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Abstract

This thesis delves into metabolomics, the study of small molecules—known as metabolites—present within a biological system. These molecules span both polar classes, such as amino acids, sugars, and nucleotides, and non-polar classes, such as fatty acids and other lipid species, which are often collectively referred to as lipids. The study of this latter group is often treated as a distinct domain called lipidomics, which focuses specifically on the comprehensive analysis of lipids. Accordingly, the term “metabolomics” may be used either as a broad umbrella that includes both polar and non-polar molecules, or a narrower term that refers to the study of polar molecules only. This work adopts the latter definition and focuses on integrating metabolomics with lipidomics to enable a more comprehensive investigation of the biological systems of interest. The first project focuses specifically on lipidomics, examining the lipid composition of extracellular vesicles secreted by wild-type and drug-resistant Leishmania parasites. By annotating and comparing lipid species present in these vesicles, this study provides detailed insight into lipid remodeling associated with drug resistance. Extracellular vesicles play a critical role in parasite–host interactions, and alterations in their lipid composition may influence membrane properties, signaling processes, and resistance mechanisms. This lipid-centric approach enables high-resolution chemical profiling of resistance-associated changes and contributes to the identification of lipid signatures that may serve as biomarkers or therapeutic targets in leishmaniasis. The second project expands beyond a single molecular class and addresses a key methodological challenge relevant to both metabolomics and lipidomics: the comparison of single-phase and dual-phase extraction strategies. While dual-phase extraction enables the simultaneous recovery of polar metabolites and lipids from a single sample, it may result in reduced analyte concentrations compared to single-phase methods. This study systematically investigates three mechanistically distinct factors hypothesized to drive these differences—pipetting effect, partitioning effect, and matrix effect. By isolating and evaluating each factor, this work provides a framework for objectively assessing extraction performance and informs methodological decision-making in integrative small-molecule analyses. Overall, this thesis advances the field by combining a lipidomics-focused biological application with a broader methodological evaluation relevant to integrated metabolomics–lipidomics workflows.