Comparison of GC-MS and GC×GC-MS in the analysis of human serum samples for biomarker discovery

GC × GC-HRMS with complementary ionization methods in the suspect screening analysis of fragrance allergens: overwhelming or justified?

Modern gas chromatography-mass spectrometry (GC-MS) allows for the analysis of complex samples, such as fragrances. However, identifying all the constituents in natural fragrance mixtures, especially allergens that need to be listed on product labels, is a significant challenge. This is primarily due to the high complexity of the sample and the fact that electron ionization, the most commonly used ionization method in GC-MS, produces numerous nonspecific fragment ions, often resulting in the absence or very low abundance of the molecular ion. These factors affect confidence in assigning the analyte. In this study, we demonstrate that the combination of GC × GC separation, with high mass resolution and accurate mass measurements, as well as chemical ionization in addition to traditional electron ionization, becomes an efficient tool for reliable qualitative analysis of a mixture containing 100 fragrance allergens, even when many of them are closely related species or isomers. The proposed approach expands the applicability of the comprehensive GC × GC-HRMS method, which includes complementary ionization techniques, from studies on anthropogenic priority pollutants and emerging contaminants to the analysis of natural products. Although targeted qualitative and quantitative analysis of allergens in the modern laboratories is well organized, GC × GC-HRMS, being a useful complement to routine quality control of volatile allergens in fragrances, definitely gives an additional contribution to the analytical cases when conventional 1D-GC-MS faces some problems or uncertainties.

Screening Disinfection Byproducts in Arid-Coastal Wastewater: A Workflow Using GC×GC-TOFMS, Passive Sampling, and NMF Deconvolution Algorithm

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts-with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks-significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks.

Non-targeted analytical comparison of a heated tobacco product aerosol against mainstream cigarette smoke: does heating tobacco produce an inherently different set of aerosol constituents?

Smoking-related diseases remain a significant public health concern, and heated tobacco products (HTPs) have emerged as a potential alternative to cigarettes. While several studies have confirmed that HTP aerosols contain lower levels of harmful and potentially harmful constituents (HPHCs) than cigarette smoke, less is known about constituents that are intrinsically higher in HTP aerosols. This study provides a comprehensive comparative assessment of an HTP aerosol produced with Tobacco Heating System 2.2 (THS) and comparator cigarette (CC) smoke aiming at identifying all unique or increased compounds in THS aerosol by applying a broad set of LC-MS and GC × GC-MS methods. To focus on differences due to heating versus burning tobacco, confounding factors were minimized by using the same tobacco in both test items and not adding flavorants. Of all analytical features, only 3.5%-corresponding to 31 distinctive compounds-were significantly more abundant in THS aerosol than in CC smoke. A notable subset of these compounds was identified as reaction products of glycerol. The only compound unique to THS aerosol was traced back to its presence in a non-tobacco material in the test item and not a direct product of heating tobacco. Our results demonstrate that heating a glycerol-containing tobacco substrate to the temperatures applied in THS does not introduce new compounds in the resulting aerosol compared to CC smoke which are detectable with the method portfolio applied in this study. Overall, this study contributes to a better understanding of the chemical composition of HTP aerosols and their potential impact on human health.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Hair and Nail-On-Chip for Bioinspired Microfluidic Device Fabrication and Biomarker Detection

The advent of biosensors has tremendously increased our potential of identifying and solving important problems in various domains, ranging from food safety and environmental analysis, to healthcare and medicine. However, one of the most prominent drawbacks of these technologies, especially in the biomedical field, is to employ conventional samples, such as blood, urine, tissue extracts and other body fluids for analysis, which suffer from the drawbacks of invasiveness, discomfort, and high costs encountered in transportation and storage, thereby hindering these products to be applied for point-of-care testing that has garnered substantial attention in recent years. Therefore, through this review, we emphasize for the first time, the applications of switching over to noninvasive sampling techniques involving hair and nails that not only circumvent most of the aforementioned limitations, but also serve as interesting alternatives in understanding the human physiology involving minimal costs, equipment and human interference when combined with rapidly advancing technologies, such as microfluidics and organ-on-a-chip to achieve miniaturization on an unprecedented scale. The coalescence between these two fields has not only led to the fabrication of novel microdevices involving hair and nails, but also function as robust biosensors for the detection of biomarkers, chemicals, metabolites and nucleic acids through noninvasive sampling. Finally, we have also elucidated a plethora of futuristic innovations that could be incorporated in such devices, such as expanding their applications in nail and hair-based drug delivery, their potential in serving as next-generation wearable sensors and integrating these devices with machine-learning for enhanced automation and decentralization.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Characterization of key volatile compounds in jasmine tea infusion with different amount of flowers

