (EQSbiota), for compounds with low water solubility and high biomagnification, were created to sustain water quality and protect top predators and humans from secondary poisoning. In reality, for multiple compounds, an exceedance of these standards is often reported in literature without a decrease in ecological water quality determined by biotic indices. In the present study, threshold concentrations were defined in biota (from 44 sampling locations throughout Flanders (Belgium)), above which a good ecological water quality, assessed by the Multimetric Macroinvertebrate Index Flanders (MMIF), was never reached. Threshold values were compared to current EQSbiota. Accumulated perfluoroctane sulfonate (PFOS), mercury (Hg), hexabromocyclododecane (HBCD), polybrominated diphenyl ethers (PBDEs), dioxins and polychlorinated biphenyls (PCBs) concentrations were measured in muscle tissue of European yellow eel (Anguilla anguilla) and perch (Perca fluviatilis). Fluoranthene and benzo(a)pyrene were also analyzed in translocated mussels (Dreissena bugensis, D. polymorpha and Corbicula fluminea). Threshold values could only be calculated using a 90th quantile regression model for PFOS (in perch; 12 µg/kg ww), PCBs (in eel; 328 µg/kg ww) and benzo(a)pyrene (in mussels: 4.35 µg/kg ww). The lack of a significant regression model for the other compounds indicated an effective threshold value higher than the concentrations measured in the present study. Alternatively, the 95th percentile of concentrations measured in locations with a good ecological quality (MMIF≥0.7), was calculated for all compounds as an additional threshold value. Finally, fish concentrations were standardized for 5% lipid content (or 26% dry weight content for PFOS and Hg). Threshold values for PFOS and benzo(a)pyrene and the 95th percentiles for dioxins and fluoranthene were comparable to the existing standards. For all other compounds, the 95th percentile was higher than the current EQSbiota, while for HBCD, it was lower. These results strongly advise revising and fine-tuning the current EQSbiota, especially for ∑PBDE and HBCD.

Distinguishing between Decaffeinated and Regular Coffee by HS-SPME-GC×GC-TOFMS, Chemometrics, and Machine Learning.

Coffee, one of the most popular beverages in the world, attracts consumers by its rich aroma and the stimulating effect of caffeine. Increasing consumers prefer decaffeinated coffee to regular coffee due to health concerns. There are some main decaffeination methods commonly used by commercial coffee producers for decades. However, a certain amount of the aroma precursors can be removed together with caffeine, which could cause a thin taste of decaffeinated coffee. To understand the difference between regular and decaffeinated coffee from the volatile composition point of view, headspace solid-phase microextraction two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS) was employed to examine the headspace volatiles of eight pairs of regular and decaffeinated coffees in this study. Using the key aroma-related volatiles, decaffeinated coffee was significantly separated from regular coffee by principal component analysis (PCA). Using feature-selection tools (univariate analysis: t-test and multivariate analysis: partial least squares-discriminant analysis (PLS-DA)), a group of pyrazines was observed to be significantly different between regular coffee and decaffeinated coffee. Pyrazines were more enriched in the regular coffee, which was due to the reduction of sucrose during the decaffeination process. The reduction of pyrazines led to a lack of nutty, roasted, chocolate, earthy, and musty aroma in the decaffeinated coffee. For the non-targeted analysis, the random forest (RF) classification algorithm was used to select the most important features that could enable a distinct classification between the two coffee types. In total, 20 discriminatory features were identified. The results suggested that pyrazine-derived compounds were a strong marker for the regular coffee group whereas furan-derived compounds were a strong marker for the decaffeinated coffee samples.

Exploiting targeted and untargeted approaches for the analysis of bacterial metabolites under altered growth conditions.

