Uncovering the Connectome

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Over the past two decades, there has been an explosion in the number of tools and technology available to neuroscientists. With the advent on array tomography (AT) in the last decade, our ability to study synapses and their proteometric composition in the mammalian cortex has skyrocketed. However, unlike electron microscopy (EM) data which is the gold standard for synapse detection, AT data presents a variety of challenges in visualizing and characterizing synapses. There are many sources of noise, no singular definition of a synapse, and no standardized approach for data processing. In this work, our goal is to study synapse anatomy by combining array tomography with novel image processing methods. First, we started by creating a probabilistic synapse detector, which detects synapses based on their proteometric subtype with no training data. Then, we created a tool to characterize the efficacy of antibodies for array tomography applications. We end by expanding the probabilistic synapse detection method for tripartite synapses and explore the differences in synapses between wild-type and FMR1 knockout mice. This analysis lead to the discovery of several new effects of the FMR1 gene on astrocytic synapse density including the observation that there is a significant decrease in the density of excitatory glutamatergic synapses and their association with astrocytes while the changes in inhibitory GABAergic synapses are less pronounced. Our results suggest that that in Fragile X Syndrome astrocytes may mediate at least some of the pathological effects on glutamatergic synapses, while GABAergic synapses are likely influenced by a different mechanism.





Simhal, Anish Kumar (2019). Uncovering the Connectome. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/18796.


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