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Title: Synaptome mapping of the postsynaptic density 95 protein in the human brain
Author: Curran, Olimpia Elwira
ISNI:       0000 0004 7429 4961
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2018
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The past three decades of synaptic research have provided new insights into synapse biology. While synapses are still considered the fundamental connectors between the nerve cells in the central nervous system, they are no longer seen as simple neuron-to-neuron contacts. In fact, the estimated 100 trillion of human synapses are extremely complex, diverse and capable of performing sophisticated computational operations giving rise to advanced repertoires of cognitive and organic behaviours. These intricate synaptic properties mean that existing methodologies for quantifying and characterising synapses are inadequate. Yet, understanding of synapse biology is crucial to deciphering human pathology as disruptions in synapse numbers, architecture and function have already been linked to many human brain disorders. The purpose of this PhD was to evaluate a novel, high-throughput synaptic protein quantification method at a single synapse resolution in human post-mortem brain tissue. The method has already been successfully tested in our laboratory in genetically engineered mice, whereby synapses have been systematically quantified across a large number of areas to generate the first molecular maps of synapses, the synaptome maps. In this project, methods have been developed to label human brain tissue with postsynaptic density protein 95 (PSD-95), the most common postsynaptic protein. We describe the use of PSD-95 combined with confocal microscopy and computational image analysis to quantify synaptic puncta immunofluorescence (IF) parameters in the human brain. In the first part of this study, the new method was used to quantify PSD-95 IF across selected 20 human brain regions to generate first PSD-95 human synaptome map. In the second part, PSD-95 IF was systematically assessed across 16 hippocampal subregions. Finally, we confirmed that our novel synaptic quantification method was sensitive to hippocampal synaptic losses in patients with Alzheimer's Disease (AD). Such a high degree of systematic synapse quantification has not previously been reported in human brain tissue. Our method is a promising approach for synaptic protein quantification in tissue with several potential applications in diagnosis and development of therapeutics for neurological and psychiatric disorders.
Supervisor: Grant, Seth ; Smith, Colin Sponsor: Medical Research Council (MRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: synapses ; synaptome ; synaptome mapping ; automated computational methods