Parallel factor analysis (PARAFAC) of target analytes in GC x GC-TOFMS data: automated selection of a model with an appropriate number of factors

Comprehensive screening of polybromochlorodibenzo-p-dioxins, dibenzofurans as mixed halogenated compounds in wastewater samples from industrial facilities by GC×GC/ToFMS and post-data processing

An enormous number of pollutants must be investigated to be able to understand which types threaten human health and environmental biota. In this study, we propose a workflow for screening polybromochlorodibenzo-p-dioxins and dibenzofurans (PBCDD/Fs), which are compounds that have thousands of isomers and congeners, by combining measurement of a sample without any in-laboratory-cleanup with the results of comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry and post-data processing. This process can be regarded as "in silico sample cleanup." The post-data processing stage comprises two methods in which the extracted mass spectra are matched to exact mass and isotopic ratios specified as formulae and filtering via mass deficiency. We applied this workflow to wastewater samples from industrial facilities to identify mixtures of halogenated dioxins. As a result, it was estimated that dioxins in an absolute quantity of 10-500 pg could be detected with sufficient accuracy by recovery testing of a standard mixture against sample crude extracts. Tri- to octa-halogenated dioxins were detected in 8 of 13 samples. Leachate from an industrial landfill was found to contain relatively large numbers of PBCDD/Fs, and several congeners were found in wastewater from an industrial fabric facility that handles decabromodiphenyl ether. The workflow, including the post-data processing method developed and applied in this study, has the advantage that additional identifications can be performed at any time from a single set of measurement data. This also enables the screening of substances that have thousands of homologous isomers, such as chlorinated and brominated dioxins, as well as other non-halogenated compounds.

Chemometrics and Related Fields in Python

The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.

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.

Global spectral deconvolution based on non-negative matrix factorization in GC × GC-HRTOFMS

A global spectral deconvolution, based on non-negative matrix factorization (NMF) in comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry, was developed. We evaluated the ability of various instrumental parameters and NMF settings to derive high-performance detection in nontarget screening using a sediment sample. To evaluate the performance of the process, a NIST library search was used to identify the deconvoluted spectra. Differences of the instrumental scan rates (25 and 50 Hz) in deconvolution were evaluated and results show that a high scan rate enhanced the number of compounds detected in the sediment sample. A higher mass resolution in the range of 1,000 to 10,000 and a higher m/z precision in the deconvolution were needed to obtain an accurate mass database. After removal of multiple duplicate hits, which occurred in batch processes of NIST library search on the deconvolution result, 62 unique assignable spectra with a match factor ≥900 were obtained in the deconvoluted chromatogram from the sediment sample, including 54 spectra that were refined by the deconvolution. This method will help to detect and build up well-resolved reference spectra from various complex mixtures and will accelerate nontarget screening.

Methods of discovery-based and targeted metabolite analysis by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection

The investigation of naturally volatile and derivatized metabolites in biological tissues by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) can provide highly complex and information-rich data for comprehensive metabolomics analysis. The addition of the second separation dimension with GC × GC provides additional chemical selectivity, and the fast scanning time of TOFMS offers benefits in chemical selectivity and overall peak capacity compared to traditional one-dimensional (1D) GC. Furthermore, methods of derivatization to facilitate volatility and thermal stability, the most prominent being the silylation of organic compounds, have extended the use of GC as an important metabolomics tool. The highly information-rich data from GC × GC-TOFMS benefits from sophisticated comprehensive targeted and nontargeted algorithmic software methods. Herein, we detail a robust derivatization and instrumental method for metabolomics analysis and provide a brief overview of possible methods for data analysis.

Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways

The abundance of high-dimensional measurements in the form of gene expression and mass spectroscopy calls for models to elucidate the underlying biological system. For widely studied organisms like yeast, it is possible to incorporate prior knowledge from a variety of databases, an approach used in several recent studies. However if such information is not available for a particular organism these methods fall short. In this paper we propose a statistical method that is applicable to a dataset consisting of Liquid Chromatography-Mass Spectroscopy (LC-MS) and gene expression (DNA microarray) measurements from the same samples, to identify genes controlling the production of metabolites. Due to the high dimensionality of both LC-MS and DNA microarray data, dimension reduction and variable selection are key elements of the analysis. Our proposed approach starts by identifying the basis functions (“building blocks”) that constitute the output from a mass spectrometry experiment. Subsequently, the weights of these basis functions are related to the observations from the corresponding gene expression data in order to identify which genes are associated with specific patterns seen in the metabolite data. The modeling framework is extremely flexible as well as computationally fast and can accommodate treatment effects and other variables related to the experimental design. We demonstrate that within the proposed framework, genes regulating the production of specific metabolites can be identified correctly unless the variation in the noise is more than twice that of the signal.

A new method for the automated selection of the number of components for deconvolving overlapping chromatographic peaks

Mathematical deconvolution methods can separate co-eluting peaks in samples for which (chromatographic) separation fail. However, these methods often heavily rely on manual user-input and interpretation. This is not only time-consuming but also error-prone and automation is needed if such methods are to be applied in a routine manner. One major hurdle when automating deconvolution methods is the selection of the correct number of components used for building the model. We propose a new method for the automatic determination of the optimum number of components when applying multivariate curve resolution (MCR) to comprehensive two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) data. It is based on a two-fold cross-validation scheme. The obtained overall cross-validation error decreases when adding components and increases again once over-fitting of the data starts to occur. The turning point indicates that the optimum number of components has been reached. Overall, the method is at least as good as and sometimes superior to the inspection of the eigenvalues when performing singular-value decomposition. However, its strong point is that it can be fully automated and it is thus more efficient and less prone to subjective interpretation. The developed method has been applied to two different-sized regions in a GC×GC-MS chromatogram. In both regions, the cross-validation scheme resulted in selecting the correct number of components for applying MCR. The pure concentration and mass spectral profiles obtained can then be used for identification and/or quantification of the compounds. While the method has been developed for applying MCR to GC×GC-MS data, a transfer to other deconvolution methods and other analytical systems should only require minor modifications.

Sample preparation methodology for mouse heart metabolomics using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry

The investigation of naturally volatile and derivatized metabolites in mammalian tissues by comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) can provide the data for a comprehensive analysis of the pathophysiology of disease processes. When relative quantification is needed for hypothesis testing, the preparation of sample tissue must provide clear evidence that a quantitative relationship exists between the final detected signal and the amount of metabolite in the tissue. Herein, we report the optimization of a metabolite extraction method for mouse heart tissue for GC × GC-TOFMS analysis. A recursive extraction experiment was initially performed to measure the extraction efficiency of representative target metabolites (sugars, tricarboxylic acid cycle metabolites, amino acids, lipid and signaling molecules) in the aqueous fraction of a three-phase extraction system involving tissue, methanol:water, and chloroform. Some metabolites suffered from incomplete extraction with a single extraction of ≈ 40 mg in 600 μl organic and 400 μl aqueous phases, possibly caused by saturation effects. Subsequent experiments, calibrating resulting metabolite signal to the mass of heart tissue extracted, demonstrated that doubling the solvent volumes and a lower tissue mass was needed to provide accurate relative quantification of the derivatized mouse heart metabolome. We demonstrate quantitative extraction of metabolites from ≈ 20 mg of heart tissue using 1200 μl organic phase (chloroform) and 800 μl aqueous phase (methanol:water in equal parts by volume).

The perinatal transition of the circulating metabolome in a nonhuman primate

INTRODUCTION

The fetal-to-neonatal transition is one of the most complex processes in biological existence; much is unknown about this transition on the molecular and biochemical level. Based on growing metabolomics literature, we hypothesize that metabolomic analysis will reveal the key biochemical intermediates that change during the birth transition.

RESULTS

Using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS), we identified 100 metabolites that changed during this transition. Of these 100 metabolites, 23 demonstrated significant change during the first 72 h. Of note, four intermediates of the tricarboxylic acid (TCA) cycle were identified (α-ketoglutaric acid, fumaric acid, malic acid, and succinyl-CoA), demonstrating a consistent rate of rise during the study. This may signify the transition of the neonate from a hypoxic in utero environment to an oxygen-rich environment. Important signaling molecules were also identified, including myo-inositol and glutamic acid.

