Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry analysis of metabolites in fermenting and respiring yeast cells

Connecting the dots: investigating the link between environmental, genetic, and epigenetic influences in metabolomic alterations in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) accounts for around 90% of all oral cancers and is the eighth most common cancer worldwide. Despite progress in managing OSCC, the overall prognosis remains poor, with a survival rate of around 50-60%, largely due to tumor size and recurrence. The challenges of late-stage diagnosis and limitations in current methods emphasize the urgent need for less invasive techniques to enable early detection and treatment, crucial for improving outcomes in this aggressive form of oral cancer. Research is currently aimed at unraveling tumor-specific metabolite profiles to identify candidate biomarkers as well as discover underlying pathways involved in the onset and progression of cancer that could be used as new targets for diagnostic and therapeutic purposes. Metabolomics is an advanced technological approach to identify metabolites in different sample types (biological fluids and tissues). Since OSCC promotes metabolic reprogramming influenced by a combination of genetic predisposition and environmental factors, including tobacco and alcohol consumption, and viral infections, the identification of distinct metabolites through screening may aid in the diagnosis of this condition. Moreover, studies have shown the use of metabolites during the catalysis of epigenetic modification, indicating a link between epigenetics and metabolism. In this review, we will focus on the link between environmental, genetic, and epigenetic influences in metabolomic alterations in OSCC. In addition, we will discuss therapeutic targets of tumor metabolism, which may prevent oral tumor growth, metastasis, and drug resistance.

Serum metabolic profiling in pregnant Holstein cows 3 weeks prior to parturition using two-dimensional gas chromatography time-of-flight mass spectrometry

This preliminary study explored potential serum biomarkers for predicting the onset of milk fever (MF), a bovine parturient disease with hypocalcemia. We conducted two-dimensional gas chromatography mass spectrometry-based metabolomics in 8 and 17 pregnant Holstein cows that did and did not develop MF 3 weeks later, respectively. In principal component analysis (PCA) applied to a dataset containing 1,498 metabolites, serum metabolites exhibited highly similar chemical profiles between cows with and without MF. PCA with a limited dataset of metabolites containing fatty acids, which had significantly different values between the groups and/or correlation coefficients of >0.5 for the serum calcium concentration, distinguished the two groups. These suggest the possibility of developing serum biomarkers for predicting bovine MF.

Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review

Endometrial cancer, the most prevalent gynecological malignancy in developed countries, is experiencing a sustained rise in both its incidence and mortality rates, primarily attributed to extended life expectancy and lifestyle factors. Currently, the absence of precise diagnostic tools hampers the effective management of the expanding population of women at risk of developing this disease. Furthermore, patients diagnosed with endometrial cancer require precise risk stratification to align with optimal treatment planning. Metabolomics technology offers a unique insight into the molecular landscape of endometrial cancer, providing a promising approach to address these unmet needs. This comprehensive literature review initiates with an overview of metabolomic technologies and their intrinsic workflow components, aiming to establish a fundamental understanding for the readers. Subsequently, a detailed exploration of the existing body of research is undertaken with the objective of identifying metabolite biomarkers capable of enhancing current strategies for endometrial cancer diagnosis, prognosis, and recurrence monitoring. Metabolomics holds vast potential to revolutionize the management of endometrial cancer by providing accuracy and valuable insights into crucial aspects.

Comprehensive two-dimensional gas chromatography under low-pressure conditions

The analysis of thermally labile and high-boiling point compounds by gas chromatography (GC) can be a challenge. One technique to overcome these challenges is low-pressure GC, which uses the vacuum produced from the mass spectrometer and wide-bore columns to elute compounds at significantly lower temperatures. While GC-MS is a powerful technique, comprehensive two-dimensional gas chromatography (GC × GC), allows for resolution of compounds that would typically coelute using GC. In this study, a pesticide standard mixture (8270 MegaMix Standard) was analyzed using a conventional GC × GC-TOFMS configuration (0.25 mm inner diameter (i.d.) to a 0.18 mm i.d. column) and low-pressure GC × GC-TOFMS configuration (0.53 mm i.d. to a 0.53 mm i.d. column). Elution temperatures, sensitivity, and peak capacity were investigated for both configurations. Compounds eluted an average of 30 °C less on the low-pressure GC × GC-TOFMS configuration compared to the conventional GC × GC-TOFMS configuration. Moreover, the compounds were separated in ∼13 min on the low-pressure GC × GC-TOFMS as opposed to 33 min for conventional GC × GC-TOFMS. However, due to the wide-bore columns and faster runtimes the low-pressure GC × GC-TOFMS had a lower, β corrected 2D peak capacity, nc,β,2D, of 1260 while the conventional GC × GC-TOFMS was 3588. Interestingly, both configurations yielded a similar peak capacity production of 93 peaks/min and 107 peaks/min for low-pressure and conventional GC × GC-TOFMS, respectively. A "real world" sample of diesel fuel was tested on the low-pressure and conventional GC × GC-TOFMS configurations and similar results were obtained compared to the pesticide standard mix except the peak capacity production of the low-pressure GC × GC-TOFMS configuration was higher than that of the conventional GC × GC-TOFMS method.

