Image analysis and computational modelling of Activity-Dependent Bulk Endocytosis in mammalian central nervous system neurons
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Date
30/11/2017Author
Stewart, Donal Patrick
Metadata
Abstract
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.