Diagnosing the Masses

UoM administered thesis: Phd

  • Authors:
  • Danielle Mcdougall

Abstract

Lipidomics is a growing field for mass spectrometry imaging (MSI) and ambient mass spectrometry analysis. Lipidomics is an important area of research due to the ability to gain information from downstream genomic changes. This research has demonstrated the overall strengths and limitations of a variety of mass spectrometry methods on a range of sample types, focusing mainly on the changing lipid profile. This includes desorption ionisation electrospray mass spectrometry (DESI-MS), rapid evaporative ionisation mass spectrometry (REIMS), and time of flight secondary ion mass spectrometry (ToF-SIMS). Prostate cancer (PCa) is the primary cause of cancer related death in Western males. It is not possible to distinguish indolent and aggressive disease, therefore the patient is treated for the more severe form, leading to overtreatment and associated complications. Literature has shown that non-steroidal anti-inflammatory drugs (NSAIDs) can potentially prevent growth of PCa, offering an alternative treatment approach. This study demonstrated that mass spectrometry imaging MSI methods such as DESI and SIMS are able to distinguish between PCa cell lines in combined pellets by observing the differences in the lipid profile. The response of the NSAID ibuprofen on PCa cell lines varying in clinical aggression was examined using MSI. As a result, it was established that ibuprofen was able to diminish the metabolic activity of the cancerous cell lines, LNCaP, LNCaP-C42 and LNCaP-C42B, sparing the normal epithelial line, PNT2-C2. This drug interaction caused significant changes to the intensities of several lipid classes including phosphatidic acid (PA) and phosphatidylinositol (PI) species. This research validated the combined strengths of these MSI techniques when used together. The potential of ambient methods such as REIMS was examined, focusing on optimising the setup for lipid profiling. Overall this thesis has outlined the benefits of MSI methods both on their individual merits and when used together for lipidomic analysis.

Details

Original languageEnglish
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Supervisors/Advisors
Award date1 Aug 2021