1. Investigación

GC-MS for untargeted metabolomics is a well-established technique. Small molecules and molecules made volatile by derivatization can be measured and those compounds are key players in main biological pathways. This tutorial provides ready-to-use protocols for GC-MS-based metabolomics, using either the well-known low-resolution approach (GC-Q-MS) with nominal mass or the more recent high-resolution approach (GC-QTOF-MS) with accurate mass, discussing their corresponding strengths and limitations. Analytical procedures are covered for different types of biofluids (plasma/serum, bronchoalveolar lavage, urine, amniotic fluid) tissue samples (brain/hippocampus, optic nerve, lung, kidney, liver, pancreas) and samples obtained from cell cultures (adipocytes, macrophages, Leishmania promastigotes, mitochondria, culture media). Together with the sample preparation and data acquisition, data processing strategies are described specially focused on Agilent equipments, including deconvolution software and database annotation using spectral libraries. Manual curation strategies and quality control are also deemed. Finally, considerations to obtain a semiquantitative value for the metabolites are also described. As a case study, an illustrative example from one of our experiments at CEMBIO Research Centre, is described and findings discussed.

Pulmonary embolism (PE) is a common cardiovascular emergency which can lead to pulmonary hypertension (PH) and right ventricular failure as a consequence of pulmonary arterial bed occlusion. The diagnosis of PE is challenging due to non-specific clinical presentation what results in relatively high mortality. Moreover, the pathological factors associated with PE are poorly understood. Metabolomics can provide with new highlights which can help in the understanding of the processes and even propose biomarkers for its diagnosis. In order to obtain more information about PE and PH, acute PE was induced in large white pigs and plasma was obtained before and after induction of PE. Metabolic fingerprints from plasma were obtained with LC-QTOF-MS (positive and negative ionization) and GC-Q-MS. Data pretreatment and statistical analysis (uni- and multivariate) was performed in order to compare metabolic fingerprints and to select the metabolites that showed higher loading for the classification (28 from LC and 19 from GC). The metabolites found differentially distributed among groups are mainly related to energy imbalance in hypoxic conditions, such as glycolysis-derived metabolites, ketone bodies, and TCA cycle intermediates, as well as a group of lipidic mediators that could be involved in the transduction of the signals to the cells such as sphingolipids and lysophospholipids, among others. Results presented in this report reveal that combination of LC−MS- and GC−MS-based metabolomics could be a powerful tool for diagnosis and understanding pathophysiological processes due to acute PE.