In the host, pathogenic microorganisms have developed stress responses to cope with constantly changing environments. Stress responses are directly related to changes in several metabolomic pathways, which could hamper microorganisms' unequivocal identification. We evaluated the effect of various in vitro stress conditions (acidic, basic, oxidative, ethanolic, and saline conditions) on the metabolism of Staphylococcus aureus, Bacillus cereus, and Pseudomonas aeruginosa, which are common lung pathogens. The metabolite profiles of the bacteria were analyzed using liquid chromatography coupled to triple quadrupole and quadrupole time-of-flight mass spectrometry. The advantages of targeted and untargeted analysis combined with univariate and multivariate statistical analysis (principal component analysis, hierarchical cluster analysis, partial least square discriminant analysis, random forest) were combined to unequivocally identify bacterial species. In normal in vitro conditions, the targeted methodology, based on the analysis of primary metabolites, enabled the rapid and efficient discrimination of the three bacteria. In changing in vitro conditions and specifically in presence of the various stressors, the untargeted methodology proved to be more valuable for the global and accurate differentiation of the three bacteria, also considering the type of stress environment within each species. In addition, species-specific metabolites (i.e., fatty acids, polysaccharides, peptides, and nucleotide bases derivatives) were putatively identified. Good intra-day repeatability and inter-day repeatability (< 10% RSD and < 15% RSD, respectively) were obtained for the targeted and the untargeted methods. This untargeted approach highlights its importance in unusual (and less known) bacterial growth environments, being a powerful tool for infectious disease diagnosis, where the accurate classification of microorganisms is sought.

Breathomics to diagnose systemic sclerosis using thermal desorption and comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry.

Systemic sclerosis is a rare autoimmune disease associated with rapidly evolving interstitial lung disease, responsible for the disease severity and mortality. Specific biomarkers enabling the early diagnosis and prognosis associated with the disease progression are highly needed. Volatile organic compounds in exhaled breath are widely available and non-invasive and have the potential to reflect metabolic processes occurring within the body. Comprehensive two-dimensional gas chromatography coupled to high-resolution mass spectrometry was used to investigate the potential of exhaled breath to diagnose systemic sclerosis. The exhaled breath of 32 patients and 30 healthy subjects was analyzed. The high resolving power of this approach enabled the detection of 356 compounds in the breath of systemic sclerosis patients, which was characterized by an increase of mainly terpenoids and hydrocarbons. In addition, the use of 4 complementary statistical approaches (two-tailed equal variance t-test, fold change, partial least squares discriminant analysis, and random forest) resulted in the identification of 16 compounds that can be used to discriminate systemic sclerosis patients from healthy subjects. Receiver operating curves were generated that provided an accuracy of 90%, a sensitivity of 92%, and a specificity of 89%. The chemical identification of eight compounds predictive of systemic sclerosis was validated using commercially available standards. The analytical variations together with the volatile composition of room air were carefully monitored during the timeframe of the study to ensure the robustness of the technique. This study represents the first reported evaluation of exhaled breath analysis for systemic sclerosis diagnosis and provides surrogate markers for such disease.

The role of sample preparation in multidimensional gas chromatographic separations for non-targeted analysis with the focus on recent biomedical, food, and plant applications.

In this review, we consider and discuss the affinity and complementarity between a generic sample preparation technique and the comprehensive two-dimensional gas chromatography process. From the initial technical development focus (e.g., on the GC×GC and solid-phase microextraction techniques), the trend is inevitably shifting toward more applied challenges, and therefore, the preparation of the sample should be carefully considered in any GC×GC separation for an overreaching research. We highlight recent biomedical, food, and plant applications (2016-July 2020), and specifically those in which the combination of tailored sample preparation methods and GC×GC-MS has proven to be beneficial in the challenging aspects of non-targeted analysis. Specifically on the sample preparation, we report on gas-phase, solid-phase, and liquid-phase extractions, and derivatization procedures that have been used to extract and prepare volatile and semi-volatile metabolites for the successive GC×GC analysis. Moreover, we also present a milestone section reporting the early works that pioneered the combination of sample preparation techniques with GC×GC for non-targeted analysis.

Advanced mono- and multi-dimensional gas chromatography-mass spectrometry techniques for oxygen-containing compound characterization in biomass and biofuel samples.

