Tile-based Fisher ratio analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) data using a null distribution approach.

Comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) is a versatile instrumental platform capable of collecting highly informative, yet highly complex, chemical data for a variety of samples. Fisher-ratio (F-ratio) analysis applied to the supervised comparison of sample classes algorithmically reduces complex GC × GC-TOFMS data sets to find class distinguishing chemical features. F-ratio analysis, using a tile-based algorithm, significantly reduces the adverse effects of chromatographic misalignment and spurious covariance of the detected signal, enhancing the discovery of true positives while simultaneously reducing the likelihood of detecting false positives. Herein, we report a study using tile-based F-ratio analysis whereby four non-native analytes were spiked into diesel fuel at several concentrations ranging from 0 to 100 ppm. Spike level comparisons were performed in two regimes: comparing the spiked samples to the nonspiked fuel matrix and to each other at relative concentration factors of two. Redundant hits were algorithmically removed by refocusing the tiled results onto the original high resolution pixel level data. To objectively limit the tile-based F-ratio results to only features which are statistically likely to be true positives, we developed a combinatorial technique using null class comparisons, called null distribution analysis, by which we determined a statistically defensible F-ratio cutoff for the analysis of the hit list. After applying null distribution analysis, spiked analytes were reliably discovered at ∼1 to ∼10 ppm (∼5 to ∼50 pg using a 200:1 split), depending upon the degree of mass spectral selectivity and 2D chromatographic resolution, with minimal occurrence of false positives. To place the relevance of this work among other methods in this field, results are compared to those for pixel and peak table-based approaches.

Serial plasma metabolites following hypoxic-ischemic encephalopathy in a nonhuman primate model.

Biomarkers that indicate the severity of hypoxic-ischemic brain injury and response to treatment and that predict neurodevelopmental outcomes are urgently needed to improve the care of affected neonates. We hypothesize that sequentially obtained plasma metabolomes will provide indicators of brain injury and repair, allowing for the prediction of neurodevelopmental outcomes. A total of 33 Macaca nemestrina underwent 0, 15 or 18 min of in utero umbilical cord occlusion (UCO) to induce hypoxic-ischemic encephalopathy and were then delivered by hysterotomy, resuscitated and stabilized. Serial blood samples were obtained at baseline (cord blood) and at 0.1, 24, 48, and 72 h of age. Treatment groups included nonasphyxiated controls (n = 7), untreated UCO (n = 11), UCO + hypothermia (HT; n = 6), and UCO + HT + erythropoietin (n = 9). Metabolites were extracted and analyzed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and quantified by PARAFAC (parallel factor analysis). Using nontargeted discovery-based methods, we identified 63 metabolites as potential biomarkers. The changes in metabolite concentrations were characterized and compared between treatment groups. Further comparison determined that 8 metabolites (arachidonic acid, butanoic acid, citric acid, fumaric acid, lactate, malate, propanoic acid, and succinic acid) correlated with early and/or long-term neurodevelopmental outcomes. The combined outcomes of death or cerebral palsy correlated with citric acid, fumaric acid, lactate, and propanoic acid. This change in circulating metabolome after UCO may reflect cellular metabolism and biochemical changes in response to the severity of brain injury and have potential to predict neurodevelopmental outcomes.

Modeling RP-1 fuel advanced distillation data using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and partial least squares analysis.

