Transgenic strategy to map structure and function of neural circuits in retina

绘制视网膜神经回路结构和功能的转基因策略

• 批准号: 7936911
• 负责人: MARKUS MEISTER
• 金额: $ 42.76万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7936911
• 关键词: Accounting; Achievement; Action Potentials; Address; Animals; Area; Axon; Back; Basic Science; Behavior; Brain; Brain Part; Cataloging; Catalogs; Cells; Chloride Channels; Complex; Disease; Environment; Equipment and supply inventories; Eye; Fiber Optics; Gene Expression Profiling; Generations; Genes; Genetic; Genetic Markers; Genomics; Goals; Image; Interneurons; Knock-in Mouse; Label; Lead; Light; Location; Maps; Measures; Methods; Modification; Molecular; Morphology; Movement; Mus; Neurons; Neurophysiology - biologic function; Neurosciences; Optic Nerve; Output; Partner in relationship; Pathway interactions; Perception; Photoreceptors; Physiological; Population; Process; Property; Regulatory Element; Reporting; Research; Retina; Retinal; Retinal Ganglion Cells; Running; Screening procedure; Shapes; Site; Stream; Structure; Synapses; System; Techniques; Technology; Testing; Transgenic Mice; Transgenic Organisms; Travel; Trees; Vision; Visual; Visual Fields; Visual system structure; Wheat Germ Agglutinins; Work; base; cell type; experience; extrastriate visual cortex; ganglion cell; image processing; improved; information processing; inhibitor/antagonist; interest; meetings; neural circuit; public health relevance; receptive field; recombinase; relating to nervous system; tool; visual information; visual neuroscience; visual process; visual processing

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area 06, Enabling technologies, specific Challenge Topic 06-NS- 106: "Validating new methods to study brain connectivity." The long-term goal of this research is a fundamental understanding of how the eye communicates with the brain. More immediately, the research serves to validate and improve a set of genetic tools for the study of neural circuits. The retina is a complex network of neurons in the back of the eye that converts a visual image into streams of action potentials that travel through the fibers of the optic nerve to the brain. The circuits of the retina begin with the photoreceptor cells that sense the light, pass through bipolar cells and other interneurons, and end with the ganglion cells that form the optic nerve. In all, the retina uses over 50 different types of neurons; the ganglion cells alone comprise about 20 different types. Each of these ganglion cell types extracts and reports a different aspect of the visual scene. A long-term goal of visual neuroscience is to understand what kind of visual processing occurs in each of these streams, and how those processes are implemented by the elaborate neural circuitry of the retina. New genetic methods are beginning to accelerate our understanding of neural systems. In particular, there has been great interest in finding genetic markers that distinguish the brain's many different neuron types. Experience has shown that availability of such a marker, in combination with new molecular and physiological approaches, can dramatically accelerate scientific progress on the structure and function of the corresponding neural circuits. Here we propose to apply these methods to assemble a complete catalog of the ganglion cell types in the mouse retina and to analyze their visual functions. The specific research goals are: (1) to find genes that are expressed specifically in one type of retinal ganglion cell; (2) to construct transgenic mice based on these genes in which all neurons of a given type are marked; (3) to exploit these lines for targeted studies of the structure and function of retinal pathways. For each type of retinal ganglion cell, we will determine the distribution of the neurons across the retina, how their dendritic fields cover visual space, and where in the brain their axons project. At the single-cell level we will examine the shape and location of the ganglion cell's dendritic tree within the retina to deduce its likely synaptic partners among retinal interneurons. To analyze visual function, we will determine what image features each ganglion cell type extracts from the visual scene. In addition, we will assess its involvement in ecologically important computations, such as the processing of image movement, and adaptation to the visual environment. This research will lead to a qualitatively new understanding of retinal function. It will inform our understanding of higher visual areas that draw all their input from the retina. Furthermore, the work will validate and gather experience with a set of genetic tools that can generalize to all brain circuits. PUBLIC HEALTH RELEVANCE: This project concerns basic research into the function of brain circuits. It will develop and test new genetic methods for visualizing types of nerve cells, and exploit these markers to understand how the circuits process information. In the long run, this will enhance our understanding of how the brain works, and how it fails in certain disorders.

描述（由申请人提供）：本申请涉及广泛的挑战领域06，使能技术，具体的挑战主题06- ns - 106：“验证研究大脑连接的新方法”。这项研究的长期目标是对眼睛如何与大脑交流有一个基本的了解。更直接的是，这项研究有助于验证和改进一套用于研究神经回路的遗传工具。视网膜是位于眼睛后部的一个复杂的神经元网络，它将视觉图像转化为动作电位流，这些动作电位流通过视神经纤维传递到大脑。视网膜的回路从感光细胞开始，通过双极细胞和其他中间神经元，最后以形成视神经的神经节细胞结束。总的来说，视网膜使用超过50种不同类型的神经元；仅神经节细胞就有大约20种不同的类型。这些神经节细胞类型中的每一种提取和报告视觉场景的不同方面。视觉神经科学的一个长期目标是了解在这些视神经流中发生了什么样的视觉处理，以及这些过程是如何通过视网膜复杂的神经回路来实现的。新的遗传方法开始加速我们对神经系统的理解。特别是，人们对寻找区分大脑中许多不同神经元类型的遗传标记非常感兴趣。经验表明，这种标记物的可用性，结合新的分子和生理学方法，可以显著加快有关神经回路结构和功能的科学进展。在这里，我们建议应用这些方法来组装一个完整的目录神经节细胞类型在小鼠视网膜和分析他们的视觉功能。具体的研究目标是：(1)寻找在一种视网膜神经节细胞中特异性表达的基因；(2)以这些基因为基础构建转基因小鼠，在转基因小鼠中标记特定类型的所有神经元；(3)利用这些细胞系对视网膜通路的结构和功能进行针对性研究。对于每种类型的视网膜神经节细胞，我们将确定神经元在视网膜上的分布，它们的树突场如何覆盖视觉空间，以及它们的轴突在大脑中的位置。在单细胞水平，我们将检查视网膜内神经节细胞树突状树的形状和位置，以推断其在视网膜中间神经元中可能的突触伙伴。为了分析视觉功能，我们将确定每种神经节细胞类型从视觉场景中提取的图像特征。此外，我们将评估其在生态重要计算中的参与，如图像运动的处理，以及对视觉环境的适应。这项研究将导致对视网膜功能的定性新认识。它将帮助我们理解从视网膜获取所有输入信息的高级视觉区域。此外，这项工作将验证并收集一套遗传工具的经验，这些工具可以推广到所有的大脑回路。