Development of variance rank initiated-unsupervised sample indexing for gas chromatography-mass spectrometry analysis

Traditional non-targeted chemometric workflows for gas chromatography-mass spectrometry (GC-MS) data rely on using supervised methods, which requires a priori knowledge of sample class membership. Herein, we propose a simple, unsupervised chemometric workflow known as variance rank initiated-unsupervised sample indexing (VRI-USI). VRI-USI discovers analyte peaks exhibiting high relative variance across all samples, followed by k-means clustering on the individual peaks. Based upon how the samples cluster for a given peak, a sample index assignment is provided. Using a probabilistic argument, if the same sample index assignment appears for several discovered peaks, then this outcome strongly suggests that the samples are properly classified by that particular sample index assignment. Thus, relevant chemical differences between the samples have been discovered in an unsupervised fashion. The VRI-USI workflow is demonstrated on three, increasingly difficult datasets: simulations, yeast metabolomics, and human cancer metabolomics. For simulated GC-MS datasets, VRI-USI discovered 85-90% of analytes modeled to vary between sample classes. Nineteen out of 53 peaks in the peak table developed for the yeast metabolome dataset had the same sample index assignments, indicating that those indices are most likely due to class-distinguishing chemical differences. A t-test revealed that 22 out of 53 peaks were statistically significant (p < 0.05) when using those sample index assignments. Likewise, for the human cancer metabolomics study, VRI-USI discovered 25 analytes that were statistically different (p < 0.05) using the sample index assignments determined to highlight meaningful sample-based differences. For all datasets, the sample index assignments that were deduced from VRI-USI were the correct class-based difference when using prior knowledge. VRI-USI holds promise as an exploratory data analysis workflow for studies in which analysts do not readily have a priori class information or want to uncover the underlying nature of their dataset.

Opportunities for green microextractions in comprehensive two-dimensional gas chromatography / mass spectrometry-based metabolomics - A review

Microextractions have become an attractive class of techniques for metabolomics. The most popular technique is solid-phase microextraction that revolutionized the field of modern sample preparation in the early nineties. Ever since this milestone, microextractions have taken on many principles and formats comprising droplets, fibers, membranes, needles, and blades. Sampling devices may be customized to impart exhaustive or equilibrium-based characteristics to the extraction method. Equilibrium-based approaches may rely on additional methods for calibration, such as diffusion-based or on-fiber kinetic calibration to improve bioanalysis. In addition, microextraction-based methods may enable minimally invasive sampling protocols and measure the average free concentration of analytes in heterogeneous multiphasic biological systems. On-fiber derivatization has evidenced new opportunities for targeted and untargeted analysis in metabolomics. All these advantages have highlighted the potential of microextraction techniques for in vivo and on-site sampling and sample preparation, while many opportunities are still available for laboratory protocols. In this review, we outline and discuss some of the most recent applications using microextractions techniques for comprehensive two-dimensional gas chromatography-based metabolomics, including potential research opportunities.

Linking the morphological and metabolomic response of Lactuca sativa L exposed to emerging contaminants using GC × GC-MS and chemometric tools

The occurrence of contaminants of emerging concern (CECs) in irrigation waters (up to low μg L-1) and irrigated crops (ng g-1 in dry weight) has been reported, but the linkage between plant morphological changes and plant metabolomic response has not yet been addressed. In this study, a non-targeted metabolomic analysis was performed on lettuce (Lactuca sativa L) exposed to 11 CECs (pharmaceuticals, personal care products, anticorrosive agents and surfactants) by irrigation. The plants were watered with different CEC concentrations (0-50 µg L-1) for 34 days under controlled conditions and then harvested, extracted, derivatised and analysed by comprehensive two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer (GC × GC-TOFMS). The resulting raw data were analysed using multivariate curve resolution (MCR) and partial least squares (PLS) methods. The metabolic response indicates that exposure to CECs at environmentally relevant concentrations (0.05 µg L-1) can cause significant metabolic alterations in plants (carbohydrate metabolism, the citric acid cycle, pentose phosphate pathway and glutathione pathway) linked to changes in morphological parameters (leaf height, stem width) and chlorophyll content.

