PhD Thesis Defence by Miriam Carolina Pérez Cova
The PhD student Miriam Carolina Pérez Cova from the Chemometrics research group, will defend her thesis on 5 September at 11:30h in the Aula Magna Enric Cassasas of the Faculty of Chemistry at the Universitat de Barcelona.
Title: Development and application of analytical and chemometric methodology for environmental metabolomic studies based on one-and two-dimensional liquid chromatography coupled to mass spectrometry
Directors: Joaquim Jaumot and Romà Tauler
Thesis Committee: Sílvia Lacorte, Yvan Vander Heyden, Miguel Herrero
Chemical exposure to emerging contaminants (ECs) is a major concern nowadays. These ECs have recently become a global environmental threat, and an in-depth characterization of their occurrence and toxic impact is needed. In this context, omic sciences have arisen as powerful tools to shed some light on the biological mechanisms affected by exposure to these chemicals. Particularly, metabolomics and lipidomics can provide a snapshot of what is actually happening at the molecular level, pointing to metabolic pathways affected by the contaminants. New analytical methodologies are required to extract the sought information in more complex biological matrices (from single cells to whole organisms). Hence, a major emphasis has been put on developing multidimensional separations and multiplatform approaches to increase the metabolome coverage. However, these novel approaches bring about massive datasets, and the complexity of the data analysis augments considerably. Therefore, chemometric strategies are a perfect match to get through this bottleneck and provide useful tools to obtain the most from the data collected.
In this PhD Thesis, the focus was set on developing analytical protocols, especially using two-dimensional liquid chromatography coupled to mass spectrometry (LC×LC-MS), as well as chemometric data analysis strategies applicable to environmental metabolomic studies. On the one hand, LC×LC-MS methods have been developed for both untargeted and targeted analyses. Active modulation strategies have been also successfully implemented in the multidimensional chromatographic separation of lipids. On the other hand, the Regions Of Interest (ROI) approach for compression and filtering has been validated for LC×LC-MS analyses. Regarding chemometric resolution methods (i.e., which allow obtaining quantitative and qualitative information from the sample constituents), and due to deviations from an ideal trilinear behavior presented by LC×LC datasets, the use of the Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) method has been preferred. Different quantification strategies have been tested based on the Regions Of Interest Multivariate Curve Resolution (ROIMCR) approach. In addition, several multivariate statistical methods based on the analysis of variance (ANOVA) have been compared for metabolomic studies. As a result, a combination of ANOVA-simultaneous component analysis (ASCA) and partial least squares discriminant analysis (PLS-DA) has been selected for statistical analysis and variable (metabolite) selection, respectively. All in all, different metabolomic workflows have been validated for the assessment of emerging contaminants in model biosystems.