NCS-FO: Collaborative Research: Computational Analysis of Synaptic Nanodomains

点击翻译按钮获取中文摘要

Successful learning and long-term memory retention are central to a successful society, starting with early education in schools and extending throughout life. For over 100 years research has shown that spaced learning is much more effective than massed learning for long-term memories. The efficiency of focused learning falls after an hour, which is paralleled in lab experiments at the level of single synapse between neurons, whose strength is saturated by focused stimulation. This project seeks to understand the synaptic mechanisms that eventually lead to continued synaptic growth on the time scale of many hours. The project hypothesis is that over this time period, regions inside the synapse open up to make room for a larger and stronger synapse. This research is the first step toward helping those with learning disabilities and new ways to enhance learning in others.The goal of this research is to build imaging, analytical, and computational tools to investigate the structure of nanodomains within the synapse. The nanodomains comprise nascent and active zones of synapses. The nascent zones have a fully defined postsynaptic region but lack presynaptic vesicles and hence are silent. New EM tomographic imaging combined with new computational analyses will refine understanding of nascent zones as they recruit presynaptic vesicles and are thus converted to active zones in support of synaptic plasticity that underlies the advantage of spaced learning. Existing and newly acquired large data sets will be analyzed at scale with artificial intelligence. This research will be transformative for Data-Intensive Neuroscience and Cognitive Science. The data sets and AI tools will be shared broadly with the neuroscience community through the NSF-funded 3Dem Portal (3dem.org) at the Texas Advanced Computing Center (TACC). The objectives of this project are: 1) Create computational tools to delineate nascent zones automatically by mapping presynaptic vesicle docking sites in serial sections of synapses in the hippocampal CA1 region and dentate gyrus at various times after induction of LTP or cLTD, compared to control stimulation. 2) Apply a new computational analysis based on information theory and overall synapse size to measure the storage capacity of synapses, refining the definition of synaptic weight as encompassing the enlarged active zones obtained during the conversion of nascent zones. 3) Perform realistic Monte Carlo reaction-diffusion simulations of synaptic nanodomain 3D structure and function using MCell to provide a functional estimate for the boundary between nascent and active zones and determine how changes in nascent and active zones alter efficacy at synapses during saturation and recovery of LTP and cLTD.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.