Classification of mouse RGC subtypes using large-scale multielectrode recording

使用大规模多电极记录对小鼠 RGC 亚型进行分类

• 批准号: 7642260
• 负责人: DAVID A FELDHEIM
• 金额: $ 19.8万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7642260
• 关键词: Affect; Biological Models; Blindness; Cells; Classification; Color; Degenerative Disorder; Development; Disease; Electrodes; Electrophysiology (science); Foundations; Future; Genes; Genetic; Glaucoma; Goals; Grant; Human; Image; Individual; Injury; Knowledge; Label; Link; Modeling; Molecular; Monitor; Morphology; Motion; Mus; Neurons; Pattern; Perception; Physiological; Physiology; Play; Positioning Attribute; Principal Investigator; Property; Relative (related person); Retina; Retinal; Retinal Degeneration; Retinal Diseases; Retinal Ganglion Cells; Role; Solid; Structure; Study models; System; Techniques; Technology; Transgenic Mice; Vision; Visual; Visual Fields; Visual system structure; Wild Type Mouse; Work; cell type; density; design; neural circuit; novel; public health relevance; receptive field; research study; response; retinal damage; spatiotemporal; therapy development; visual stimulus

DESCRIPTION (provided by applicant): The goal of this co-principal investigator, interdisciplinary proposal is to develop techniques for the comprehensive functional characterization of retinal ganglion cell (RGC) types in the mouse retina, and to combine this characterization with mouse transgenic technology in order to determine the relationships between the morphology and physiology of RGC types. Our experiments take advantage of novel multi- electrode array (MEA) recording systems built in the Litke lab. These systems contain over 500 electrodes and can simultaneously record the activity from hundreds of neurons in an intact retina; this represents a 10 fold better yield over currently available technology. We find that this increase in yield is critical for unambiguous functional classification and reliable characterization of the many RGC types in the retina. Furthermore, we hypothesize that the detailed information provided by these MEA systems will make it possible to match the physiologically identified neurons to optically imaged RGCs. Such a match will create a link between function and structure in an unprecedented manner. We have two main aims to accomplish our goals. In the first aim we propose to use two types of large- scale MEAs, a 512-electrode array with 605m interelectrode spacing, and a high density 519-electrode array with 305m spacing, to characterize the receptive field and mosaic properties of RGC types of wild type mice. We have chosen to use these arrays to characterize RGC types in mice because the mouse retina has become an important model to study the role various genes/molecules play in the development of retinal circuitry. The mouse also serves as a model for studying the progression of retinal-degenerative diseases such as glaucoma. In the second aim we plan to use the large-scale MEA technology to correlate the morphological RGC types to their functional counterparts. In addition to being classified using physiological criteria, RGCs are classified using morphological criteria. However, only in rare circumstances can cells be classified by both morphological and physiological properties. Experiments proposed in this aim are designed to correlate morphologically labeled cells with their physiological properties. We will do this by matching the morphological images of GFP marked RGCs with their electrophysiological images and receptive fields. (As described in section c, the electrophysiological image is a technique, developed by the Litke lab, for imaging the spatiotemporal pattern of electrical activity generated by individual neurons.). The purpose of this grant is to obtain a comprehensive characterization of retinal ganglion cell (RGC) types in the mouse retina. This knowledge is essential in order to understand how various molecular and environmental perturbations affect the retina's development and function. Vision is a crucial component of human perception and blindness is a devastating affliction. Understanding what the retinal circuits are is the first step toward understanding how they develop and is also essential to better understand the progression of retinal degenerative diseases (Are specific circuits differentially affected in disease?). In the last 10-20 years, modern molecular techniques, in combination with powerful advances in imaging and electrophysiology, have led to an increase in the use of the mouse as a model system for studying retinal circuitry. It is likely that the knowledge obtained from experiments proposed here will be essential to all who use the mouse visual system as a model. PUBLIC HEALTH RELEVANCE: The first aim of this project is to develop techniques for the classification and functional characterization of retinal ganglion cell (RGC) types in the mouse retina. The second aim is to combine this characterization with mouse transgenic technology in order to determine the relationship between the morphology and physiology of RGC types. Upon completion of the proposed aims, we will be in a strong position to use these techniques to determine how various genetic, activity-related, and disease perturbations affect and control the development of neural circuits. This will build a solid foundation for future work aimed at developing therapies for treating retinal damage due to injury or disease.

描述(由申请人提供)：该联合首席研究员的目标是开发小鼠视网膜中视网膜神经节细胞(RGC)类型的综合功能表征技术，并将该表征与小鼠转基因技术相结合，以确定RGC类型的形态和生理之间的关系。我们的实验利用了Litke实验室建立的新型多电极阵列(MEA)记录系统。这些系统包含500多个电极，可以同时记录完整视网膜中数百个神经元的活动；这意味着产量是目前可用技术的10倍。我们发现，这种产量的增加对于明确的功能分类和对视网膜中许多RGC类型的可靠表征至关重要。此外，我们假设这些MEA系统提供的详细信息将使生理学识别的神经元与光学成像的RGC匹配成为可能。这样的匹配将以前所未有的方式在功能和结构之间建立联系。我们有两个主要目标来实现我们的目标。在第一个目的中，我们建议使用两种类型的大规模MEA，一个是具有605m电极间距的512电极阵列，另一个是具有305m间距的高密度519电极阵列，以表征RGC类型野生型小鼠的感受野和镶嵌特性。我们选择使用这些阵列来表征小鼠的RGC类型，因为小鼠的视网膜已经成为研究各种基因/分子在视网膜电路发展中所起作用的重要模型。这只小鼠也是研究青光眼等视网膜退行性疾病进展的模型。在第二个目标中，我们计划使用大规模的MEA技术来将形态RGC类型与它们的功能对应联系起来。除了使用生理标准进行分类外，视网膜节细胞还使用形态标准进行分类。然而，只有在极少数情况下，细胞才能根据形态和生理特性进行分类。在这一目标中提出的实验旨在将形态标记的细胞与它们的生理特性相关联。我们将通过将GFP标记的视网膜节细胞的形态图像与它们的电生理图像和感受野进行匹配来实现这一点。(如c部分所述，电生理图像是由Litke实验室开发的一种技术，用于成像单个神经元产生的电活动的时空模式。)这项资助的目的是获得对小鼠视网膜中视网膜神经节细胞(RGC)类型的全面表征。为了了解各种分子和环境干扰如何影响视网膜的发育和功能，这些知识是必不可少的。视觉是人类感知的重要组成部分，失明是一种毁灭性的痛苦。了解什么是视网膜回路是了解它们如何发展的第一步，也是更好地了解视网膜退行性疾病的进展(特定回路在疾病中受到不同影响吗？)的关键。在过去的10-20年里，现代分子技术与成像和电生理学的强大进步相结合，导致越来越多地使用小鼠作为研究视网膜回路的模型系统。很可能，从这里提出的实验中获得的知识对于所有使用鼠标视觉系统作为模型的人来说都是必不可少的。 公共卫生相关性：该项目的第一个目标是开发小鼠视网膜中视网膜神经节细胞(RGC)类型的分类和功能表征技术。第二个目的是将这一特征与小鼠转基因技术相结合，以确定RGC类型的形态和生理之间的关系。在完成提议的目标后，我们将处于有利地位，可以使用这些技术来确定各种遗传、活动相关和疾病扰动如何影响和控制神经回路的发展。这将为未来旨在开发治疗因损伤或疾病造成的视网膜损伤的疗法的工作奠定坚实的基础。