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Developing Molecular and Computational Tools to Enable Visualization of Synaptic Plasticity In Vivo

开发分子和计算工具以实现体内突触可塑性的可视化

基本信息

批准号：
10009886
负责人：
Richard L Huganir
金额：
$ 175.71万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10009886
关键词：
AMPA Receptors Address Algorithms Alzheimer&apos s Disease Animals Area Behavior Behavioral Paradigm Brain Brain region CRISPR/Cas technology Cells Cephalic Clustered Regularly Interspaced Short Palindromic Repeats Cognition Communication Communities Complex Computer Analysis Data Set Detection Disease Ensure Excitatory Synapse Fright Future Genes Genetic Goals Human Image Individual Intellectual functioning disability Investigation Knock-in Knock-in Mouse Label Learning Machine Learning Manuals Measurement Mediating Memory Methodology Methods Microscopy Molecular Molecular Computations Mus Nature Neocortex Neurons Neurosciences Optics Process Proteins Reagent Regulation Research Resolution Role Schizophrenia Shapes Silicon Speed Synapses Synaptic Transmission Synaptic plasticity Techniques Time Training Viral Virus Visualization analytical tool autism spectrum disorder automated algorithm base brain volume calcium indicator cell type computerized tools deep learning density experimental study in vivo in vivo two-photon imaging interest minimally invasive multidisciplinary neurological pathology neuropsychiatric disorder novel novel strategies novel therapeutic intervention open source postsynaptic relating to nervous system sensor synaptic function tool two photon microscopy

项目摘要

Project Summary Developing new methodological and analytical tools to address currently insurmountable experimental questions is crucial to the future of neuroscience. While recent advances in two-photon microscopy and activity sensors have revolutionized our understanding of the cellular and circuit basis of behavior, many barriers still exist that preclude fully exploring the molecular basis of these processes in vivo. This is an important question, as modulating synaptic strength is thought to underlie higher brain functions such as learning and memory, whereas synaptic degradation is observed in many neurological pathologies. Despite the clear significance of synaptic communication, a large-scale analysis of how synapses across the brain are distributed and change during learning has not been performed, mainly due to technical difficulties arising from the immensely complex nature of synaptic networks. Here, we present a suite of novel methodologies that breaks through these barriers. Our novel approach leverages CRISPR-based labeling of endogenous synaptic proteins, in vivo two-photon microscopy to visualize fluorescently tagged synapses in behaving animals, and deep-learning-based automatic synapse detection. Using these minimally invasive methods, we will be able to longitudinally track how the strength of millions of individual synapses change during learning. By developing and enabling new strategies to automatically detect and track vast numbers of synapses across entire brain regions, this pioneering approach has the potential to provide us with an unprecedented view of synapses in behaving animals, enabling new discoveries regarding how dynamic regulation of synaptic strength encodes learning and memory.

项目总结