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

NCS-FO：协作研究：突触纳米域的计算分析

• 批准号: 2219894
• 负责人: Kristen Harris
• 金额: $ 25.81万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219894&HistoricalAwards=false
• 关键词: NCS; FO; Collaborative; Research; Computational

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.

成功的学习和长期记忆保持是成功社会的核心，从学校的早期教育开始，延伸到整个生命。 100多年来的研究表明，对于长期记忆来说，间隔学习比集中学习更有效。 集中学习的效率在一小时后福尔斯，这在实验室实验中是在神经元之间的单个突触水平上进行的，其强度被集中刺激饱和。 该项目旨在了解最终导致突触在许多小时的时间尺度上持续生长的突触机制。 该项目的假设是，在这段时间内，突触内的区域打开，为更大更强的突触腾出空间。 这项研究是帮助那些有学习障碍的人的第一步，也是增强其他人学习能力的新方法。这项研究的目标是建立成像，分析和计算工具来研究突触内纳米结构域的结构。纳米结构域包括突触的新生区和活性区。新生区具有完全确定的突触后区域，但缺乏突触前囊泡，因此是沉默的。新的电磁断层成像结合新的计算分析将细化新生区的理解，因为它们招募突触前囊泡，从而转换为支持突触可塑性的活动区，这是间隔学习的优势。现有和新获得的大型数据集将通过人工智能进行大规模分析。这项研究将为数据密集型神经科学和认知科学带来变革。数据集和人工智能工具将通过德克萨斯州高级计算中心（TACC）的NSF资助的3Dem门户网站（3dem.org）与神经科学界广泛共享。本研究的主要目的是：1）建立一种计算工具，通过绘制LTP或cLTD诱导后不同时间海马CA 1区和齿状回突触连续切片中突触前囊泡对接位点，与对照刺激相比，自动描绘新生区域。2)应用基于信息论和整体突触大小的新的计算分析来测量突触的存储容量，将突触权重的定义细化为包含在新生区域转换期间获得的扩大的活动区域。3)执行真实的蒙特卡罗反应-使用MCell对突触纳米结构域3D结构和功能进行扩散模拟，以提供新生区和活动区之间边界的功能估计，并确定新生区和活动区的变化如何在LTP和cLTD饱和和恢复期间改变突触的功效。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。