As an emerging field, lipidomics still encounters methodological challenges. Indeed, as it is an –omics field, analysis of data is time and labour intensive, if the necessary and appropriate bioinformatics tools are not existent. Here I present two programs I developed to address the challenges of peak identification and peak annotation and filtering for a targeted lipidomics approach, which is not supported by available software. The first program, Lipid Identification Tool (LIT), consists of a stand-alone offline search engine, which provides a robust in silico database generated by linear equations based on lipid structures, containing information lacking on presently available online databases. The second program, Lipid Identification for Targeted Lipidomics (LITL), allows the annotation of HPLC-ESI-MS/MS MRM acquired spectral data in text-based format, comparing them to a library of identities, via retention time and mass-over-charge of detected ions, and to easily export the results to a statistical analysis software. // La lipidomique est un jeune domaine encore proie à des défis quant à sa méthodologie. En effet, l’analyse de données en lipidomique demeurera longue et ardue, tant que des outils bioinformatiques appropriés ne sont pas créés. Je décris ici deux de mes programmes développés pour remédier aux défis d’identification de pics et d’annotations et de sélection de pics provenant d’une approche lipidomique dénommée ciblée, données présentement incompatibles avec d’autres programmes. Lipid Identification Tool (LIT) est un moteur de recherche hors-ligne possédant une robuste base de données in silico générée mathématiquement, et contenant des informations sur des espèces lipidiques non décrites dans les bases de données disponibles. Lipid Identification for Targeted Lipidomics (LITL) permet l’annotation de données provenant de méthodes HPLC-ESI-MS/MS MRM, en comparant leur temps de rétention ainsi que leur rapport masse-sur-charge à une bibliothèque d’identités lipidiques, et permet de les exporter facilement vers un logiciel d’analyse statistique.

Advances in high performance liquid chromatography-electrospray ionization-mass spectrometry made in proteomics and now applied to the emerging field of lipidomics has enabled the identification of lipid composition at the molecular level. These improvements have given fresh impetus to lipid research. Modulating lipid compositions has been suggested to represent a novel therapeutic target for intervention in Alzheimer’s disease. A better understanding of how metabolic alterations in the lipid landscape alter Alzheimer’s disease prognosis is required to realize this promise. To achieve this goal, further methodological improvement in lipidomic data acquisition and analysis are required as are comprehensive comparative analyses of lipid metabolism at the systems level in clinical samples and mouse models of human neurodegenerative disease. In this thesis, I present two new lipidomic bioinformatic tools Retention Time Standardization and Registration (RTStaR) and Visualization and Phospholipid Identification (VaLID) designed to facilitate analysis of high performance liquid chromatography-electrospray ionization-mass spectrometry lipidomic data. Using these tools and methodologies, I then comparatively profiled the glycerophosphocholine lipidome in the plasma of young adults, cognitively normal elderly with vascular impairment, mild cognitive impairment and late-onset Alzheimer’s disease patients and the entorhinal-hippocampal circuit of late-onset Alzheimer’s disease patients, TgCRND8 human amyloid beta precursor protein transgenic mice (Alzheimer’s disease mouse model), and across the lifespan of NonTg female littermates. Systems-level analyses identified aberrant glycerophosphocholine metabolic pathways systemically perturbed by age, disease, and amyloid beta biogenesis resulting in the regionally-specific accumulation of critical platelet-activating factor and, to a lesser extent, the lysoglycerophosphocholine, metabolites in brain that could be, in part, predicted by changes in plasma. Finally, using proteomic approaches I identified additional changes in lipid metabolic pathways associated with phenoconversion in the TgCRND8 mouse model of Alzheimer’s disease.