DISCUSSION

GC × GC-TOFMS was able to identify important metabolites associated with metabolism and signaling. These data can be used as a baseline for normal birth transition, which may aid in future perinatal research investigations.

METHODS

Late-preterm Macaca nemestrina were delivered by hysterotomy, with plasma drawn from the cord blood and after birth at eight additional time points to 72 h of age.

Investigation of interpolation techniques for the reconstruction of the first dimension of comprehensive two-dimensional liquid chromatography-diode array detector data

Simulated and experimental data were used to measure the effectiveness of common interpolation techniques during chromatographic alignment of comprehensive two-dimensional liquid chromatography-diode array detector (LC×LC-DAD) data. Interpolation was used to generate a sufficient number of data points in the sampled first chromatographic dimension to allow for alignment of retention times from different injections. Five different interpolation methods, linear interpolation followed by cross correlation, piecewise cubic Hermite interpolating polynomial, cubic spline, Fourier zero-filling, and Gaussian fitting, were investigated. The fully aligned chromatograms, in both the first and second chromatographic dimensions, were analyzed by parallel factor analysis to determine the relative area for each peak in each injection. A calibration curve was generated for the simulated data set. The standard error of prediction and percent relative standard deviation were calculated for the simulated peak for each technique. The Gaussian fitting interpolation technique resulted in the lowest standard error of prediction and average relative standard deviation for the simulated data. However, upon applying the interpolation techniques to the experimental data, most of the interpolation methods were not found to produce statistically different relative peak areas from each other. While most of the techniques were not statistically different, the performance was improved relative to the PARAFAC results obtained when analyzing the unaligned data.

Application of comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry method to identify potential biomarkers of perinatal asphyxia in a non-human primate model

Perinatal asphyxia is a leading cause of brain injury in infants, occurring in 2-4 per 1000 live births. The clinical response to asphyxia is variable and difficult to predict with current diagnostic tests. Reliable biomarkers are needed to help predict the timing and severity of asphyxia, as well as response to treatment. Two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC×GC-TOFMS) was used herein, in conjunction with chemometric data analysis approaches for metabolomic analysis in order to identify significant metabolites affected by birth asphyxia. Blood was drawn before and after 15 or 18 min of cord occlusion in a Macaca nemestrina model of perinatal asphyxia. Postnatal samples were drawn at 5 min of age (n=20 subjects). Metabolomic profiles of asphyxiated animals were compared to four controls delivered at comparable gestational age. Fifty metabolites with the greatest change pre- to post-asphyxia were identified and quantified. The metabolic profile of post-asphyxia samples showed marked variability compared to the pre-asphyxia samples. Fifteen of the 50 metabolites showed significant elevation in response to asphyxia, ten of which remained significant upon comparison to the control animals. This metabolomic analysis confirmed lactate and creatinine as markers of asphyxia and discovered new metabolites including succinic acid and malate (intermediates in the Krebs cycle) and arachidonic acid (a brain fatty acid and inflammatory marker) as potential biomarkers. GC×GC-TOFMS coupled with chemometric data analysis are useful tools to identify acute biomarkers of brain injury. Further study is needed to correlate these metabolites with severity of disease, and response to treatment.

