Activity Based Tagging of Neurons

基于活动的神经元标记

基本信息

批准号：
8550801
负责人：
MARK R MAYFORD
金额：
$ 45.93万
依托单位：
SCRIPPS RESEARCH INSTITUTE, THE
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-30 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8550801
关键词：
Address Adoption Amygdaloid structure Animals Area Automobile Driving Behavior Behavior Control Behavioral Behavioral Paradigm Brain Brain region Cell Nucleus Cells Complex Data Disease Disease model Doxycycline Epigenetic Process Excision Exposure to Fluorescence Functional disorder Genetic Markers Goals Grant Hour Image Imaging Techniques Individual Intervention Learning Life Link Location Memory Mental disorders Methods Molecular Molecular Analysis Morphology Mus Neurons Neurosciences Noise Pattern Performance Population Protein Biosynthesis Proteins RNA Recruitment Activity Relative (related person)Reporting Sensory Slice Sorting - Cell Movement Specificity Stimulus System Techniques Technology Testing Tetracyclines Therapeutic Agents Time Tissues Trans-Activators Transgenes Transgenic Mice Variant approach behavior base brain tissue cell type chromophore cognitive function conditioned fear excitatory neuron high throughput analysis information processing memory retrieval nervous system disorder performance tests population based prevent promoter relating to nervous system research study response single cell analysis tool

项目摘要

DESCRIPTION (provided by applicant): Information encoding in the brain is thought to be reflected in the pattern of activation of excitatory neurons in response to a given stimulus. This suggests that, in essence, a neural cell type is defined by the various stimuli and conditions that recruit its electrical activity. Alterations in activity in specific brain regions are associated wth a variety of neurological and psychiatric diseases and the pharmacological interventions to treat these diseases alter activity in specific circuits. The cellular and molecular changes that underli complex cognitive functions such as learning and memory are likely to occur at critical specific points in the circuits activated by the relevant stimuli. A great deal of effort in neuroscience is focused on defining these activated circuits however, currently available techniques are limited to discrete brain areas, lack cellular specificity, or provide a record of activity at only a singl time point preventing the identification of consistent patterns of network activation from noise or the identification of network changes over time in response to intervention. The approach that we will develop in this grant uses a single florescent marker to identify neural activity patterns t two independent time points. This provides a number of advantages over existing technology including, the ability to analyze the brain using high throughput automated imaging techniques, to identify specific cell populations in brain slices based on their activation patterns in the whoe animal for electrophysiological, morphological, or molecular studies, and the ability to apply FACS sorting techniques to the isolation of individual nuclei for epigenetic studies. The two time points at which activity is reported can be separated by at least one week allowing the analysis of circuit changes and target cell populations that are responsive to prolonged behavioral or pharmacological intervention. This should be useful in identifying the critical changes in the brain in response to these therapies.

描述（由申请人提供）：大脑中的信息编码被认为反映在兴奋性神经元响应给定刺激的激活模式中。这表明，本质上，神经细胞类型是由各种刺激和条件定义的招募其电活动。特定大脑区域活动的改变与多种神经和精神疾病相关，治疗这些疾病的药物干预会改变特定回路的活动。学习和记忆等复杂认知功能背后的细胞和分子变化很可能发生在相关刺激激活的回路中的关键特定点上。神经科学领域的大量努力然而，专注于定义这些激活的电路，目前可用的技术仅限于离散的大脑区域，缺乏细胞特异性，或者仅提供单个时间点的活动记录，从而无法从噪声或噪声中识别网络激活的一致模式。识别网络随时间的变化以响应干预。我们将在本次资助中开发的方法使用单个荧光标记来识别两个独立时间点的神经活动模式。与现有技术相比，这提供了许多优势，包括能够使用高通量自动成像技术分析大脑，根据动物体内的激活模式识别脑切片中的特定细胞群，以进行电生理学、形态学或分子研究，以及应用 FACS 分选技术分离单个细胞核以进行表观遗传学研究的能力。报告活动的两个时间点可以相隔至少一周，从而可以分析对长期行为或药物干预做出反应的回路变化和靶细胞群。这应该有助于识别大脑响应这些疗法的关键变化。