NeuroNex Technology Hub: Live Imaging of the C.elegans Connectome

NeuroNex 技术中心：线虫连接组的实时成像

• 批准号: 1707401
• 负责人: Oliver Hobert
• 金额: $ 209.79万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707401&HistoricalAwards=false
• 关键词: NeuroNex; Technology; Hub; Live; Imaging

The human brain is composed of billions of interconnected neurons that form highly complex neuronal circuits that process information and encode behavior. Many questions about these interconnected networks are unanswered: How variable are they from individual to individual, how do they change throughout life, how does the environment impact on them, and what are the genetic blueprints that generate these networks. Disruptions of the genetic blueprints that build neuronal networks are the likely cause of many human neurological diseases. In order to study neuronal networks in the brain, it is of paramount interest to easily visualize the patterns of connectivity of neurons, ideally in the context of live organisms. The cellular complexity of brains prevents such types of studies in complex organisms, and this project therefore uses a simple invertebrate model system, the nematode C.elegans, to visualize all the major neuronal connections of its simple nervous system. Previous studies have amply demonstrated that mechanisms of brain patterning discovered in C.elegans are conserved in other animals as well. The investigators develop and use cutting-edge fluorescent reporter technology, combined with microscopical and computer vision technology to achieve this goal. The project's construction of animals in which most neuronal connections are fluorescently labeled provides a major resource. This resource is made available to the large field of C.elegans researchers who with that resource can study the many questions that relate to circuits in the brain, including the decoding of the nervous system's genetic blueprint. In addition, the project includes cutting-edge, interdisciplinary training opportunities for undergraduate and graduate students from diverse backgrounds, as well as postdoctoral fellows.The project entails the development and dissemination of tools that empower the C.elegans neuroscience community to study the connectome of the nematode C.elegans. In the first phase, the technology hub develops two sets of tools: One group uses fluorescent-based reporter technology (GRASP and iBlinc as potential alternative) to generate a large number of transgenic C.elegans strains in which the main "edges" of the entire wiring diagram (i.e., pairwise combinations of neurons) are visualized. As part of this project, this resource is distributed throughout the C.elegans community to enable labs with long-standing interest in various aspects of neuronal development and function and with a focus on specific neuronal circuits and behaviors to use these synaptic labels to examine variability, development, and plasticity of these connections. In parallel, the other group develops microfluidic-based and automated image analysis technologies to precisely quantify the structure of the connectome and to enable high-throughput screening of worm population for defects in synaptic wiring. Computer vision and machine learning is used to score automatically disruptions of synaptic wiring to detect subtle changes in wiring. This NeuroTechnology Hub award is part of the BRAIN Initiative and NSF's Understanding the Brain activities.

人脑由数十亿个相互连接的神经元组成，这些神经元形成了处理信息和编码行为的高度复杂的神经元电路。关于这些相互连接的网络的许多问题都没有答案：它们在不同个体之间有多大的变化，它们在一生中是如何变化的，环境对它们有什么影响，以及产生这些网络的基因蓝图是什么。构建神经元网络的基因蓝图的破坏可能是许多人类神经疾病的原因。为了研究大脑中的神经元网络，最有兴趣的是容易地可视化神经元的连接模式，理想情况下是在活的有机体的背景下。大脑的细胞复杂性阻碍了在复杂生物体中进行这种类型的研究，因此该项目使用了一个简单的无脊椎动物模型系统--线虫线虫--来可视化其简单神经系统的所有主要神经元连接。以前的研究已经充分证明，在线虫中发现的大脑模式机制在其他动物中也是保守的。研究人员开发和使用尖端的荧光报告技术，结合显微镜和计算机视觉技术来实现这一目标。该项目构建的动物中，大多数神经元连接都被荧光标记，这提供了一个主要的资源。这一资源可供广大线虫研究人员使用，他们可以利用这些资源研究与大脑电路有关的许多问题，包括解码神经系统的遗传蓝图。此外，该项目包括为来自不同背景的本科生和研究生以及博士后研究员提供前沿的跨学科培训机会。该项目需要开发和传播工具，使线虫神经科学界能够研究线虫的连接组。在第一阶段，技术中心开发了两套工具：一组使用基于荧光的报告技术(GRASH和iBlinc作为潜在的替代方案)来生成大量转基因线虫菌株，在这些菌株中，整个接线图的主要“边”(即神经元的成对组合)被可视化。作为该项目的一部分，该资源分布在线虫社区，使长期对神经元发育和功能的各个方面感兴趣并专注于特定神经元电路和行为的实验室能够使用这些突触标记来检查这些连接的可变性、发育和可塑性。与此同时，另一个小组开发了基于微流体的自动图像分析技术，以精确量化连接体的结构，并能够高通量筛选蠕虫种群以寻找突触连接中的缺陷。计算机视觉和机器学习被用来自动对突触连接中断进行评分，以检测连接的细微变化。这个神经技术中心奖是大脑倡议和NSF了解大脑活动的一部分。