A wide variety of biomass, from triglycerides to lignocellulosic-based feedstock, are among promising candidates to possibly fulfill requirements as a substitute for crude oils as primary sources of chemical energy feedstock. During the feedstock processing carried out to increase the H:C ratio of the products, heteroatom-containing compounds can promote corrosion, thus limiting and/or deactivating catalytic processes needed to transform the biomass into fuel. The use of advanced gas chromatography techniques, in particular multi-dimensional gas chromatography, both heart-cutting and comprehensive coupled to mass spectrometry, has been widely exploited in the field of petroleomics over the past 30 years and has also been successfully applied to the characterization of volatile and semi-volatile compounds during the processing of biomass feedstock. This review intends to describe advanced gas chromatography-mass spectrometry-based techniques, mainly focusing in the period 2011-early 2020. Particular emphasis has been devoted to the multi-dimensional gas chromatography-mass spectrometry techniques, for the isolation and characterization of the oxygen-containing compounds in biomass feedstock. Within this context, the most recent advances to sample preparation, derivatization, as well as gas chromatography instrumentation, mass spectrometry ionization, identification, and data handling in the biomass industry, are described.

Untargeted Serum Metabolic Profiling by Comprehensive Two-Dimensional Gas Chromatography-High-Resolution Time-of-Flight Mass Spectrometry.

While many laboratories take appropriate care, there are still cases where the performances of untargeted profiling methods suffer from a lack of design, control, and articulation of the various steps involved. This is particularly harmful to modern comprehensive analytical instrumentations that otherwise provide an unprecedented coverage of complex matrices. In this work, we present a global analytical workflow based on comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry. It was optimized for sample preparation and chromatographic separation and validated on in-house quality control (QC) and NIST SRM 1950 samples. It also includes a QC procedure, a multiapproach data (pre)processing workflow, and an original bias control procedure. Compounds of interest were identified using mass, retention, and biological information. As a proof of concept, 35 serum samples representing three subgroups of Crohn's disease (with high, low, and quiescent endoscopic activity) were analyzed along with 33 healthy controls. This led to the selection of 33 unique candidate biomarkers able to classify the Crohn's disease and healthy samples with an orthogonal partial least-squares discriminant analysis Q2 of 0.48 and a receiver-operating-characteristic area under the curve of 0.85 (100% sensitivity and 82% specificity in cross validation). Fifteen of these 33 candidates were reliably annotated (Metabolomics Standards Initiative level 2).

Translation of a One-Dimensional to a Comprehensive Two-Dimensional Gas Chromatography Method with Dual-Channel Detection for Volatile Organic Compound Measurement in Forensic Applications.

After its introduction in the early 1990s, comprehensive two-dimensional gas chromatography (GC×GC) has evolved from a separation science research tool to the central component of many industries. Despite the maturity of the technique, some fields remain reluctant to its use in routine applications. In the case of forensic science, some constraints are the strict requirements enforced in forensic laboratories and the time and effort that must be invested for intralaboratory method validation. Concerns may also arise about whether information could be lost when transitioning to a new technique. This study reports on a method translation from conventional one-dimensional (1D) GC to GC×GC, ensuring the integrity of data as conversion is made. The GC was retrofitted with a reverse fill/flush (RFF) flow modulator and equipped with dual-channel detection using a quadrupole mass spectrometer (qMS) and a flame ionization detector (FID). The parallel use of two detectors, where qMS was applied for qualitative identification and FID for quantification, allowed higher flows and slightly wider peaks to be exploited for the analysis of a volatile organic compound (VOC) reference mixture relevant to forensic VOC profiling. Peak quality assessment and calibration curves using GC-qMS and GC×GC-qMS/FID document the transfer and adaptation of the original method without a loss in data quality. Furthermore, the preprocessing and the data analysis processing steps, including calibration and peak quality assessment for each of the three data sets, are explained in detail. This information provides benchmark data for routine laboratories that want to implement a GC×GC approach into routine workflows.

In-Depth Cannabis Multiclass Metabolite Profiling Using Sorptive Extraction and Multidimensional Gas Chromatography with Low- and High-Resolution Mass Spectrometry.