Recent efforts in predicting rocket propulsion (RP-1) fuel performance through modeling put greater emphasis on obtaining detailed and accurate fuel properties, as well as elucidating the relationships between fuel compositions and their properties. Herein, we study multidimensional chromatographic data obtained by comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC-TOFMS) to analyze RP-1 fuels. For GC × GC separations, RTX-Wax (polar stationary phase) and RTX-1 (non-polar stationary phase) columns were implemented for the primary and secondary dimensions, respectively, to separate the chemical compound classes (alkanes, cycloalkanes, aromatics, etc.), providing a significant level of chemical compositional information. The GC × GC-TOFMS data were analyzed using partial least squares regression (PLS) chemometric analysis to model and predict advanced distillation curve (ADC) data for ten RP-1 fuels that were previously analyzed using the ADC method. The PLS modeling provides insight into the chemical species that impact the ADC data. The PLS modeling correlates compositional information found in the GC × GC-TOFMS chromatograms of each RP-1 fuel, and their respective ADC, and allows prediction of the ADC for each RP-1 fuel with good precision and accuracy. The root-mean-square error of calibration (RMSEC) ranged from 0.1 to 0.5 °C, and was typically below ∼0.2 °C, for the PLS calibration of the ADC modeling with GC × GC-TOFMS data, indicating a good fit of the model to the calibration data. Likewise, the predictive power of the overall method via PLS modeling was assessed using leave-one-out cross-validation (LOOCV) yielding root-mean-square error of cross-validation (RMSECV) ranging from 1.4 to 2.6 °C, and was typically below ∼2.0 °C, at each % distilled measurement point during the ADC analysis.

Properties of PTFE tape as a semipermeable membrane in fluorous reactions.

In a PTFE tape phase-vanishing reaction (PV-PTFE), a delivery tube sealed with PTFE tape is inserted into a vessel which contains the substrate. The reagent diffuses across the PTFE tape barrier into the reaction vessel. PTFE co-polymer films have been found to exhibit selective permeability towards organic compounds, which was affected by the presence of solvents. In this study, we attempted to establish general trends of permeability of PTFE tape to different compounds and to better describe the process of solvent transport in PV-PTFE bromination reactions. Though PTFE tape has been reported as impermeable to some compounds, such as dimethyl phthalate, solvent adsorption to the tape altered its permeability and allowed diffusion through channels of solvent within the PTFE tape. In this case, the solvent-filled pores of the PTFE tape are chemically more akin to the adsorbed solvent rather than to the PTFE fluorous structure. The solvent uptake effect, which was frequently observed in the course of PV-PTFE reactions, can be related to the surface tension of the solvent and the polarity of the solvent relative to the reagent. The lack of pores in bulk PTFE prevents solvents from altering its permeability and, therefore, bulk PTFE is impermeable to most solvents and reagents. However, bromine, which is soluble in liquid fluorous media, diffused through the bulk PTFE. A better understanding of the PTFE phase barrier will make it possible to further optimize the PV-PTFE reaction design.

Implications of phase ratio for maximizing peak capacity in comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry.

The relationship between the phase ratio, ß, of the primary (1D) and secondary (2D) separation dimensions of comprehensive two-dimensional (2D) gas chromatography (GC×GC) separations, and the implications of ß on realization of maximal 2D peak capacity, nc,2D, are examined. A GC×GC chromatographic system with time-of-flight mass spectrometry, TOFMS, was otherwise held constant for the separation of a multi-component test mixture spanning a range of chemical functionalities, while only the ß of the two analytical columns were changed, 1ß for 1D and 2ß for 2D. Six column sets were studied using common, commercially available ß values. The ß ratio, ßR=1ß/2ß, is defined as a quantitative metric to facilitate this study. It is demonstrated that ßR plays a key role in maximizing nc,2D. Overall, ßR substantially affected nc,2D by influencing retention factors on the 2D column, 2k, and thereby changing the modulation period, PM, necessary for proper 2D column separations. The necessary changes to PM modify the modulation ratio, MR, which affects the 1D column peak widths and 1nc due to the impact of undersampling. Through changes to 1ß, the range of 2k can be controlled, with subsequent effects to both 2nc and 1nc. These effects were opposite in direction, such that improvements to 2nc may result in declines in 1nc. It is observed that due to the pseudo-isothermal nature of the 2D separation, there are diminishing returns to extending the 2nc at the cost of 1nc. In this particular study, column set 3 (1D: 20m length, 250µm i.d., 0.25µm film; 2D: 2m, 180µm i.d., 0.2µm film; ßR=1.11) with a PM of 3s provided the highest theoretical nc,2D of ∼8200, though this was at a relatively low MR of ∼1.8. Column set 2 (1D: 20m length, 250µm i.d., 0.5µm film; 2D: 2m, 180µm i.d., 0.2µm film; ßR=0.56) with a PM of 1.5s provided a high theoretical nc,2D of ∼5800, at a much higher MR of ∼3.7. Though column set 2 had a lesser total peak capacity than column set 3, its higher MR suggests that by improving the 1D column efficiency (i.e., narrowing the 1D column peak widths) to improve 1nc, can result in an increased theoretical nc,2D.

