Improved conventional and microwave-assisted silylation protocols for simultaneous gas chromatographic determination of tocopherols and sterols: Method development and multi-response optimization

A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms

Ovarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for OC diagnosis. EVs, nanosized extracellular vesicles found in the blood, have been proposed as promising biomarkers for liquid biopsies. In this study we recruited 37 OC patients, 22 benign ovarian tumor (BE) patients, and 46 clinically healthy control patients (CON). Plasma EVs were purified from blood samples and sensitive thermal separation probe-based mass spectrometry analysis using a global untargeted metabolic profiling strategy was employed to characterize the metabolite fingerprints. Uniform manifold approximation and projection (UMAP) analysis demonstrated a distinct separation of EVs among the three groups. We screened for diagnostic biomarkers from plasma EV metabolites using seven machine learning algorithms, including artificial neural network (ANN), decision tree (DT), K nearest neighbor (KNN), logistics regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM). For the OC-CON comparison, the highest AUC values were found for RF (0.91), ANN (0.90) and NB (0.90), with the F1-scores of 0.88, 0.83, and 0.76 respectively. For the OC-BE comparison, SVM (0.94), RF (0.86), and KNN (0.86) gave the highest AUCs, with F1-scores of 0.80, 0.80, and 0.91 respectively. A total of 19 and 158 metabolic features exhibited significant differences (FC = 1.5, q < 0.01) in the OC vs BE and OC vs CON comparisons, respectively. Notably, the quantities of 9-octadecenamide and 1,4-methanobenzocyclodecene were significantly elevated, while maltol showed a significant reduction in the OC group compared to the BE group. When comparing the OC group to the CON group, the concentrations of 4-amino-furazan-3-carboxylic acid 2-hydroxy-4-methoxybenzaldehyde, N-phenylethyl, and 4-morpholineethanamine were significantly elevated, while the remaining metabolites, including hydrazine and pyridine sulfonamide, were reduced, in the OC group. The metabolites showing different abundancies are associated with cancer-related mutations, immune responses, and metabolic reprogramming. We demonstrate that the RF algorithm, combined with sensitive thermal separation probe-based mass spectrometry analysis of plasma EVs, can effectively identify OC patients with good accuracy. Thus, our study has shortlisted a set of potential biomarkers in plasma EVs, and the proposed approach could serve as a routine prescreening tool for ovarian cancer.

Novel sampling and gas-phase derivatization strategy: Proof-of-concept by profiling ionic polar metabolites using gas chromatography-mass spectrometry

Metabolomic research involves the comprehensive analysis of metabolites in biological samples and has many applications. Gas chromatography-mass spectrometry (GC-MS) is an established and widely used approach for metabolic profiling. However, sample preparation and metabolite derivatization are time-consuming, and derivatization options are limited. We propose gas-solid phase derivatization (GSPD) as a novel sampling and derivatization method that uses a silica monolith substrate and gaseous derivatization reagents for metabolomics using GC-MS. We developed a method to measure the organic acids and sugar phosphates responsible for glycolysis and the tricarboxylic acid (TCA) cycle. GSPD simplifies the sample preparation and can be applied to derivatization reactions that are difficult to perform in solution owing to solvent limitations. The developed method was applied to human plasma and tomato pulp and was shown to have a higher detection performance than the conventional method. This study provides a strategy to simplify sample preparation and expand derivatization options for GC-MS-based metabolomics.

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

Identification of urinary volatile organic compounds as a potential non-invasive biomarker for esophageal cancer

Early diagnosis of esophageal cancer (EC) is extremely challenging. The study presented herein aimed to assess whether urinary volatile organic compounds (VOCs) may be emerging diagnostic biomarkers for EC. Urine samples were collected from EC patients and healthy controls (HCs). Gas chromatography-ion mobility spectrometry (GC-IMS) was next utilised for volatile organic compound detection and predictive models were constructed using machine learning algorithms. ROC curve analysis indicated that an 8-VOCs based machine learning model could aid the diagnosis of EC, with the Random Forests having a maximum AUC of 0.874 and sensitivities and specificities of 84.2% and 90.6%, respectively. Urine VOC analysis aids in the diagnosis of EC.

Gas chromatography with mass spectrometry analysis of 4,4-dimethylsterols and 4-methylsterols in edible oils after their enrichment by means of solid phase extraction

4-Methylsterols (4-M-sterols) and 4,4-dimethylsterols (4,4-D-sterols) are a group of underexplored minor sterols that occur in almost all living organisms. Here, we developed a strategy for the determination of the biochemical precursors of the predominant 4-desmethylsterols in edible oils. Due to their low contribution to the sterol content in the samples, a solid phase extraction (SPE) method was developed for the enrichment of 4-M- and 4,4-D-sterols in the hexane extracts of saponified oils. In a two-fold SPE procedure, the bulk of 4,4-D-sterols was collected in one fraction. The residual sample was subjected to a second SPE step which targeted all 4-M-sterols and low shares of 4,4-D-sterols in one fraction and the predominant 4-desmethylsterols in another one. After silylation of the SPE fractions, gas chromatography with mass spectrometry (GC/MS) was used to analyze 4,4-D- and 4-M-sterols. The results were used to define eight subgroups whose characteristic structural features could be linked with the presence of specific m/z values. These m/z values were measured sensitively by GC/MS operated in selected ion monitoring (SIM) mode. Application of the GC/MS method to eighteen edible oils enabled the detection of 55 mostly very low abundant 4-M- and 4,4-D-sterols. Twenty-four of the 4-M- and 4,4-D-sterols could be assigned and the remaining 31 unknown sterols could be traced back to their basic structures.