The present research reports on the development of a methodology to unravel the complex phytochemistry of cannabis. Specifically, cannabis inflorescences were considered and stir bar sorptive extraction (SBSE) was used for the preconcentration of the metabolites. Analytes were thermally desorbed into a comprehensive two-dimensional (2D) gas chromatography (GC × GC) system coupled with low- and high-resolution mass spectrometry (MS). Particular attention was devoted to the optimization of the extraction conditions, to extend the analytes' coverage, and the chromatographic separation, to obtain a robust data set for further untargeted analysis. Monoterpenes, sesquiterpenes, hydrocarbons, cannabinoids, other terpenoids, and fatty acids were considered to optimize the extraction conditions. The response of selected ions for each chemical class, delimited in specific 2D chromatographic regions, enabled an accurate and fast evaluation of the extraction variables (i.e., time, temperature, solvent, salt addition), which were then selected to have a wide analyte selection and good reproducibility. Under optimized SBSE conditions, eight different cannabis inflorescences and a quality control sample were analyzed and processed following an untargeted and unsupervised approach. Principal component analysis on all detected metabolites revealed chemical differences among the sample types which could be associated with the plant subspecies. With the same SBSE-GC × GC-MS methodology, a quantitative targeted analysis was performed on three common cannabinoids, namely, Δ9-tetrahydrocannabinol, cannabidiol, and cannabinol. The method was validated, giving correlation factors over 0.98 and <20% reproducibility (relative standard deviation). The high-resolution MS acquisition allowed for high-confidence identification and post-targeted analysis, confirming the presence of two pesticides, a plasticizer, and a cannabidiol degradation product in some of the samples.

Specificity of metabolic colorectal cancer biomarkers in serum through effect size.

INTRODUCTION: Colorectal cancer is one of the most diagnosed cancers, leading to numerous deaths. In addition to existing screening methods, metabolic profiling could help both to diagnose and to understand the various states of the disease. OBJECTIVES: Find specific candidate biomarkers (CB) in serum of patients with colorectal cancer (CRC), in comparison to the situation after remission (R-CRC), evaluated on distinct patients. METHODS: All serum samples were analyzed using comprehensive two-dimensional gas chromatography (GC × GC) coupled to high resolution time of flight mass spectrometry (TOF-MS) through an optimized and validated untargeted analytical method regulated by a quality control (QC) system. First, we used a specific multi-approaches data (pre)processing workflow to highlight, annotate and assess the performances of the most altered metabolites between CRC patients (n = 18) and healthy control samples (HC, n = 19) specifically matched for age and gender, two of the most influential confounding factors. On the contrary, due to the difficulty to control for all clinical and demographic traits when sampling small cohorts, the samples from patients in remission (n = 17) were not matched. Because of the consequent risk of bias, the usual null hypothesis significance tests (NHST) could not be applied reliably. Therefore, we compared the R-CRC samples to another specifically matched group of healthy controls (R-HC, n = 17), and used this comparison to indirectly address the difference between patients with colorectal cancer and patients in remission through a measure called effect size (ES) whose methodological aspects were investigated. RESULTS: 24 candidate biomarkers were found significantly altered and able to discriminate the CRC and HC samples efficiently (Receiver Operating Characteristic (ROC) area under the curve (AUC) of 0.86, sensitivity and specificity of 0.72 and 0.78). 10 of those were found to have signals close to healthy levels in the R-CRC samples and were therefore specific to colorectal cancer. In the point-biserial case studied here, r-like (strength of association) and d-like (standardized mean difference) ES were directly convertible and only linear and rank-based ES were different. We therefore used and recommend Hedges' g, Spearman's rho and Kendall's tau, along with an unstandardized ES. The confidence intervals, that quantify the uncertainty of the measure, were well represented through scatterplots and distribution curves. CONCLUSION: The candidate biomarkers found, along with their specificity, could help for the detection of colorectal cancer, the diagnosis of remission, and for the understanding of its pathophysiology, after proper validation on independent cohorts. The effect size, here applied on a MS global profiling data set, is an ideal complement to NHST and a useful tool to compare and combine distinct cohorts, within a study as well as between studies (meta-analysis).