The quality of jasmine tea is related to the volatiles of its infusion. In this study, the volatiles of jasmine tea infusion were extracted under the optimal conditions with a 50/30 μm DVB/CAR/PDMS fiber, tea/water ratio of 1:25 and extraction time of 5 min. A total of 204 volatiles were analyzed by comprehensive two-dimensional gas chromatography-quadrupole time-of-flight mass spectrometry (GC × GC-Q-TOF-MS). Twenty-five compounds were identified as the key volatile compounds by fold change (FC), orthogonal partial least squares discriminant analysis (OPLS-DA), and two-way orthogonal partial least squares analysis (O2PLS). Then optimal amount of flowers (80%-120%) was obtained by the equation describing key volatiles and quality of jasmine tea infusion. And 80% amount of flowers was more appropriate considering the production cost and more pleasant taste. This study laid a foundation for the extraction and research of volatiles of tea infusion and guided the reasonable amount of flowers to produce jasmine tea.

Exploring the interrelationship between the skin microbiome and skin volatiles: A pilot study

Unravelling the interplay between a human’s microbiome and physiology is a relevant task for understanding the principles underlying human health and disease. With regard to human chemical communication, it is of interest to elucidate the role of the microbiome in shaping or generating volatiles emitted from the human body. In this study, we characterized the microbiome and volatile organic compounds (VOCs) sampled from the neck and axilla of ten participants (five male, five female) on two sampling days, by applying different methodological approaches. Volatiles emitted from the respective skin site were collected for 20 min using textile sampling material and analyzed on two analytical columns with varying polarity of the stationary phase. Microbiome samples were analyzed by a culture approach coupled with MALDI-TOF-MS analysis and a 16S ribosomal RNA gene (16S RNA) sequencing approach. Statistical and advanced data analysis methods revealed that classification of body sites was possible by using VOC and microbiome data sets. Higher classification accuracy was achieved by combination of both data pools. Cutibacterium, Staphylococcus, Micrococcus, Streptococcus, Lawsonella, Anaerococcus, and Corynebacterium species were found to contribute to classification of the body sites by the microbiome. Alkanes, esters, ethers, ketones, aldehydes and cyclic structures were used by the classifier when VOC data were considered. The interdisciplinary methodological platform developed here will enable further investigations of skin microbiome and skin VOCs alterations in physiological and pathological conditions.

GC × GC-TOFMS metabolomics analysis identifies elevated levels of plasma sugars and sugar alcohols in diabetic mellitus patients with kidney failure

Two dimensional GC (GC × GC)-time-of-flight mass spectrometry (TOFMS) has been used to improve accurate metabolite identification in the chemical industry, but this method has not been applied as readily in biomedical research. Here, we evaluated and validated the performance of high resolution GC × GC-TOFMS against that of GC-TOFMS for metabolomics analysis of two different plasma matrices, from healthy controls (CON) and diabetes mellitus (DM) patients with kidney failure (DM with KF). We found GC × GC-TOFMS outperformed traditional GC-TOFMS in terms of separation performance and metabolite coverage. Several metabolites from both the CON and DM with KF matrices, such as carbohydrates and carbohydrate-conjugate metabolites, were exclusively detected using GC × GC-TOFMS. Additionally, we applied this method to characterize significant metabolites in the DM with KF group, with focused analysis of four metabolite groups: sugars, sugar alcohols, amino acids, and free fatty acids. Our plasma metabolomics results revealed 35 significant metabolites (12 unique and 23 concentration-dependent metabolites) in the DM with KF group, as compared with those in the CON and DM groups (N = 20 for each group). Interestingly, we determined 17 of the 35 (14/17 verified with reference standards) significant metabolites identified from both the analyses were metabolites from the sugar and sugar alcohol groups, with significantly higher concentrations in the DM with KF group than in the CON and DM groups. Enrichment analysis of these 14 metabolites also revealed that alterations in galactose metabolism and the polyol pathway are related to DM with KF. Overall, our application of GC × GC-TOFMS identified key metabolites in complex plasma matrices.

Metabolomic profiling relates tianeptine effectiveness with hippocampal GABA, myo-inositol, cholesterol, and fatty acid metabolism restoration in socially isolated rats

RATIONALE

Discovering biomarkers of major depressive disorder (MDD) can give a deeper understanding of this mood disorder and improve the ability to screen for, diagnose, and treat MDD.