Method to determine the true modulation ratio for comprehensive two-dimensional gas chromatography.

A new method is presented to determine the true modulation ratio, MR, from the measurable effective modulation ratio, MR*, in comprehensive two-dimensional gas chromatography, GC×GC, without the requirement for a detector at the end of the primary column. The method was developed through the investigation of modulator induced band broadening, as a function of 1Wb and the selected modulation period, PM, for simulated GC×GC data, by first defining primary column 1D peak(s) and simulating the modulation process. Gaussian curve fitting is used to model each modulated secondary column separation peaklet, 2D, in the unfolded GC×GC data to accurately determine the maxima of the peaklet distribution, followed by Gaussian curve fitting to the maxima to determine the effective 1D peak profile and width, 1Wb*. The relationship between 1Wb and 1Wb* is studied as a function of the effective modulation ratio, MR*, which is 1Wb* divided by PM, in order to determine the true modulation ratio, MR, which is 1Wb divided by PM. We explore how peak sampling phase (in-phase and out-of-phase) plays a role in the relationship between MR and MR*. Experimental validation of the simulated results is also provided, to span a range of commonly implemented conditions with typical 1Wb (2-4.5s) and PM (0.25-8s). Use of MR<2 significantly broadens the 1D peak (MR*≥1.2MR) corresponding to a loss in 1D peak capacity, 1nc≥20%. The new method relies upon mapping from MR* to MR, which is discussed in relation to peak capacity theories for GC×GC. It is found that optimizing 1nc in GC×GC requires that 1Wb is minimized and must be sampled with a sufficiently short PM (1-2s) to minimize modulator induced band broadening and a subsequent reduction in the effective 1D peak capacity.

Performance evaluation of tile-based Fisher Ratio analysis using a benchmark yeast metabolome dataset.

Performance of tile-based Fisher Ratio (F-ratio) data analysis, recently developed for discovery-based studies using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS), is evaluated with a metabolomics dataset that had been previously analyzed in great detail, but while taking a brute force approach. The previously analyzed data (referred to herein as the benchmark dataset) were intracellular extracts from Saccharomyces cerevisiae (yeast), either metabolizing glucose (repressed) or ethanol (derepressed), which define the two classes in the discovery-based analysis to find metabolites that are statistically different in concentration between the two classes. Beneficially, this previously analyzed dataset provides a concrete means to validate the tile-based F-ratio software. Herein, we demonstrate and validate the significant benefits of applying tile-based F-ratio analysis. The yeast metabolomics data are analyzed more rapidly in about one week versus one year for the prior studies with this dataset. Furthermore, a null distribution analysis is implemented to statistically determine an adequate F-ratio threshold, whereby the variables with F-ratio values below the threshold can be ignored as not class distinguishing, which provides the analyst with confidence when analyzing the hit table. Forty-six of the fifty-four benchmarked changing metabolites were discovered by the new methodology while consistently excluding all but one of the benchmarked nineteen false positive metabolites previously identified.

Chemical characterization of the acid alteration of diesel fuel: Non-targeted analysis by two-dimensional gas chromatography coupled with time-of-flight mass spectrometry with tile-based Fisher ratio and combinatorial threshold determination.

