Plasma fingerprinting with GC-MS in acute coronary syndrome.
ABSTRACT New biomarkers of cardiovascular disease are needed to augment the information obtained from traditional indicators and to illuminate disease mechanisms. One of the approaches used in metabolomics/metabonomics for that purpose is metabolic fingerprinting aiming to profile large numbers of chemically diverse metabolites in an essentially nonselective way. In this study, gas chromatography-mass spectrometry was employed to evaluate the major metabolic changes in low molecular weight plasma metabolites of patients with acute coronary syndrome (n = 9) and with stable atherosclerosis (n = 10) vs healthy subjects without significant differences in age and sex (n = 10). Reproducible differences between cases and controls were obtained with pattern recognition techniques, and metabolites accounting for higher weight in the classification have been identified through their mass spectra. On this basis, it seems inherently plausible that even a simple metabolite profile might be able to offer improved clinical diagnosis and prognosis, but in addition, specific markers are being identified.
SourceAvailable from: Fernando De la Cuesta[Show abstract] [Hide abstract]
ABSTRACT: How to design a state-of-the art proteomic/metabolomic analysis.•Latest advances in atherothrombosis through proteomics/metabolomics.•Potential of systems biology for integrating omics projects.•Biomarker and therapeutical target implementation in the clinic.10/2014; DOI:10.1016/j.trprot.2014.10.002
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ABSTRACT: Based on theoretically calculated comprehensive lipid libraries, in lipidomics as many as 1000 multiple reaction monitoring (MRM) transitions can be monitored for each single run. On the other hand, lipid analysis from each MRM chromatogram requires tremendous manual efforts to identify and quantify lipid species. Isotopic peaks differing by up to a few atomic masses further complicate analysis. To accelerate the identification and quantification process we developed novel software, MRM-DIFF, for the differential analysis of large-scale MRM assays. It supports a correlation optimized warping (COW) algorithm to align MRM chromatograms and utilizes quality control (QC) sample datasets to automatically adjust the alignment parameters. Moreover, user-defined reference libraries that include the molecular formula, retention time, and MRM transition can be used to identify target lipids and to correct peak abundances by considering isotopic peaks. Here, we demonstrate the software pipeline and introduce key points for MRM-based lipidomics research to reduce the mis-identification and overestimation of lipid profiles. The MRM-DIFF program, example data set and the tutorials are downloadable at the "Standalone software" section of the PRIMe (Platform for RIKEN Metabolomics, http://prime.psc.riken.jp/) database website.Frontiers in Genetics 01/2014; 5:471. DOI:10.3389/fgene.2014.00471
Chapter: Chapter 24 – Metabonomics[Show abstract] [Hide abstract]
ABSTRACT: In this chapter, an overview of gas chromatography mass spectrometry (GC-MS)-based metabonomics is presented with special emphasis on the GC-MS conditions, data acquisition, data preprocessing, data pretreatment, chemometric data analysis, model validation, biomarker identification, and pathway mapping. Moreover, details of GC-MS-based tissue and urine metabonomics and their applications in biomedical research, such as in cancer biology, toxicology, nutritional studies, diabetes, cardiovascular diseases, neurodegenerative diseases, and metabolic syndromes, are discussed. Lastly, an idea of the recent advances in GC-MS technologies such as solid-phase microextraction (SPME) and gas chromatography tandem mass spectrometry (GC-MS/MS) and their impact on metabonomic applications is also presented.Gas Chromatography, 06/2012: chapter Gas Chromatography: pages 545-562; Elsevier.