OBJECTIVES

In this study, metabolomics was used in unraveling metabolite fluctuations of MDD and drug outcome by creating specific metabolomic fingerprints. We report metabolomic patterns of change of the hippocampus of adult male Wistar rats following chronic social isolation (CSIS) (6 weeks), an animal model of depression, and/or chronic tianeptine (Tian) treatment (10 mg kg^-1 per day) (lasting 3 weeks of 6-week CSIS), monitored by using comprehensive GC × GC-MS.

RESULTS

The comparative metabolomic analysis highlighted the role of gamma aminobutyric acid (GABA), iso-allocholate, and unsaturated fatty acid metabolism alterations following the CSIS, which was corroborated with moderate to strong negative Pearson's correlation of GABA, docosahexaenoic, 9-hexadecenoic acid, 5,8,11,14-eicosatetraynoic, and arachidonic acids with immobility behavior in the forced swim test. The antidepressant effect of Tian restored GABA levels, which was absent in Tian resilient rats. Tian decreased myo-inositol and increased TCA cycle intermediates, amino acids, and cholesterol and its metabolite. As key molecules of divergence between Tian effectiveness and resilience, metabolomics revealed myo-inositol, GABA, cholesterol, and its metabolite. A significant moderate positive correlation between myo-inositol and immobility was revealed. Tian probably acted by upregulating NMDAR's and α₂ adrenergic receptors (AR) or norepinephrine transporter in both control and stressed animals.

CONCLUSION

Metabolomics revealed several dysregulations underlying CSIS-induced depressive-like behavior and responsiveness to Tian, predominantly converging into NMDAR-mediated glutamate and myo-inositol signalization and GABA inhibitory pathways.

A Liquid Biopsy in Bladder Cancer—The Current Landscape in Urinary Biomarkers

The non-muscle invasive bladder cancer tends to recur and progress. Therefore, it requires frequent follow-ups, generating costs and making it one of the most expensive neoplasms. Considering the expensive and invasive character of the current gold-standard diagnostic procedure, white-light cystoscopy, efforts to find an alternative method are ongoing. Although the last decade has seen significant advancements in urinary biomarker tests (UBTs) for bladder cancer, international guidelines have not recommended them. Currently, the paramount urgency is to find and validate the test with the best specificity and sensitivity, which would allow for the optimizing of diagnosis, prognosis, and a treatment plan. This review aims to summarise the up-to-date state of knowledge relating to UBTs and new developments in the detection, prognosis, and surveillance of bladder cancer and their potential applications in clinical practice.

Evaluation of fresh, frozen, and lyophilized fecal samples by SPME and derivatization methods using GC×GC-TOFMS

INTRODUCTION

Feces is a highly complex matrix containing thousands of metabolites. It also contains live bacteria and enzymes, and does not have a static chemistry. Consequently, proper control of pre-analytical parameters is critical to minimize unwanted variations in the samples. However, no consensus currently exists on how fecal samples should be stored/processed prior to analysis.

OBJECTIVE

The effects of sample handling conditions on fecal metabolite profiles and abundances were examined using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS).

METHODS

Solid-phase microextraction (SPME) and derivatization via trimethylsilylation (TMS) were employed as complementary techniques to evaluate fresh, frozen, and lyophilized fecal samples with expanded coverage of the fecal metabolome. The total number of detected peaks and the signal intensities were compared among the different handling conditions.

RESULTS

Our analysis revealed that the metabolic profiles of fecal samples depend greatly on sample handling and processing conditions, which had a more pronounced effect on results obtained by SPME than by TMS derivatization. Overall, lyophilization resulted in a greater amount of total and class-specific metabolites, which may be attributed to cell lysis and/or membrane disintegration.

CONCLUSIONS

A comprehensive comparison of the sample handling conditions provides a deeper understanding of the physicochemical changes that occur within the samples during freezing and lyophilization. Based on our results, snap-freezing at -80 °C would be preferred over lyophilization for handling samples in the field of fecal metabolomics as this imparts the least change from the fresh condition.

CRISP: a deep learning architecture for GC × GC-TOFMS contour ROI identification, simulation and analysis in imaging metabolomics

Two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) provides a large amount of molecular information from biological samples. However, the lack of a comprehensive compound library or customizable bioinformatics tool is currently a challenge in GC × GC-TOFMS data analysis. We present an open-source deep learning (DL) software called contour regions of interest (ROI) identification, simulation and untargeted metabolomics profiler (CRISP). CRISP integrates multiple customizable deep neural network architectures for assisting the semi-automated identification of ROIs, contour synthesis, resolution enhancement and classification of GC × GC-TOFMS-based contour images. The approach includes the novel aggregate feature representative contour (AFRC) construction and stacked ROIs. This generates an unbiased contour image dataset that enhances the contrasting characteristics between different test groups and can be suitable for small sample sizes. The utility of the generative models and the accuracy and efficacy of the platform were demonstrated using a dataset of GC × GC-TOFMS contour images from patients with late-stage diabetic nephropathy and healthy control groups. CRISP successfully constructed AFRC images and identified over five ROIs to create a deepstacked dataset. The high fidelity, 512 × 512-pixels generative model was trained as a generator with a Fréchet inception distance of <47.00. The trained classifier achieved an AUROC of >0.96 and a classification accuracy of >95.00% for datasets with and without column bleed. Overall, CRISP demonstrates good potential as a DL-based approach for the rapid analysis of 4-D GC × GC-TOFMS untargeted metabolite profiles by directly implementing contour images. CRISP is available at https://github.com/vivekmathema/GCxGC-CRISP.