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

Web Server for Peak Detection, Baseline Correction, and Alignment in Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics Data

Two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) is superior for chromatographic separation and provides great sensitivity for complex biological fluid analysis in metabolomics. However, GC×GC/TOF-MS data processing is currently limited to vendor software and typically requires several preprocessing steps. In this work, we implement a web-based platform, which we call GC²MS, to facilitate the application of recent advances in GC×GC/TOF-MS, especially for metabolomics studies. The core processing workflow of GC²MS consists of blob/peak detection, baseline correction, and blob alignment. GC²MS treats GC×GC/TOF-MS data as pictures and clusters the pixels as blobs according to the brightness of each pixel to generate a blob table. GC²MS then aligns the blobs of two GC×GC/TOF-MS data sets according to their distance and similarity. The blob distance and similarity are the Euclidean distance of the first and second retention times of two blobs and the Pearson's correlation coefficient of the two mass spectra, respectively. GC²MS also directly corrects the raw data baseline. The analytical performance of GC²MS was evaluated using GC×GC/TOF-MS data sets of Angelica sinensis compounds acquired under different experimental conditions and of human plasma samples. The results show that GC²MS is an easy-to-use tool for detecting peaks and correcting baselines, and GC²MS is able to align GC×GC/TOF-MS data sets acquired under different experimental conditions. GC²MS is freely accessible at http://gc2ms.web.cmdm.tw .

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.

Pixel-based analysis of comprehensive two-dimensional gas chromatograms (color plots) of petroleum: a tutorial

We demonstrate how to process comprehensive two-dimensional gas chromatograms (GC × GC chromatograms) to remove nonsample information (artifacts), including background and retention time shifts. We also demonstrate how this, combined with further reduction of the influence of irrelevant information, allows for data analysis without integration or peak deconvolution (pixel-based analysis).

Integrative biological analysis for neuropsychopharmacology

Although advances in psychotherapy have been made in recent years, drug discovery for brain diseases such as schizophrenia and mood disorders has stagnated. The need for new biomarkers and validated therapeutic targets in the field of neuropsychopharmacology is widely unmet. The brain is the most complex part of human anatomy from the standpoint of number and types of cells, their interconnections, and circuitry. To better meet patient needs, improved methods to approach brain studies by understanding functional networks that interact with the genome are being developed. The integrated biological approaches--proteomics, transcriptomics, metabolomics, and glycomics--have a strong record in several areas of biomedicine, including neurochemistry and neuro-oncology. Published applications of an integrated approach to projects of neurological, psychiatric, and pharmacological natures are still few but show promise to provide deep biological knowledge derived from cells, animal models, and clinical materials. Future studies that yield insights based on integrated analyses promise to deliver new therapeutic targets and biomarkers for personalized medicine.

Headspace SPME-GC-MS metabolomics analysis of urinary volatile organic compounds (VOCs)

In this chapter, we describe the procedure for metabolic profiling of hundreds of urinary volatile organic compounds (VOCs) using headspace solid-phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS). SPME is a solvent-free technology that is reproducible, fast, cost effective, and versatile in extracting small molecular weight organic compounds (metabolites) from biofluids using a fiber that is coated with an extracting phase. After extraction, the SPME fiber can be directly injected to the GC-MS, where the extracted metabolites will desorb thermally from the fiber, elute along a GC column, and finally enter into the MS for detection. The analysis of urine samples is presented using this approach.

Non-targeted analysis of petroleum metabolites in groundwater using GC×GC-TOFMS

Groundwater at fuel release sites often contains nonpolar hydrocarbons that originate from both the fuel release and other environmental sources, as well as polar metabolites of petroleum biodegradation. These compounds, along with other polar artifacts, can be quantified as "total petroleum hydrocarbons" using USEPA Methods 3510/8015B, unless a silica gel cleanup step is used to separate nonpolar hydrocarbons from polar compounds prior to analysis. Only a limited number of these metabolites have been identified by traditional GC-MS methods, because they are difficult to resolve using single-column configurations. Additionally, the targeted use of derivatization limits the detection of many potential metabolites of interest. The objective of this research was to develop a nontargeted GC×GC-TOFMS approach to characterize petroleum metabolites in environmental samples gathered from fuel release sites. The method tentatively identified more than 760 unique polar compounds, including acids/esters, alcohols, phenols, ketones, and aldehydes, from 22 groundwater samples collected at five sites. Standards for 28 polar compounds indicate that effective limits of quantitation for most of these compounds in the groundwater samples range from 1 to 11 μg/L.