Investigation of the Synaptic Molecular Network using Multiplexed Imaging

使用多重成像研究突触分子网络

基本信息

批准号：
10651858
负责人：
Mark Bathe
金额：
$ 18.93万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10651858
关键词：
Antidepressive Agents Award BRAIN initiative Bayesian Analysis Bayesian Modeling Bayesian Network Bayesian Prediction Binding Biochemical Brain Brain Diseases Calcium Central Nervous System Chemicals Complex DNA Data Data Set Dependence Derivation procedure Disease Environment Etiology Excitatory Synapse Generations Genes Glutamates Health Hippocampus Image Imaging Device In Situ In Vitro Intervention Investigation Learning Measurement Measures Memory Mental disorders Modality Modeling Molecular Morbidity - disease rate Morphologic artifacts Mutation Neurodegenerative Disorders Neurodevelopmental Disorder Neurons Neurosciences Pharmaceutical Preparations Phosphorylation Phosphotransferases Population Probability Process Proteins Proteome Protocols documentation Psychiatric therapeutic procedure Quality Control Rattus Reagent Research Personnel Resolution Resources Role Schizophrenia Site Stains Structure Synapses System Techniques Testing Time antibody conjugate autism spectrum disorder drug candidate drug development experimental study genetic regulatory protein high dimensionality in vitro testing knock-down molecular imaging molecular phenotype multiplexed imaging nervous system disorder novel postsynaptic predictive modeling presynaptic rational design receptor scaffold screening small molecule synaptic function therapy design tool

项目摘要

PROJECT SUMMARY The synaptic molecular network is a complex, tightly interacting system of hundreds of proteins that forms the basis for learning, memory, and other brain functions. It is a disrupted locus of many neurological, neurodegenerative, and psychiatric disorders, and is a focal point of action for small molecule psychiatric treatments. Understanding this network and the rules that govern it is necessary for understanding the molecular etiology of brain diseases, and for the rational design of psychiatric drugs. To achieve this, we propose to apply PRISM, a recently developed multiplexed imaging tool which allows in-situ measurements of many proteins at single-synapse resolution, to construct and validate a causal, predictive model of interdependencies among proteins of the glutamatergic synapse, their subunit composition, and activation state. We will expand the repertoire of targets available for PRISM and measure these targets in a large population of synapses across a variety of chemical environments. This will serve two purposes: measurement of many ‘snapshots’ of the synaptic molecular network pulled in different directions, which is necessary for subsequent model learning, and in-depth characterization of the downstream synaptic biochemical effects of perturbations that include antidepressants of different classes. We will also combine sensitive live calcium imaging of synapse activity with subsequent PRISM measurements of the same synapses. We will then use these data to construct a Bayesian network model of causal dependencies between the probability distributions of these measurements. This Bayesian network will yield predictions about causal connections between nodes and downstream effects of perturbing certain targets, which we will subsequently test. The result of this study will be a powerful, predictive model connecting up to 30 measures of protein levels, subunit compositions, phosphorylation states, and synapse activity. This model will provide a unifying context to integrate mechanistic details of interactions between specific synaptic actors into a holistic understanding of the synapse as a whole. It will also provide predictions about system-level effects of chemical perturbations, potentially paving the way for an entirely novel modality for in vitro screening of psychiatric treatments.

项目摘要合成分子网络是数百种蛋白质的复杂，紧密相互作用的系统，形成了学习，记忆和其他大脑功能的基础。这是许多神经系统的轨迹，神经退行性和精神疾病，是小分子精神病的焦点治疗。了解该网络及管辖的规则是理解分子的必要条件脑疾病的病因，以及精神药物的合理设计。为了实现这一目标，我们建议申请 Prism是一种最近开发的多重成像工具单节解析，以构建和验证相互依赖的因果，预测模型谷氨酸能突触的蛋白质，其亚基组成和激活状态。我们将扩展可用于棱镜的目标的曲目，并在跨A的大量突触中衡量这些目标各种化学环境。这将有两个目的：测量突触的许多“快照” 分子网络朝着不同的方向拉动，这对于随后的模型学习是必不可少的，深入扰动的下游合成生化作用的表征，包括抗抑郁药不同的类。我们还将结合突触活动的敏感活钙成像与随后的棱镜相同突触的测量。然后，我们将使用这些数据来构建一个贝叶斯网络模型这些测量值的概率分布之间的因果关系。这个贝叶斯网络将关于节点与某些目标的下游效应之间因果关系的产量预测，我们将随后测试。这项研究的结果将是一个强大的预测模型，可达30 蛋白质水平，亚基组成，磷酸化状态和突触活性的度量。这个模型将提供统一的环境，将特定合成参与者之间相互作用的机械细节整合到一个对整个突触的整体理解。它还将提供有关系统级效应的预测化学扰动，有可能为完全新颖的方式铺平了道路精神病治疗。