Comparison of the effect of chemically and biologically induced inflammation on the volatile metabolite production of lung epithelial cells by GC×GC-TOFMS.

Exhaled breath analysis has a high potential for early non-invasive diagnosis of lung inflammatory diseases, such as asthma. The characterization and understanding of the inflammatory metabolic pathways involved into volatile organic compounds (VOCs) production could bring exhaled breath analysis into clinical practice and thus open new therapeutic routes for inflammatory diseases. In this study, lung inflammation was simulated in vitro using A549 epithelial cells. We compared the VOC production from A549 epithelial cells after a chemically induced oxidative stress in vitro, exposing the cells to H2O2, and a biological stress, exposing the cells to an inflammatory pool of sputum supernatants. Special attention was devoted to define proper negative and positive controls (8 different types) for our in vitro models, including healthy sputum co-culture. Sputum from 25 asthmatic and 8 healthy patients were collected to create each pool of supernatants. Each sample type was analyzed in 4 replicates using solid-phase microextraction (SPME) comprehensive two-dimensional gas chromatography hyphenated to time-of-flight mass spectrometry (GC×GC-TOFMS). This approach offers high resolving power for complex VOC mixtures. According to the type of inflammation induced, significantly different VOCs were produced by the epithelial cells compared to all controls. For both chemical and biological challenges, an increase of carbonyl compounds (54%) and hydrocarbons (31%) was observed. Interestingly, only the biological inflammation model showed a significant cell proliferation together with an increased VOC production linked to asthma airway inflammation. This study presents a complete GC×GC-TOFMS workflow for in vitro VOC analysis, and its potential to characterize complex lung inflammatory mechanisms.

Investigating aroma diversity combining purge-and-trap, comprehensive two-dimensional gas chromatography, and mass spectrometry.

Headspace gas chromatography is frequently used for aroma profiling thanks to its ability to naturally exploit the volatility of aroma compounds, and also to provide chemical information on sample composition. Its main advantages rely on simplicity, no use of solvent, amenability to automation, and the cleanliness of the extract. In the present contribution, the most effective sampling (dynamic extraction), separation (multidimensional gas chromatography), and detection (mass spectrometry) techniques for untargeted analysis are exploited in combination, showing their potential in unraveling aroma profiles in fruit beers. To complete the overall analytical process, a neat workflow for data analysis is discussed and used for the successful characterization and identification of five different beer flavors (berries, cherry, banana, apple, and peach). From the technical viewpoint, the coupling of purge-and-trap, comprehensive two-dimensional gas chromatography, and mass spectrometry makes the global methodology unique, and it is for the first time discussed. A (low-)flow modulation approach allowed for the full transfer into the second dimension with mass-spectrometry compatible flow (< 7 mL/min), avoiding the need of splitting before detection and making the overall method sensitive (1.2-5.2-fold higher signal to noise ratio compared to unmodulated gas chromatography conditions) and selective.

Exhaled Volatile Organic Compounds Are Able to Discriminate between Neutrophilic and Eosinophilic Asthma.

Rationale: Analysis of exhaled breath for asthma phenotyping using endogenously generated volatile organic compounds (VOCs) offers the possibility of noninvasive diagnosis and therapeutic monitoring. Induced sputum is indeed not widely available and markers of neutrophilic asthma are still lacking.Objectives: To determine whether analysis of exhaled breath using endogenously generated VOCs can be a surrogate marker for recognition of sputum inflammatory phenotypes.Methods: We conducted a prospective study on 521 patients with asthma recruited from the University Asthma Clinic of Liege. Patients underwent VOC measurement, fraction of exhaled nitric oxide (FeNO) spirometry, sputum induction, and gave a blood sample. Subjects with asthma were classified in three inflammatory phenotypes according to their sputum granulocytic cell count.Measurements and Main Results: In the discovery study, seven potential biomarkers were highlighted by gas chromatography-mass spectrometry in a training cohort of 276 patients with asthma. In the replication study (n = 245), we confirmed four VOCs of interest to discriminate among asthma inflammatory phenotypes using comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry. Hexane and 2-hexanone were identified as compounds with the highest classification performance in eosinophilic asthma with accuracy comparable to that of blood eosinophils and FeNO. Moreover, the combination of FeNO, blood eosinophils, and VOCs gave a very good prediction of eosinophilic asthma (area under the receiver operating characteristic curve, 0.9). For neutrophilic asthma, the combination of nonanal, 1-propanol, and hexane had a classification performance similar to FeNO or blood eosinophils in eosinophilic asthma. Those compounds were found in higher levels in neutrophilic asthma.Conclusions: Our study is the first attempt to characterize VOCs according to sputum granulocytic profile in a large population of patients with asthma and provide surrogate markers for neutrophilic asthma.