Discovery of Potential Biomarkers for Postmenopausal Osteoporosis Based on Untargeted GC/LC-MS

PURPOSE

As an important public health problem, osteoporosis (OP) in China is also in an upward trend year by year. As a standard method for diagnosing OP, dual-energy X-ray absorptiometry (DXA) cannot analyze the pathological process but only see the results. It is difficult to evaluate the early diagnosis of OP. Our study was carried out through a serum metabolomic study of OP in Chinese postmenopausal women on untargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) to find possible diagnostic markers.

MATERIALS AND METHODS

50 Chinese postmenopausal women with osteoporosis and 50 age-matched women were selected as normal controls. We first used untargeted GC/LC-MS to analyze the serum of these participants and then combined it with a large number of multivariate statistical analyses to analyze the data. Finally, based on a multidimensional analysis of the metabolites, the most critical metabolites were considered to be biomarkers of OP in postmenopausal women. Further, biomarkers identified relevant metabolic pathways, followed by a map of metabolic pathways found in the database.

RESULTS

We found that there may be metabolic pathway disorders like glucose metabolism, lipid metabolism, and amino acid metabolism in postmenopausal women with OP. 18 differential metabolites are considered to be potential biomarkers of OP in postmenopausal women which are a major factor in metabolism and bone physiological function.

CONCLUSION

These findings can be applied to clinical work through further validation studies. It also shows that metabonomic analysis has great potential in the application of early diagnosis and recurrence monitoring in postmenopausal OP women.

Analytical Methods for the Determination of Neuroactive Steroids

Neuroactive steroids are a family of all steroid-based compounds, of both natural and synthetic origin, which can affect the nervous system functions. Their biosynthesis occurs directly in the nervous system (so-called neurosteroids) or in peripheral endocrine tissues (hormonal steroids). Steroid hormone levels may fluctuate due to physiological changes during life and various pathological conditions affecting individuals. A deeper understanding of neuroactive steroids' production, in addition to reliable monitoring of their levels in various biological matrices, may be useful in the prevention, diagnosis, monitoring, and treatment of some neurodegenerative and psychiatric diseases. The aim of this review is to highlight the most relevant methods currently available for analysis of neuroactive steroids, with an emphasis on immunoanalytical methods and gas, or liquid chromatography combined with mass spectrometry.

Combining metabolomics with bioanalysis methods to investigate the potential toxicity of dihexyl phthalate

Dihexyl phthalate (DHP) is one of the most commonly used phthalate esters in various plastic and consumer products. Human are inevitably exposed to DHPs. Although several animal and human experiments have revealed that DHP can cause multiple toxicities, few studies have previously assessed the effects of DHP exposure by liquid chromatography mass spectrometry (LC-MS) analysis combine with molecular biology methods on human cells. Therefore, the purpose of our study was to investigate the effect of DHP on human cell metabolism by systems biology methods. In this study, U₂ OS cancer cells were treated with 10 μM DHP for metabolomics analysis and apoptosis analysis at indicate time. Metabolomic study of the metabolic changes caused by DHP in U₂ OS cells was performed for the first time using integrative liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). To investigate the possible reason of fatty acids level altered by DHP, we measured some key fatty acid synthesis and oxidation-related enzyme expression levels by quantitative real-time PCR (Q-PCR). Apoptotic cells were analyzed by flow cytometry and apoptosis-related gene expressions were measured by Q-PCR. 2',7'-Dichlorofluorescein diacetate (DCFH-DA) staining was used to evaluate ROS content. Partial least squares-discriminate analysis (PLS-DA) clearly showed that significant differences in metabolic profiles were observed in U₂ OS cells exposed to DHP compared with controls. A total of 58 putative metabolites in electrospray ionization source (ESI) + mode and 32 putative metabolites in ESI-mode were detected, the majority of the differential metabolites being lipids and lipid-like molecules. Among them, the altered fatty acids level corresponded to expression levels of genes encoding enzymes related to fatty acids synthesis and oxidation. Moreover, DHP induced reactive oxygen species (ROS) accumulation, promoted cell apoptosis and inflammation, and resulted in a significant increase in apoptosis and inflammation-related gene expression levels compared with controls. In summary, our results suggested that metabolomics combined with molecular bioanalysis methods could be an efficient tool to assess toxic effects, which contribute to explore the possible cytotoxicity mechanisms of DHP, and provide a basis for further research.

Review of recent developments in GC-MS approaches to metabolomics-based research

BACKGROUND

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available.

AIM OF REVIEW

This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.