Toward a global analysis of metabolites in regulatory mutants of yeast

The AMP-activated protein kinase in yeast, Snf1, coordinates expression and activity of numerous intracellular signaling and developmental pathways, including those regulating cellular differentiation, response to stress, meiosis, autophagy, and the diauxic transition. Snf1 phosphorylates metabolic enzymes and transcription factors to change cellular physiology and metabolism. Adr1 and Cat8, transcription factors that activate gene expression after the diauxic transition, are regulated by Snf1; Cat8 through direct phosphorylation and Adr1 by dephosphorylation in a Snf1-dependent manner. Adr1 and Cat8 coordinately regulate numerous genes encoding enzymes of gluconeogenesis, the glyoxylate cycle, β-oxidation of fatty acids, and the utilization of alternative fermentable sugars and nonfermentable substrates. To determine the roles of Adr1, Cat8, and Snf1 in metabolism, two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and liquid chromatography coupled to tandem mass spectrometry were used to identify metabolites whose levels change after the diauxic transition in wild-type-, ADR1-, CAT8-, and SNF1-deficient yeast. A discovery-based approach to data analysis utilized chemometric algorithms to identify, quantify, and compare 63 unique metabolites between wild type, adr1∆, cat8∆, adr1∆cat8∆, and snf1∆ strains. The primary metabolites found to differ were those of gluconeogenesis, the glyoxylate and tricarboxylic acid cycles, and amino acid metabolism. In general, good agreement was observed between the levels of metabolites derived from these pathways and the levels of transcripts from the same strains, suggesting that transcriptional control plays a major role in regulating the levels of metabolites after the diauxic transition.

The impact of sampling time on peak capacity and analysis speed in on-line comprehensive two-dimensional liquid chromatography

Comprehensive two-dimensional liquid chromatography (2DLC) offers a number of practical advantages over optimized one-dimensional LC in peak capacity and thus in resolving power. The traditional "product rule" for overall peak capacity for a 2DLC system significantly overestimates peak capacity because it neglects under-sampling of the first dimension separation. Here we expand on previous work by more closely examining the effects of the first dimension peak capacity and gradient time, and the second dimension cycle times on the overall peak capacity of the 2DLC system. We also examine the effects of re-equilibration time on under-sampling as measured by the under-sampling factor and the influence of molecular type (peptide vs. small molecule) on peak capacity. We show that in fast 2D separations (less than 1h), the second dimension is more important than the first dimension in determining overall peak capacity and conclude that extreme measures to enhance the first dimension peak capacity are usually unwarranted. We also examine the influence of sample types (small molecules vs. peptides) on second dimension peak capacity and peak capacity production rates, and how the sample type influences optimum second dimension gradient and re-equilibration times.

Development and application of a comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry method for the analysis of L-beta-methylamino-alanine in human tissue

L-Beta-methylamino-alanine (BMAA) has been proposed as a worldwide contributor to neurodegenerative diseases, including Parkinson dementia complex (PDC) of Guam and Alzheimer's disease (AD). Recent conflicting reports of the presence of this amino acid in human brain from patients affected by these diseases have made it necessary to develop methods that provide unambiguous detection in complex samples. Comprehensive two-dimensional gas chromatography coupled with time-of-flight-mass-spectrometry analysis (GCxGC-TOFMS) followed by a targeted Parallel Factor Analysis (PARAFAC) deconvolution method has been used recently in metabolomic investigations to separate, identify, and quantify components of complex biological specimens. We have extended and applied this methodology to the toxicological problem of detecting BMAA in extracts of brain tissue. Our results show that BMAA can be isolated from closely eluting compounds and detected in trace amounts in extracts of brain tissue spiked with low levels of this analyte, ranging from 2.5ppb to 50ppb, with a limit of detection (LOD) of 0.7ppb. This new method was sufficiently sensitive to detect BMAA in cerebral extracts of mice fed BMAA. This optimized approach was then applied to analyze tissue from humans; however, no BMAA was detected in the brain extracts from controls or patients with PDC or AD. Our results demonstrate the application of multidimensional chromatography-mass spectrometry methods and computational deconvolution analysis to the problem of detecting trace amounts of a potential toxin in brain extracts from mice and humans.

Utilizing the third order advantage with isotope dilution mass spectrometry

Akin to the standard addition method requiring only a single chromatographic injection, a robust isotope dilution mass spectrometry method is described. The (13)C labeled analyte at known concentration serves as the standard to quantify the unlabeled target analyte. Two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC x GC-TOFMS) provides a combined (12)C and (13)C analyte peak as part of the third order data cube. This combined peak can be isolated from interfering compounds and noise based on the "third order advantage" with parallel factor analysis (PARAFAC). The combined mass spectra are then mathematically resolved using classical least squares (CLS) providing a (12)C/(13)C ratio, thus absolute amounts of (12)C and (13)C. Good agreement between the prepared and determined concentration ratios for test analytes was achieved with further demonstration to real-world samples.