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Integrated omics analysis reveals sirtuin signaling is central to hepatic response to a high fructose diet

BACKGROUND

Dietary high fructose (HFr) is a known metabolic disruptor contributing to development of obesity and diabetes in Western societies. Initial molecular changes from exposure to HFr on liver metabolism may be essential to understand the perturbations leading to insulin resistance and abnormalities in lipid and carbohydrate metabolism. We studied vervet monkeys (Clorocebus aethiops sabaeus) fed a HFr (n=5) or chow diet (n=5) for 6 weeks, and obtained clinical measures of liver function, blood insulin, cholesterol and triglycerides. In addition, we performed untargeted global transcriptomics, proteomics, and metabolomics analyses on liver biopsies to determine the molecular impact of a HFr diet on coordinated pathways and networks that differed by diet.

RESULTS

We show that integration of omics data sets improved statistical significance for some pathways and networks, and decreased significance for others, suggesting that multiple omics datasets enhance confidence in relevant pathway and network identification. Specifically, we found that sirtuin signaling and a peroxisome proliferator activated receptor alpha (PPARA) regulatory network were significantly altered in hepatic response to HFr. Integration of metabolomics and miRNAs data further strengthened our findings.

CONCLUSIONS

Our integrated analysis of three types of omics data with pathway and regulatory network analysis demonstrates the usefulness of this approach for discovery of molecular networks central to a biological response. In addition, metabolites aspartic acid and docosahexaenoic acid (DHA), protein ATG3, and genes ATG7, and HMGCS2 link sirtuin signaling and the PPARA network suggesting molecular mechanisms for altered hepatic gluconeogenesis from consumption of a HFr diet.

Characterizing Multidevice Capillary Vibrating Sharp-Edge Spray Ionization for In-Droplet Hydrogen/Deuterium Exchange to Enhance Compound Identification

Multidevice capillary vibrating sharp-edge spray ionization (cVSSI) source parameters have been examined to determine their effects on conducting in-droplet hydrogen/deuterium exchange (HDX) experiments. Control experiments using select compounds indicate that the observed differences in mass spectral isotopic distributions obtained upon initiation of HDX result primarily from solution-phase reactions as opposed to gas-phase exchange. Preliminary studies have determined that robust HDX can only be achieved with the application of same-polarity voltage to both the analyte and the deuterium oxide reagent (D₂O) cVSSI devices. Additionally, a similar HDX reactivity dependence on the voltage applied to the D₂O device for various analytes is observed. Analyte and reagent flow experiments show that, for the multidevice cVSSI setup employed, there is a nonlinear dependence on the D₂O reagent flow rate; increasing the D₂O reagent flow by 100% results in only an ∼10-20% increase in deuterium incorporation for this setup. Instantaneous (subsecond) response times have been demonstrated in the initiation or termination of HDX, which is achieved by turning on or off the reagent cVSSI device piezoelectric transducer. The ability to distinguish isomeric species by in-droplet HDX is presented. Finally, a demonstration of a three-component cVSSI device setup to perform multiple (successive or in combination) in-droplet chemistries to enhance compound ionization and identification is presented and a hypothetical metabolomics workflow consisting of successive multidevice activation is briefly discussed.

Automated Screening and Filtering Scripts for GC×GC-TOFMS Metabolomics Data

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a powerful tool for the analysis of complex mixtures, and it is ideally suited to discovery studies where the entire sample is potentially of interest. Unfortunately, when unit mass resolution mass spectrometers are used, many detected compounds have spectra that do not match well with libraries. This could be due to the compound not being in the library, or the compound having a weak/nonexistent molecular ion cluster. While high-speed, high-resolution mass spectrometers, or ion sources with softer ionization than 70 eV electron impact (EI) may help with some of this, many GC×GC systems presently in use employ low-resolution mass spectrometers and 70 eV EI ionization. Scripting tools that apply filters to GC×GC-TOFMS data based on logical operations applied to spectral and/or retention data have been used previously for environmental and petroleum samples. This approach rapidly filters GC×GC-TOFMS peak tables (or raw data) and is available in software from multiple vendors. In this work, we present a series of scripts that have been developed to rapidly classify major groups of compounds that are of relevance to metabolomics studies including: fatty acid methyl esters, free fatty acids, aldehydes, alcohols, ketones, amino acids, and carbohydrates.

Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics

BACKGROUND

Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks.

RESULTS

We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism.

CONCLUSIONS

In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.

Unique ion filter-A data reduction tool for chemometric analysis of raw comprehensive two-dimensional gas chromatography-mass spectrometry data

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one- and two-dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over-redundancy in two-dimensional gas chromatography-mass spectrometry data by limiting the data to a few unique/pseudo-unique ions, sub-peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two-dimensional gas chromatography-mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two-dimensional gas chromatography-mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo-unique ions, one sub-peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy.

Headspace analysis of E-cigarette fluids using comprehensive two dimensional GC×GC-TOF-MS reveals the presence of volatile and toxic compounds

The analysis of electronic cigarrete (E-cigarette) fluids by high performance liquid chromatography or gas chromatography (GC) coupled to mass spectrometry (MS), GC hyphenated to flame-ionisation detection, or nuclear magnetic resonance spectroscopy poses many challenges due to the complex matrix and extremely high number of compounds present. In order to overcome these challenges, this study focused on the detection of the multiple complex compounds classes produced by the pyrolysis of E-cigarette liquids using comprehensive two dimensional gas chromatography (GCxGC) coupled to time of flight (TOF)-MS. Gas samples were prepared by heating E-liquids inside aluminium tins for 5 min. The tins were placed in a sand bath, which was temperature controlled at 200 °C. The samples were collected using thermal desorption tubes connected to volatile organic compound (VOC) sampling pump attached and subsequently analysed using GCxGC-TOF-MS. The greater peak resolution obtained when using GCxGC-TOF-MS allowed to distinguish many toxic compounds and VOCs that could not be detected by the other methods mentioned above. As a result, a comprehensive list of volatile compounds emitted from E-cigarette fluids when heated was established, which might allow a better understanding of potential health effects of vaping. Heating E-liquids to moderate temperature results in the emission of over 1000 volatile compounds of which over 150 are toxic. These compounds are either present in the liquid or can be formed during storage or heating leading to a more complex volatile profile of E-cigarette liquids than previously assumed. The application of GCxGC-TOF-MS allows the elucidation of this profile and therefore a better understanding of possible health implications.

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).

Technologies to improve the sensitivity of existing chromatographic methods used for bioanalytical studies

Chromatographic method has long been recognized as the most widely used separation method in bioanalytical research. However, the relatively low sensitivity of existing chromatographic methods remains a significant challenge, as the requirements for experimental procedures become more demanding. This review discusses the main causes for the low sensitivity of chromatographic methods and aims to introduce different technologies for enhancing their sensitivity in the following aspects: (i) different pretreatment methods for improving clean-up efficiency and recovery; (ii) derivatization step for altering the chromatographic behavior of analytes and enhancing MS ionization efficiency; (iii) optimal LC-MS conditions and appropriate separation mechanism; and (iv) applications of other chromatographic methods, including miniaturized LC, 2D-LC, 2D-GC, and supercritical fluid chromatography. Altogether, this review is devoted to summarizing the recent technologies reported in the literature and providing new strategies for the detection of bioanalytes.

Using Multiple Analytical Platforms to Investigate the Androgen Depletion Effects on Fecal Metabolites in a Mouse Model of Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by circulating autoantibodies that deposit in target organs (e.g., kidneys), resulting in chronic inflammation and eventual destruction of the organ. SLE is much more prevalent in females than males in both humans and spontaneous mouse models of lupus, such as NZBxNZW F1 (BWF1) mice. Depleting androgens by castration dramatically increases the susceptibility of BWF1 male to lupus. We compared fecal metabolite profiles of castrated BWF1 (androgen-depleted) male, intact (androgen-replete) male, and female mice. Four analytical platforms were employed to study the profiles of polar metabolites in mouse feces collected from adult BWF1 mice, and a total of 435 metabolites was identified. Of these, the abundance levels of 72 metabolites were significantly different between castrated and intact male groups, and 63 metabolites were different between female and male groups. Pathway analysis indicated that the pathway differences between castrated and intact male mice closely resembled the pathway differences between female and intact male mice, suggesting that low levels of androgens, whether due to depletion (castrated male) or endogenous (female), are associated with multiple fecal metabolomic alterations, which could potentially affect SLE progression. Our findings demonstrate that analyzing fecal metabolites using multiple analytical platforms holds great promise for detecting metabolomic alterations in complex disease model systems.

