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

ABSTRACT: 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.

SUBMITTER: Yu Z

PROVIDER: S-EPMC5294743 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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

Yu Zhanru Z Huang Honglei H Reim Alexander A Charles Philip D PD Northage Alan A Jackson Dianne D Parry Ian I Kessler Benedikt M BM

Talanta 20170107

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 ...[more]

PMID: 28153317

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Project description:BackgroundMass spectrometry (MS) based metabolite profiling has been increasingly popular for scientific and biomedical studies, primarily due to recent technological development such as comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS). Nevertheless, the identifications of metabolites from complex samples are subject to errors. Statistical/computational approaches to improve the accuracy of the identifications and false positive estimate are in great need. We propose an empirical Bayes model which accounts for a competing score in addition to the similarity score to tackle this problem. The competition score characterizes the propensity of a candidate metabolite of being matched to some spectrum based on the metabolite's similarity score with other spectra in the library searched against. The competition score allows the model to properly assess the evidence on the presence/absence status of a metabolite based on whether or not the metabolite is matched to some sample spectrum.ResultsWith a mixture of metabolite standards, we demonstrated that our method has better identification accuracy than other four existing methods. Moreover, our method has reliable false discovery rate estimate. We also applied our method to the data collected from the plasma of a rat and identified some metabolites from the plasma under the control of false discovery rate.ConclusionsWe developed an empirical Bayes model for metabolite identification and validated the method through a mixture of metabolite standards and rat plasma. The results show that our hierarchical model improves identification accuracy as compared with methods that do not structurally model the involved variables. The improvement in identification accuracy is likely to facilitate downstream analysis such as peak alignment and biomarker identification. Raw data and result matrices can be found at http://www.biostat.iupui.edu/~ChangyuShen/index.htm.Trial registration2123938128573429.

Project description:Liver fibrosis is caused by liver injury induced by a number of chronic liver diseases, including schistosome infection, hepatitis infection, metabolic disease, alcoholism and cholestasis. The tissue damage occurring after injury or inflammation of the liver is a reversible lesion; however, liver fibrosis has become a worldwide problem and poses a threat to human health. The development of an effective drug for the prevention and treatment of liver fibrosis is ongoing and uses information from different occurrences of liver fibrosis. In the present study, carbon tetrachloride (CCl4)-induced metabonomic changes in serum and urine at 12 weeks were analyzed using gas chromatography-mass spectrometry (GC/MS) to investigate potential biomarkers. Liver fibrosis was induced in rats by subcutaneous injections of CCl4 twice a week for 12 consecutive weeks. Histopathological changes were used to assess the successful production of a CCl4-induced liver fibrosis model. Serum and urine samples from the two groups were collected at 12 weeks. The metabolic profile changes were analyzed by GC/MS alongside principal component analysis and orthogonal projections to latent structures. Metabolic profile studies indicated that the clustering of the two groups could be separated and seven metabolites in serum and five metabolites in urine were identified. In serum, the metabolites identified included isoleucine, L-malic acid, α-copper, carnitine, hippuric acid, glutaric acid and glucose. In urine 2-hydroxy butyric acid, isoleucine, N-acetyl-β-alanine, cytidine and corticoid were identified. The present study demonstrated that the pathogenesis of liver fibrosis may be associated with the dysfunction of a number of metabolic pathways, including glucose, amino acid, P450, fatty acid, nucleic acid, water-electrolyte and glutathione biosynthesis. Assessing potential biomarkers may therefore provide novel targets and theories for the innovation of novel drugs to prevent and cure liver fibrosis.

Dataset Information

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

Publications

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

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