Controlling neural circuits with single-cell resolution in behaving animals

以单细胞分辨率控制行为动物的神经回路

• 批准号: 8559985
• 负责人: Christopher Fang-Yen
• 金额: $ 35万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8559985
• 关键词: Algorithms; Animal Model; Animals; Architecture; Axon; Behavior; Behavior monitoring; Behavioral Genetics; Biogenic Amines; Biological Models; Biomedical Engineering; Brain; Caenorhabditis elegans; Calcium; Cells; Code; Computer software; Data; Dendrites; Development; Devices; Dimensions; Disease; Dissection; Dopamine; Drosophila genus; Feedback; Financial compensation; Fluorescence; Food; Future; Genetic; Genetic Models; Goals; Image; Individual; Ion Channel; Ion Pumps; Label; Laboratories; Larva; Lasers; Lead; Light; Lighting; Locomotion; Methods; Modeling; Movement; Nematoda; Nerve; Nerve Degeneration; Nervous system structure; Neurologic; Neurology; Neurons; Neurotransmitters; New York; Opsin; Optics; Organism; Pathway interactions; Pattern; Pennsylvania; Performance; Process; Reagent; Resolution; Role; Serotonin; Shapes; Signal Transduction; Speed; Staging; Structure; Swimming; Synapses; Synaptic Transmission; System; Techniques; Technology; Testing; Time; Universities; Varicosity; Vision; Work; Zebrafish; awake; base; design; digital; dopaminergic neuron; fluorescence imaging; image processing; improved; instrumentation; mutant; nervous system disorder; neural circuit; neuron development; neuronal cell body; neuroregulation; new technology; next generation; optogenetics; public health relevance; relating to nervous system; research study; spatiotemporal; tool

DESCRIPTION (provided by applicant): Neural circuits are the fundamental functional units of the nervous system. A basic understanding of circuit function will provide an important basis for understanding how these circuits malfunction in neurological disorders. The study of neural circuits in small and relatively simple model animals such as C. elegans and Drosophila has many advantages, including genetic manipulability and amenability to optical techniques. Circuit analysis in these organisms has been buoyed by the recent development of 'optogenetic' methods for stimulating and inhibiting neural activity using light-sensitive ion channels and pumps [1]. Progress in optogenetics requires not only development and optimization of new opsin molecules but also new strategies and technologies for perturbing specific opsin-expressing neurons. In this project, we will develop optical and genetic methods for manipulating neural circuits with single- neuron resolution in freely moving C. elegans. This project extends previous work by Dr. Fang-Yen, in which machine vision algorithms and lasers patterned by a digital micromirror device (DMD) were used to achieve spatiotemporal control of neural activity in freely behaving worms [2]. This earlier system was limited to a spatial resolution of about 20-30 microns, which is insufficient to selectively illuminate single neurons in the animal's nerve ring (brain). In this project we will develop a next-generation system capable of resolving single neurons and subcellular features. We will approach this goal in three directions. First, we will develop instrumentation and machine vision algorithms to automatically image and track individual neurons and processes using fluorescence imaging. We will use a dual-magnification optical system to simultaneously track behavior of the entire worm and fluorescence in a smaller region. Second, we will design and implement predictive algorithms to illuminate tracked targets with compensation for the latency due to image processing and data transfer. This system will be designed with real-time feedback such that fine-tuning of its parameters can be done in an automated manner. Third, we will use our system, in combination with other methods, to elucidate the mechanisms of modulation of locomotory behaviors by dopaminergic and serotonergic circuits. By enabling, for the first time, the dynamic perturbation of individual or multiple neurons in a behaving animal, the technology we develop will become an important tool for the analysis of neural circuits, with numerous advantages compared with existing methods. In addition to improving our understanding of the circuit basis of behavior, these studies will help provide a circuit-level context for interpreting genetic mutants, for example in C. elegans models of synaptic transmission, neuronal development, and neurodegeneration. While the focus of this project is on C. elegans, we expect that our methods will be readily extensible to other model organisms. This project will be centered in Dr. Fang-Yen's laboratory but will draw on the expertise of several unpaid consultants at the University of Pennsylvania or nearby. These include Dr. David Raizen (Dept. of Neurology), an expert in C. elegans genetics and behavior, Dr. Brian Chow (Dept. of Bioengineering), an expert on optogenetic reagents, and Dr. Niels Ringstad (New York University), an expert in C. elegans genetics and neurotransmitter signaling.

描述(申请人提供)：神经回路是神经系统的基本功能单位。对电路功能的基本了解将为了解这些电路如何在神经系统疾病中发生故障提供重要的基础。在小型和相对简单的模式动物，如线虫和果蝇中研究神经回路具有许多优势，包括遗传可操纵性和光学技术的适应性。这些生物体中的电路分析得到了利用光敏离子通道和泵来刺激和抑制神经活动的“光遗传”方法的最新发展[1]的支持。光遗传学的进步不仅需要开发和优化新的视蛋白分子，还需要新的策略和技术来干扰特定的视蛋白表达神经元。在这个项目中，我们将开发光学和遗传学方法来操纵自由运动线虫中单神经元分辨率的神经回路。该项目扩展了Fang-Yen博士之前的工作，在该工作中，使用机器视觉算法和由数字微镜设备(DMD)构图的激光来实现对自由行为蠕虫神经活动的时空控制[2]。这个早期的系统被限制在大约20-30微米的空间分辨率，这不足以选择性地照亮动物神经环(大脑)中的单个神经元。在这个项目中，我们将开发能够分辨单个神经元和亚细胞特征的下一代系统。我们将从三个方向实现这一目标。首先，我们将开发仪器和机器视觉算法，使用荧光成像自动成像和跟踪单个神经元和过程。我们将使用双倍放大光学系统来同时跟踪整个蠕虫的行为和较小区域内的荧光。其次，我们将设计和实现预测算法来照亮跟踪目标，并补偿图像处理和数据传输带来的延迟。该系统将设计为具有实时反馈，以便能够以自动方式对其参数进行微调。第三，我们将使用我们的系统，结合其他方法，阐明多巴胺能和5-羟色胺能回路调节运动行为的机制。通过首次实现对行为动物中单个或多个神经元的动态扰动，我们开发的技术将成为分析神经电路的重要工具，与现有方法相比具有许多优势。除了提高我们对电路行为基础的理解外，这些研究还将有助于为口译提供电路层面的背景 基因突变，例如在线虫突触传递、神经元发育和神经退化的模型中。虽然这个项目的重点是线虫，但我们预计我们的方法将很容易扩展到其他模式生物。这个项目将以方燕博士的实验室为中心，但将利用宾夕法尼亚大学或附近几名无偿顾问的专业知识。这些人包括David Raizen博士(系神经病学)，线虫遗传学和行为方面的专家，Brian Chow博士(系)光遗传试剂专家Niels Ringstad博士(纽约大学)和线虫遗传学和神经递质信号专家Niels Ringstad博士。