The illicit chemical alteration of petroleum fuels is of keen interest, particularly to regulatory agencies that set fuel specifications, or taxes/credits based on those specifications. One type of alteration is the reaction of diesel fuel with concentrated sulfuric acid. Such reactions are known to subtly alter the chemical composition of the fuel, particularly the aromatic species native to the fuel. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) is well suited for the analysis of diesel fuel, but may provide the analyst with an overwhelming amount of data, particularly in sample-class comparison experiments comprised of many samples. Tile-based Fisher-ratio (F-ratio) analysis reduces the abundance of data in a GC×GC-TOFMS experiment to only the peaks which significantly distinguish the unaltered and acid altered sample classes. Three samples of diesel fuel from differently branded filling stations were each altered to discover chemical features, i.e., analyte peaks, which were consistently changed by the acid reaction. Using different fuels prioritizes the discovery of features likely to be robust to the variation present between fuel samples and may consequently be useful in determining whether an unknown sample has been acid altered. The subsequent analysis confirmed that aromatic species are removed by the acid alteration, with the degree of removal consistent with predicted reactivity toward electrophilic aromatic sulfonation. Additionally, we observed that alkenes and alkynes were also removed from the fuel, and that sulfur dioxide or compounds that degrade to sulfur dioxide are generated by the acid alteration. In addition to applying the previously reported tile-based F-ratio method, this report also expands null distribution analysis to algorithmically determine an F-ratio threshold to confidently select only the features which are sufficiently class-distinguishing. When applied to the acid alteration of diesel fuel, the suggested per-hit F-ratio threshold was 12.4, which is predicted to maintain the false discovery rate (FDR) below 0.1%. Using this F-ratio threshold, 107 of the 3362 preliminary hits were deemed significantly changing due to the acid alteration, with the number of false positives estimated to be about 3. Validation of the F-ratio analysis was performed using an additional three fuels.

Non-targeted determination of (13)C-labeling in the Methylobacterium extorquens AM1 metabolome using the two-dimensional mass cluster method and principal component analysis.

A novel analytical workflow is presented for the analysis of time-dependent (13)C-labeling of the metabolites in the methylotrophic bacterium Methylobacterium extorquens AM1 using gas chromatography time-of-flight mass spectrometry (GC-TOFMS). Using (13)C-methanol as the substrate in a time course experiment, the method provides an accurate determination of the number of carbons converted to the stable isotope. The method also extracts a quantitative isotopic dilution time course profile for (13)C uptake of each metabolite labeled that could in principle be used to obtain metabolic flux rates. The analytical challenges encountered require novel analytical platforms and chemometric techniques. GC-TOFMS offers advanced separation of mixtures, identification of individual components, and high data density for the application of advanced chemometrics. This workflow combines both novel and traditional chemometric techniques, including the recently reported two-dimensional mass cluster plot method (2D m/z cluster plot method) as well as principal component analysis (PCA). The 2D m/z cluster plot method effectively indexed all metabolites present in the sample and deconvoluted metabolites at ultra-low chromatographic resolution (RS≈0.04). Using the pure mass spectra extracted, two PCA models were created. Firstly, PCA was used on the first and last time points of the time course experiment to determine and quantify the extent of (13)C uptake. Secondly, PCA modeled the full time course in order to quantitatively extract the time course profile for each metabolite. The 2D m/z cluster plot method found 152 analytes (metabolites and reagent peaks), with 54 pure analytes, and 98 were convoluted, with 65 of the 98 requiring mathematical deconvolution. Of the 152 analytes surveyed, 83 were metabolites determined by the PCA model to have incorporated (13)C while 69 were determined to be either metabolites or reagent peaks that remained unlabeled.

Trilinearity deviation ratio: a new metric for chemometric analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry data.

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) is a well-established instrumental platform for complex samples. However, chemometric data analysis is often required to fully extract useful information from the data. We demonstrate that retention time shifting from one modulation to the next, Δ(2)tR, is not sufficient alone to quantitatively describe the trilinearity of a single GC×GC-TOFMS run for the purpose of predicting the performance of the chemometric method parallel factor analysis (PARAFAC). We hypothesize that analyte peak width on second dimension separations, (2)Wb, also impacts trilinearity, along with Δ(2)tR. The term trilinearity deviation ratio, TDR, which is Δ(2)tR normalized by (2)Wb, is introduced as a quantitative metric to assess accuracy for PARAFAC of a GC×GC-TOFMS data cube. We explore how modulation ratio, MR, modulation period, PM, temperature programming rate, Tramp, sampling phase (in-phase and out-of-phase), and signal-to-noise ratio, S/N, all play a role in PARAFAC performance in the context of TDR. Use of a PM in the 1-2 s range provides an optimized peak capacity for the first dimension separation (500-600) for a 30 min run, with an adequate peak capacity for the second dimension separation (12-15), concurrent with an optimized two-dimensional peak capacity (6000-7500), combined with sufficiently low TDR values (0-0.05) to facilitate low quantitative errors with PARAFAC (0-0.5%). In contrast, use of a PM in the 5s or greater range provides a higher peak capacity on the second dimension (30-35), concurrent with a lower peak capacity on the first dimension (100-150) for a 30 min run, and a slightly reduced two-dimensional peak capacity (3000-4500), and furthermore, the data are not sufficiently trilinear for the more retained second dimension peaks in order to directly use PARAFAC with confidence.

