Deciphering Biochemical Networks in Single Dendritic Spines

破译单树突棘中的生化网络

• 批准号: 9150333
• 负责人: Ryohei Yasuda
• 金额: $ 95.5万
• 依托单位: MAX PLANCK FLORIDA CORPORATION
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9150333
• 关键词: Autistic Disorder; Biochemical; Complex; Data; Data Set; Dementia; Dendritic Spines; Disease; Elements; Failure; Fluorescence Resonance Energy Transfer; Goals; Lead; Learning; Measurement; Measures; Mediating; Memory; Mental Retardation; Nerve Degeneration; Neurons; Output; Play; Proteins; Psyche structure; Signal Transduction; Signaling Protein; Surface; Synaptic plasticity; Systems Development; Techniques; Vertebral column; base; imaging system; improved; insight; postsynaptic; public health relevance; sensor

 DESCRIPTION (provided by applicant): Synaptic plasticity, a cellular basis of learning and memory, is mediated by a complex biochemical signaling network consists of hundreds of signaling proteins. In particular, Ca2+- dependent signaling in dendritic spines, tiny postsynaptic compartments emanating from dendritic surface, plays a key role in the induction of long-term synaptic plasticity. In order to understand the operational principles of this network and the mechanisms underlying synaptic plasticity, the activity of hundreds of proteins under many manipulations needs to be measured in single dendritic spines during synaptic plasticity. The activity of proteins in spines has been measured using advanced fluorescence resonance energy transfer (FRET)-based techniques. However, thus far only a small fraction of the entire network has been measured. Our understanding of signaling networks is limited by this scarcity of measurements for signaling components. Thus, the goal of this project is to establish a high-throughput system for the development and optimization of signaling sensors, and a fully automated system for imaging signal transduction during plasticity in single dendritic spines. Using this high-throughput imaging system, we aim to improve the overall efficiency of data output by orders of magnitude, producing large data sets that could be further analyzed for information flow in the signaling network and connectivity between network elements. Thus, this project is expected to lead to a dramatic advance in our understanding of intracellular signaling in neurons and provide key insights into the mechanisms underlying synaptic plasticity and ultimately learning and memory.