CHEAR Plasma Reference Material Plasma GC-MS
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
TISSUE(S): Blood
PROVIDER: ST000532 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
Dataset's files
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| mwtab | Other |
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Project description: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.
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Project description:BackgroundMalignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma.MethodsBy using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers.ResultsUsing samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid.ConclusionsTo the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings.
| S-EPMC10200095 | biostudies-literature
Project description: Not available
| ST000545 | MetabolomicsWorkbench
Project description:Food contact materials (FCM) made of plastic materials contain various additives, e.g. plasticisers, UV-stabilisers, preservatives, antioxidants, etc. These compounds can migrate from the material to the food and display adverse health effects in consumers. Inertness of FCM is established by migration testing with appropriate food simulants [1]. A GC-MS/MS method for the simultaneous determination of several different groups of additives to plastics has been developed to perform a migration testing and to determine these compounds in real samples, as described in the research publication "Development of a SPME-GC-MS/MS method for the determination of some contaminants from food contact material in beverages" [2]. Here, we present the data on the optimisation of GC-MS/MS parameters: GC column and temperature programme choice, MS/MS parameters optimisation, and choice of internal standard. Subsequently, SPME parameters were also optimised as described in [2].
| S-EPMC6950780 | biostudies-literature
Project description: Not available
| ST000531 | MetabolomicsWorkbench
Project description:Polyunsaturated fatty acids (PUFAs), including essential omega-3 and omega-6 fatty acids, play important roles in diverse physiological and pathological processes. Diligent monitoring of PUFAs is recommended for individuals with increased risk of developing essential fatty acid deficiency (EFAD), including premature and very low birth weight infants, patients on prolonged parenteral nutrition, and those with dietary restrictions, for example due to inborn errors of metabolism. Here, we present a gas chromatography-negative chemical ionization-mass spectrometry (GC-NCI-MS) method for the quantitation of total levels of twenty-two fatty acids (C12-C22) in serum/plasma, including omega-3 and omage-6 PUFAs. Hydrolysis was used to release esterified fatty acids, which were analyzed by GC-NCI-MS as pentafluorobenzyl esters in selected-ion monitoring (SIM) mode. The calibration curves for all analytes had consistent slopes with R2 of ⩾0.990. Intra- and inter-assay precision CVs were ⩽9.0% and ⩽13.2%, respectively. Samples were found to be stable for 24 h at room temperature, at least 7 days at 4 °C, at least 75 days at -20 °C, and for three freeze/thaw cycles. No matrix effects or interferences were observed. This method offers improvements over published studies including smaller sample volume, inclusion of additional internal standards, analysis in a single injection, and use of methane reagent gas. This method could be used in a clinical laboratory setting for the diagnosis of EFAD, evaluation of nutritional status, and diet monitoring.
| S-EPMC11322783 | biostudies-literature
Project description:BackgroundEarly diagnosis of hypoxic-ischaemic encephalopathy (HIE) is crucial in preventing neurodevelopmental disabilities and reducing morbidity and mortality. The study was to investigate the plasma metabolic signatures in the peripheral blood of HIE newborns and explore the potential diagnostic biomarkers.MethodIn the present study, 24 newborns with HIE and 24 healthy controls were recruited. The plasma metabolites were measured by gas chromatography-mass spectrometry (GC-MS), and the raw data was standardized by the EigenMS method. Significantly differential metabolites were identified by multivariate statistics. Pathway enrichment was performed by bioinformatics analysis. Meanwhile, the diagnostic value of candidate biomarkers was evaluated.ResultThe multivariate statistical models showed a robust capacity to distinguish the HIE cases from the controls. 52 metabolites were completely annotated. 331 significantly changed pathways were enriched based on seven databases, including 33 overlapped pathways. Most of them were related to amino acid metabolism, energy metabolism, neurotransmitter biosynthesis, pyrimidine metabolism, the regulation of HIF by oxygen, and GPCR downstream signaling. 14 candidate metabolites showed great diagnostic potential on HIE. Among them, alpha-ketoglutaric acid has the potential to assess the severity of HIE in particular.ConclusionThe blood plasma metabolic profile could comprehensively reflect the metabolic disorders of the whole body under hypoxia-ischaemic injury. Several candidate metabolites may serve as promising biomarkers for the early diagnosis of HIE. Further validation based on large clinical samples and the establishment of guidelines for the clinical application of mass spectrometry data standardization methods are imperative in the future.
| S-EPMC8249136 | biostudies-literature
Project description:The LH surge induces panoply of events that are essential for ovulation and corpus luteum formation. The transcriptional responses to the LH surge of pre-ovulatory granulosa cells are complex and still poorly understood. In the present study, a genome wide bovine oligo array was used to determine how the gene expression profiles of granulosa cells are modulated by the LH surge. Granulosa cells from three different statuses were used (1) 2 h before the induction of the LH surge, (2) 6 h and (3) 22 h after the LH surge to assess the short and long term effects of this hormone on follicle differentiation. The results obtained were a list of differentially expressed transcripts for each granulosa cell group. To provide a comprehensive understanding of the processes at play, biological annotations were used to reveal the different functions of transcripts, confirming that the LH surge acts in a temporal manner. The pre-LH group is involved in typical tasks such as cell division, development and proliferation, while the short response of the LH surge included features such as response to stimulus, vascularisation and lipid synthesis, which are indicative of cells preparing for ovulation. The late response of granulosa cells revealed terms associated with protein localization and intra-cellular transport corresponding to the future secretion task that will be required for the transformation of granulosa cells into corpus luteum. Overall, results described in this study provide new insights into the different transcriptional steps that granulosa cells go through during ovulation and before luteinization. Three biological granulosa cells samples: 2 h pre-LH vs. 6 h post-LH vs. 22 h post-LH. Biological replicates: 3 with a technical dye-swap replicates (Dy 547 and Dy 647) for each biological replicate. Hybridizations were performed in a loop design for a total a 9 hybridizations.
2010-10-15 | E-GEOD-23900 | biostudies-arrayexpress
Project description:We compared the performance of gas chromatography time-of-flight mass spectrometry (GC-MS) and comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) for metabolite biomarker discovery. Metabolite extracts from 109 human serum samples were analyzed on both platforms with a pooled serum sample analyzed after every 9 biological samples for the purpose of quality control (QC). The experimental data derived from the pooled QC samples showed that the GC×GC-MS platform detected about three times as many peaks as the GC-MS platform at a signal-to-noise ratio SNR ≥ 50, and three times the number of metabolites were identified by mass spectrum matching with a spectral similarity score Rsim ≥ 600. Twenty-three metabolites had statistically significant abundance changes between the patient samples and the control samples in the GC-MS data set while 34 metabolites in the GC×GC-MS data set showed statistically significant differences. Among these two groups of metabolite biomarkers, nine metabolites were detected in both the GC-MS and GC×GC-MS data sets with the same direction and similar magnitude of abundance changes between the control and patient sample groups. Manual verification indicated that the difference in the number of the biomarkers discovered using these two platforms was mainly due to the limited resolution of chromatographic peaks by the GC-MS platform, which can result in severe peak overlap making subsequent spectrum deconvolution for metabolite identification and quantification difficult.
| S-EPMC4849136 | biostudies-literature