Tile-based Fisher-ratio software for improved feature selection analysis of comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry data.

Comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) is a highly capable instrumental platform that produces complex and information-rich multi-dimensional chemical data. The data can be initially overwhelming, especially when many samples (of various sample classes) are analyzed with multiple injections for each sample. Thus, the data must be analyzed in such a way as to extract the most meaningful information. The pixel-based and peak table-based Fisher ratio algorithmic approaches have been used successfully in the past to reduce the multi-dimensional data down to those chemical compounds that are changing between the sample classes relative to those that are not changing (i.e., chemical feature selection). We report on the initial development of a computationally fast novel tile-based Fisher-ratio software that addresses the challenges due to 2D retention time misalignment without explicitly aligning the data, which is often a shortcoming for both pixel-based and peak table-based algorithmic approaches. Concurrently, the tile-based Fisher-ratio algorithm significantly improves the sensitivity contrast of true positives against a background of potential false positives and noise. In this study, eight compounds, plus one internal standard, were spiked into diesel at various concentrations. The tile-based F-ratio algorithmic approach was able to "discover" all spiked analytes, within the complex diesel sample matrix with thousands of potential false positives, in each possible concentration comparison, even at the lowest absolute spiked analyte concentration ratio of 1.06, the ratio between the concentrations in the spiked diesel sample to the native concentration in diesel.

Fast, high peak capacity separations in comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry.

Peak capacity production is substantially improved for two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) and applied to the fast separation of a 28 component liquid test mixture, and two complex vapor samples (a 65 component volatile organic compound test mixture, and the headspace of warm ground coffee beans). A high peak capacity is achieved in a short separation time by selecting appropriate experimental conditions based on theoretical modeling of on-column band broadening, and by reducing the off-column band broadening by applying a narrow, concentrated injection pulse onto the primary column using high-speed cryo-focusing injection (HSCFI), referred to as thermal injection. A long, relatively narrow open tubular capillary column (20 m, 100 µm inner diameter (i.d.) with a 0.4 µm film thickness to benefit column capacity) was used as the primary column. The initial flow rate was 2 ml/min (60 cm/s average linear flow velocity) which is slightly below the optimal average linear gas velocity of 83 cm/s, due to the flow rate constraint of the TOFMS vacuum system. The oven temperature programming rate was 30°C/min. The secondary column (1.8m, 100 µm i.d. with a 0.1 µm film thickness) provided a relatively high peak capacity separation, concurrent with a significantly shorter modulation period, P(M), than commonly applied with the commercial instrument. With this GC×GC-TOFMS instrumental platform, compounds in the 28 component liquid test mixture provided a ∼7 min separation (with a ∼6.5 min separation time window), producing average peak widths of ∼600 ms full width half maximum (FWHM), resulting in a peak capacity on the primary column of ∼400 peaks (at unit resolution). Using a secondary column with a 500 ms P(M), average peak widths of ∼20 ms FWHM were achieved, thus providing a peak capacity of 15 peaks on the second dimension. Overall, an ideal orthogonal GC×GC peak capacity of ∼6000 peaks (at unit resolution) was achieved (or a ß-corrected orthogonal peak capacity of ∼4400, at an average modulation ratio, M(R), of ∼2). This corresponds to an ideal orthogonal peak capacity production of ∼1000 peaks/min (or ∼700 peaks/min, ß-corrected). For comparison, standard split/split-less injection techniques with a 1:100 split, when combined with standard GC×GC conditions typically provide a peak capacity production of ∼100 peaks/min, hence the instrumental platform we report provides a ∼7-fold to 10-fold improvement.