Integrating comprehensive two-dimensional gas chromatography mass spectrometry and parallel two-dimensional liquid chromatography mass spectrometry for untargeted metabolomics

The diverse characteristics and large number of entities make metabolite separation challenging in metabolomics. To date, there is not a singular instrument capable of analyzing all types of metabolites. In order to achieve a better separation for higher peak capacity and accurate metabolite identification and quantification, we integrated GC × GC-MS and parallel 2DLC-MS for analysis of polar metabolites. To test the performance of the developed system, 13 rats were fed different diets to form two animal groups. Polar metabolites extracted from rat livers were analyzed by GC × GC-MS, parallel 2DLC-MS (-) and parallel 2DLC-MS (+), respectively. By integrating all data together, 58 metabolites were detected with significant change in their abundance levels between groups (p≤ 0.05). Of the 58 metabolites, three metabolites were detected in two platforms and two in all three platforms. Manual examination showed that discrepancy of metabolite regulation measured by different platforms was mainly caused by the poor shape of chromatographic peaks resulting from low instrument response. Pathway analysis demonstrated that integrating the results from multiple platforms increased the confidence of metabolic pathway assignment.

Global Plasma Profiling for Colorectal Cancer-Associated Volatile Organic Compounds: a Proof-of-Principle Study

Volatile organic compounds (VOCs) could reflect changes resulting from ongoing pathophysiological processes and altered body metabolisms, and thus have been studied for various types of cancers. We aimed to test an advanced global metabolomic technique to characterize circulating VOCs in patients diagnosed with colorectal cancer (CRC). We employed solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography mass-spectrometry (GC × GC-MS). We analyzed 30 random plasma samples from incident cases of CRC. The 30 samples were from population controls enrolled in a large population-based case-control study. The number of metabolite peaks detected in the cases was significantly lower than that detected in the controls (median 1530 vs. 1694, P = 0.02). Partial least squares-discriminant analysis showed clear VOC profile differences between the CRC and the controls. After adjustment for multiple comparisons at the 5% false discovery rate level, five VOCs were differentially expressed between the cases and the controls. Among these five VOCs, 2,3,4-trimethyl-hexane (decreased) and 2,4-dimethylhept-1-ene (increased) were both lipid peroxidation products but not previously reported for CRC. In summary, this study pointed to an intriguing observation that the richness of volatile metabolites may be reduced in CRC cases and demonstrated the utility of SPME GC × GC-MS in discovery of candidate markers for further validation.

Metabolome-based signature of disease pathology in MS

BACKGROUND

Diagnostic delays are common for multiple sclerosis (MS) since diagnosis typically depends on the presentation of nonspecific clinical symptoms together with radiologically-determined central nervous system (CNS) lesions. It is important to reduce diagnostic delays as earlier initiation of disease modifying therapies mitigates long-term disability. Developing a metabolomic blood-based MS biomarker is attractive, but prior efforts have largely focused on specific subsets of metabolite classes or analytical platforms. Thus, there are opportunities to interrogate metabolite profiles using more expansive and comprehensive approaches for developing MS biomarkers and for advancing our understanding of MS pathogenesis.

METHODS

To identify putative blood-based MS biomarkers, we comprehensively interrogated the metabolite profiles in 12 non-Hispanic white, non-smoking, male MS cases who were drug naïve for 3 months prior to biospecimen collection and 13 non-Hispanic white, non-smoking male controls who were frequency matched to cases by age and body mass index. We performed untargeted two-dimensional gas chromatography and time-of-flight mass spectrometry (GCxGC-TOFMS) and targeted lipidomic and amino acid analysis on serum. 325 metabolites met quality control and supervised machine learning was used to identify metabolites most informative for MS status. The discrimination potential of these select metabolites were assessed using receiver operator characteristic curves based on logistic models; top candidate metabolites were defined as having area under the curves (AUC) >80%. The associations between whole-genome expression data and the top candidate metabolites were examined, followed by pathway enrichment analyses. Similar associations were examined for 175 putative MS risk variants and the top candidate metabolites.

RESULTS

12 metabolites were determined to be informative for MS status, of which 6 had AUCs >80%: pyroglutamate, laurate, acylcarnitine C14:1, N-methylmaleimide, and 2 phosphatidylcholines (PC ae 40:5, PC ae 42:5). These metabolites participate in glutathione metabolism, fatty acid metabolism/oxidation, cellular membrane composition, and transient receptor potential channel signaling. Pathway analyses based on the gene expression association for each metabolite suggested enrichment for pathways associated with apoptosis and mitochondrial dysfunction. Interestingly, the predominant MS genetic risk allele HLA-DRB1×15:01 was associated with one of the 6 top metabolites.

