Advancing Lipidomics through Method Development, Data Processing, and its Applications in Cancer Research
Creators: Sousa Monteiro, Fernanda
Publisher: name: University of Alberta Library
Year: 2024

<p>Lipidomics, a subfield of metabolomics, offers comprehensive insights into the lipid composition of biological systems. Lipids, comprising a broad class of biological molecules, are essential to several biological functions. They play critical roles in cell membrane formation, serving as signaling molecules and storing energy. Studying the lipidome can give insights into cellular processes and metabolic pathways. Several diseases affect lipid synthesis and metabolism, making lipidomics a valuable tool in health research. Understanding lipid changes caused by diseases can aid in biomarker research for early detection and disease progression. However, despite its potential, lipidomics requires more reliable and sensitive methods to enhance the profiling of biological samples.<br>
Biological samples are susceptible to changes in their composition due to a broad range of factors, impacting lipid composition and adding uncertainty to quantitative analyses. To ensure a robust LC-MS workflow, it is crucial to maintain uniform sample concentrations, guaranteeing consistent instrument response. However, a standard method for lipid quantification has not been established in lipidomics. Often, metabolomics or proteomics techniques are used instead. Given the diverse characteristics of lipid samples, a specific method for lipid normalization is essential. The first part of this work focuses on developing a reliable Sulfo-Phospho-Vanillin (SPV) assay for normalizing samples in LC-MS lipidomics. This colorimetric method, optimized for better limits of detection and quantification, ensures accurate lipid measurements by reducing variability. Furthermore, this study also evaluates lipid profiles in human saliva, investigating the effects of different normalization methods on lipid annotations. <br>
The second part of this work focused on developing a two-dimensional liquid chromatography-mass spectrometry (2DLC-MS) method. By optimizing the HILIC method and its integration with RPLC-MS, a robust 2DLC-MS framework was developed to overcome common analytical challenges in lipidomics, such as ion suppression and co-elution. This method showed improvements in peak resolution and reduction of ion suppression through a fractionation approach. Comparative analyses using different data processing software revealed varying levels of annotation accuracy, highlighting the importance of software selection in lipidomics studies. Moreover, Chapter V describes the development and optimization of a 1.0 mm inner diameter LC-MS method using Orbitrap for lipidomic profiling. The optimized method was validated using biological samples, demonstrating improved lipid coverage and peak resolution. Lastly, Chapter VI applies the methods developed in Chapters IV and V to investigate how cisplatin and rapamycin, two cancer drugs, alter lipid profiles in non-small cell lung cancer (NSCLC) cells. This chapter focuses on understanding the metabolic disruptions caused by these chemotherapeutic agents, providing a comprehensive lipidomic profile of A549 cells under different treatment conditions. The results reveal significant lipid composition and metabolism changes, highlighting the distinct and combined effects of cisplatin and rapamycin.<br>
This work shows that more sensitive methods can improve lipid profiling and offer a comprehensive understanding of metabolic changes caused by diseases and treatments. The results described herein show the potential of lipidomics for biomarker discovery.</p>