This thesis presents applications of high resolution secondary ion mass spectrometry (NanoSIMS) analysis for stable isotope imaging in biological samples. These projects were designed to explore the potential applications of NanoSIMS analysis, and to develop protocols and novel methodologies to visualize and quantify biological processes. Working with collaborators in the UK and USA, I have applied NanoSIMS analysis to study 3 research areas, including molecule interactions, single cell metabolisms and lipid imaging in tissues. Antimicrobial peptides (AMPs) play important role in the immune system, and understanding how AMPs interact with cell membranes can provide useful information to design new therapies to control infection. The pore structures and dynamics of the interaction of AMPs with membranes has been visualized for the first time and confirmed with combined AFM and NanoSIMS analysis. A correlative backscattered electron (BSE) imaging and NanoSIMS analysis methodology has been developed to study glutamine metabolism in single cancer cells. This method enables us to measure the chemical information in specific organelles in these cells and can be widely applied to study metabolisms and to trace the uptake of labelled molecules in biological matrices. Quantitative analysis on the effects of hypoxic conditions and the PYGL gene were studied. Applying correlative BSE and NanoSIMS analysis, I also studied lipid uptake mechanisms in various mouse tissues, including brown adipose tissue, heart, intestines, liver and skeletal muscle, mainly focused on a recently discovered protein, GPIHBP1, and its function in the lipid uptake process. TRL margination was proved to depend on the GPIBP1-LPL complex, and 3 stages of lipid transport from capillary lumen to lipid droplets was also visualized by combined BSE and NanoSIMS analysis.
Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:629537 |
Date | January 2014 |
Creators | Jiang, Haibo |
Contributors | Grovenor, Chris |
Publisher | University of Oxford |
Source Sets | Ethos UK |
Detected Language | English |
Type | Electronic Thesis or Dissertation |
Source | http://ora.ox.ac.uk/objects/uuid:15456362-6022-41e1-b78d-1127d6d172b0 |
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