CONCLUSION

Our analysis represents the most comprehensive description of metabolic changes associated with MS in serum, to date, with the inclusion of genomic and genetic information. We identified atypical metabolic processes that differed between MS patients and controls, which may enable the development of biological targets for diagnosis and treatment.

Rice Grain Quality Benchmarking Through Profiling of Volatiles and Metabolites in Grains Using Gas Chromatography Mass Spectrometry

Gas chromatograph coupled with mass spectrometer is widely used to profile volatiles and metabolites from the homogenized rice flour obtained from mature grains. Rice grains consist of central endosperm which stores majorly starch and, in addition, accumulate various storage proteins as storage reserves. The outer nutritious aleurone layer stores lipids, sugar alcohols, volatiles, antioxidants, vitamins, and various micronutrients. Once paddy sample is dehulled, milled, and ground cryogenically, the brown rice flour is subjected to extraction of primary metabolites and volatiles using an appropriate extraction method. In metabolite profiling of the liquid extract obtained from the rice sample, mixture is initially subjected to methoxyamination then silylation before being subjected to untargeted metabolite profiling. Peaks obtained are processed for noise reduction and specific signal selection. Volatile compounds are initially extracted using a solid phase adsorbent prior to analysis. All these compounds, metabolites, and volatiles are detected in the mass selective detector by fragmentation at 70 eV ionization energy and the resultant mass spectrum compared with a built-in library of compounds. Data mined from the gas chromatography mass spectrometry analysis are then subjected to post-processing statistical analysis.

Advances in the application of comprehensive two-dimensional gas chromatography in metabolomics

Due to excellent separation capacity for complex mixtures of chemicals, comprehensive two-dimensional gas chromatography (GC × GC) is being utilized with increasing frequency for metabolomics analyses. This review describes recent advances in GC × GC method development for metabolomics, organismal sampling techniques compatible with GC × GC, metabolomic discoveries made using GC × GC, and recommendations and best practices for collecting and reporting GC × GC metabolomics data.

Analysis of volatile components in Dalbergia cochinchinensis Pierre by a comprehensive two-dimensional gas chromatography with mass spectrometry method using a solid-state modulator

Volatile components in Dalbergia cochinchinensis Pierre were analysed using a comprehensive two-dimensional gas chromatography with mass spectrometry method featuring a new solid-state modulator. Compared to one-dimensional gas chromatography, the number of detected peaks were significantly increased. A total of 45 major compounds were identified in this study and the forward and reverse match factors of these compounds were both above 800. The results showed that the volatile components in Dalbergia cochinchinensis Pierre were primarily aldehyde and ketone compounds such as benzaldehyde, cinnamaldehyde, 4-chromanone, 1-(2-hydroxyphenyl)ethanone and acetophenone. In addition, a semi-quantitative analysis was conducted to determine the contents of the detected compounds based on peak area percentage. Moreover, the repeatability of the comprehensive two-dimensional gas chromatography-mass spectrometry analysis in this study was quite satisfactory with relative standard deviations less than 12.7% for intraday and 17.3% for interday measurements.

High-resolution gas chromatography/mass spectrometry metabolomics of non-human primate serum

RATIONALE

Metabolomics analyses using gas chromatography/mass spectrometry (GC/MS)-based metabolomics are heavily impeded by the lack of high-resolution mass spectrometers and limited spectral libraries to complement the excellent chromatography that GC platforms offer, a challenge that is being addressed with the implementation of high-resolution (HR) platforms such as 1D-GC/Orbitrap-MS.

METHODS

We used serum samples from a non-human primate (NHP), a baboon (Papio hamadryas), with suitable quality controls to quantify the chemical space using an advanced HRMS platform for confident metabolite identification and robust quantification to assess the suitability of the platform for routine clinical metabolomics research. In a complementary approach, we also analyzed the same serum samples using two-dimensional gas chromatography/time-of-flight mass spectrometry (2D-GC/TOF-MS) for metabolite identification and quantification following established standard protocols.

RESULTS

Overall, the 2D-GC/TOF-MS (~5000 peaks per sample) and 1D-GC/Orbitrap-MS (~500 peaks per sample) analyses enabled identification and quantification of a total of 555 annotated metabolites from the NHP serum with a spectral similarity score Rsim  ≥ 900 and signal-to-noise (S/N) ratio of >25. A common set of 30 metabolites with HMDB and KEGG IDs was quantified in the serum samples by both platforms where 2D-GC/TOF-MS enabled quantification of a total 384 metabolites (118 HMDB IDs) and 1D-GC/Orbitrap-MS analysis quantification of a total 200 metabolites (47 HMDB IDs). Thus, roughly 30-70% of the peaks remain unidentified or un-annotated across both platforms.

