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Title: Image analysis and computational modelling of Activity-Dependent Bulk Endocytosis in mammalian central nervous system neurons
Author: Stewart, Donal Patrick
ISNI:       0000 0004 7429 8671
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2017
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Synaptic vesicle recycling is the reuse of synaptic membrane material and proteins after vesicles have been exocytosed at the pre-synaptic terminal of a neuronal synapse. The discovery of the mechanisms by which recycling operates is a subject of active research. Within small mammalian central nervous system nerve terminals, two studied mechanisms of recovery are clathrin-mediated endocytosis and activity-dependent bulk endocytosis. Research into the comparative kinetics and mechanisms underlying these endocytosis mechanisms commonly involves time-series fluorescence microscopy of in vitro cultures. Synaptic proteins are tagged with fluorescent markers, or the synaptic vesicles are labelled with fluorescent dye. The change in fluorescence levels of individual synapses over time in response to stimuli is used to understand synaptic activity. The image analysis of these time-series images frequently requires substantial manual effort to extract the changing synaptic fluorescence intensity levels over time. This work focusses on two closely interlinked areas, the development of improved automated image analysis tools to facilitate the analysis of microscopy image data, and computational simulations to leverage the data obtained from these experiments to gain mechanistic insight into the underlying processes involved in synaptic vesicle recycling. The imaged properties of synapses within the time-series images are characterised, in terms of synapse movement during the course of an experiment. This characterisation highlights the properties which risk adding error to the extracted fluorescence intensity data, as analysis generally requires segmentation of regions of interest with fixed size and location. Where possible, protocols to optimise the manual selection of synapses in the image are suggested. The manual selection of synapses within time-series images is a common but time consuming and difficult task. It requires considerable skill on the part of the researcher to select synapses from noisy images without introducing error or bias. Automated tools for either general image segmentation or for segmentation of synapse-like puncta do exist, but have mixed results when applied to time-series experiments. This work introduces the use of knowledge of the experiment protocol into the segmentation process. The selection of synapses as they respond to known stimuli is compared against other current segmentation methods, and tools to perform this segmentation are provided. This use of synapse activity improves the quality of the segmented set of synapses over existing segmentation tools. Finally, this work builds a number of computational models, to allow published individual data points to be aggregated into a coherent view of overall synaptic vesicle recycling. The first is FM-Sim, a stochastic hybrid model of overall synapse recycling as is expected to occur during the course of an experiment. This closed system model handles the processes of exocytosis and endocytosis. It uses Bayesian inference to fit model parameters to experimental data. In particular, it uses the experimental protocol to separate the mechanisms and rates that may contribute to the observed experimental data. The second is a mathematical model of one aspect of synaptic vesicle recycling of particular interest - homoeostasis of plasma membrane integrity on the presynaptic terminal. This model provides bounds on efficiency of the studied endocytosis mechanisms at recovery of plasma membrane area during and after neuronal stimulus. Both the image analysis and the computational simulations demonstrated in this work provide useful tools and insights into current research of synaptic vesicle recycling and the role of activity-dependent bulk endocytosis. In particular, the utility of adding time-dependent experimental protocol knowledge to both the image analysis tools and the computational simulations is shown.
Supervisor: Gilmore, Stephen ; Cousin, Mike Sponsor: Biotechnology and Biological Sciences Research Council (BBSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: synaptic vesicle recycling ; synaptic fluorescence intensity levels ; automated image analysis ; stochastic hybrid model ; FM-Sim ; computational simulations