Molecular Characterization of Volatiles and Petrochemical Base Oils by Photo-Ionization GC×GC-TOF-MS.

The characterization of organic mixtures by comprehensive two-dimensional gas chromatography (GC×GC) coupled to electron impact (EI) ionization time-of-flight mass spectrometry (TOF-MS) allows the detection of thousands of compounds. However, owing to the exhaustive fragmentation following EI ionization, despite the use of mass spectral libraries, a majority of the compounds remains unidentified because of the lack of parent ion preservation. Thus, soft-ionization energies leading to organic compounds being ionized with limited or no fragmentation, retaining the molecular ion, has been of interest for many years. In this study, photoionization (PI) was evaluated as the ion source for GC×GC-TOF-MS measurements. First, capabilities and limitations of PI were tested using an authentic mixture of compounds of several chemical classes. Ionization energy exhibited by PI, equivalent to 10.8 eV, resulted in significant retention of molecular ion information; [M]+• for alkanes, ketones, FAMEs, aromatics, [M-H]+• for chloroalkanes, and [M-H2O]+• for alcohols. Second, considering the potential of PI for hydrocarbons, base oils, complex mixtures of saturated and unsaturated hydrocarbons blended for finished lubricant formulations, were extensively evaluated. Several chemical classes of hydrocarbons were positively identified including a large number of isomeric compounds, both aliphatics and cyclics. Interestingly, branched-alkanes were ionized with lower excess internal energy, not only retaining the molecular ions but also exhibiting unique fragmentation patterns. The results presented herein offer a unique perspective into the detailed molecular characterization of base oils. Such unprecedented identification power of PI coupled with GC×GC-TOF-MS is the first report covering volatiles to low-volatile organic mixtures.

A minimally-invasive method for profiling volatile organic compounds within postmortem internal gas reservoirs.

In forensic casework, non-invasive and minimally-invasive methods for postmortem examinations are extremely valuable. Whole body postmortem computed tomography (PMCT) is often used to provide visualization of the internal characteristics of a body prior to more invasive procedures and has also been used to locate gas reservoirs inside the body to assist in determining cause of death. Preliminary studies have demonstrated that exploiting the volatile organic compounds (VOCs) located in these gas reservoirs by comprehensive two-dimensional gas chromatography-high-resolution time-of-flight mass spectrometry (GC×GC-HRTOF-MS) may assist in providing information regarding the postmortem interval. The aim of the current study was to further develop the procedures related to solid-phase microextraction (SPME) and GC×GC-HRTOF-MS analysis of gas reservoirs collected from deceased individuals. SPME fiber extraction parameters, internal standard approach, and sample stability were investigated. Altering the SPME parameters increased the selectivity and sensitivity for the VOC profile, and the use of a mixed deuterated internal standard contributed to data quality. Samples were found to be stable up to 6 weeks but were recommended to be analyzed within 4 weeks due to higher variation observed beyond this point. In addition, 29 VOC markers of interest were identified, and heart and/or abdominal cavity samples were suggested as a possible standardized sampling location for future studies. The data presented in this study will contribute to the long-term goal of producing a routine, accredited method for minimally-invasive VOC analysis in postmortem examinations.