CONCLUSIONS

Our study provides insights into the benefits and limitations of the use of a higher mass resolution and mass accuracy instrument for untargeted GC/MS-based metabolomics with multi-dimensional chromatography in future studies addressing clinical conditions or exposome studies.

Optimized GC-MS metabolomics for the analysis of kidney tissue metabolites

INTRODUCTION

Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites.

OBJECTIVES

As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways.

METHODS

Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC-ToF-MS) platform after methoximation and silylation.

RESULTS

Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites.

CONCLUSION

This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

Analysis of Polycyclic Aromatic Hydrocarbons in Ambient Aerosols by Using One-Dimensional and Comprehensive Two-Dimensional Gas Chromatography Combined with Mass Spectrometric Method: A Comparative Study

Advanced separation technology paired with mass spectrometry is an ideal method for the analysis of atmospheric samples having complex chemical compositions. Due to the huge variety of both natural and anthropogenic sources of organic compounds, simultaneous quantification and identification of organic compounds in aerosol samples represents a demanding analytical challenge. In this regard, comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS) has become an effective analytical method. However, verification and validation approaches to quantify these analytes have not been critically evaluated. We compared the performance of gas chromatography with quadrupole mass spectrometry (GC-qMS) and GC×GC-TOFMS for quantitative analysis of eighteen target polycyclic aromatic hydrocarbons (PAHs). The quantitative obtained results such as limits of detection (LODs), limits of quantification (LOQs), and recoveries of target PAHs were approximately equivalent based on both analytical methods. Furthermore, a larger number of analytes were consistently identified from the aerosol samples by GC×GC-TOFMS compared to GC-qMS. Our findings suggest that GC×GC-TOFMS would be widely applicable to the atmospheric and related sciences with simultaneous target and nontarget analysis in a single run.

Surface fitting for calculating the second dimension retention index in comprehensive two-dimensional gas chromatography mass spectrometry

Comprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS) has been widely used for analysis of volatile compounds. However, the second dimension retention index (I) of each compound is not widely used to aid compound identification owing to the limited accuracy of I calculation. We report a surface fitting approach to the calculation of I using n-alkanes (C7-C30) as references, where the second dimension retention time (2tR) and the second dimension column temperature (2Te) formed the X-Y plane and the I was the Z-axis to form the I surface. Compared to the conventional approach for calculating I using isovolatility curves, the surface fitting approach eliminated the construction of isovolatility curves for the reference compounds and gives better reproducibility. The goodness of the proposed surface fitting achieved R2 = 0.9999 and RMSE = 6.1 retention index units (iu). Ten-fold cross validation demonstrated the surface fitting approach had a good predictability with average R2 = 0.9999 and RMSE = 6.6 iu. The developed method was also applied to calculate the second dimension retention indices of compound standards in two commercial mixtures MegaMix A and MegaMix B. The mean standard deviation of the calculated I was only 1.6 iu for compounds in MegaMix A and 3.4 iu for compounds in MegaMix B. Compared with the literature results, the small value of standard deviation in the calculated retention index using surface fitting method shows that the surface fitting method has less measurement variability than the conventional isovolatility curve approach.

Optimizing 2D gas chromatography mass spectrometry for robust tissue, serum and urine metabolite profiling

Two-dimensional gas chromatography mass spectrometry (GCxGC-MS) is utilized to an increasing extent in biomedical metabolomics. Here, we established and adapted metabolite extraction and derivatization protocols for cell/tissue biopsy, serum and urine samples according to their individual properties. GCxGC-MS analysis revealed detection of ~600 molecular features from which 165 were characterized representing different classes such as amino acids, fatty acids, lipids, carbohydrates, nucleotides and small polar components of glycolysis and the Krebs cycle using electron impact (EI) spectrum matching and validation using external standard compounds. Advantages of two-dimensional gas chromatography based resolution were demonstrated by optimizing gradient length and separation through modulation between the first and second column, leading to a marked increase in metabolite identification due to improved separation as exemplified for lactate versus pyruvate, talopyranose versus methyl palmitate and inosine versus docosahexaenoic acid. Our results demonstrate that GCxGC-MS represents a robust metabolomics platform for discovery and targeted studies that can be used with samples derived from the clinic.

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GCxGC-MS detected ~600 features;165 represented metabolites of different classes.

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Optimizing gradient length and separation through modulation improved metabolite ID.

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improved separation of lactate/pyruvate, talopyranose/palmitate and inosine/docosahexaenoate.