Functional connectomics of the binocular optic flow processing circuit in zebrafish

斑马鱼双目光流处理电路的功能连接组学

• 批准号: 346717992
• 负责人: Professor Dr. Winfried Denk
• 金额: --
• 依托单位: Abteilung Gene - Schaltkreise - Verhalten
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/346717992?language=en
• 关键词: Functional; connectomics; binocular; optic; flow

The complexity of interconnectivity between neurons presents a major challenge in our understanding of functions and dysfunctions of the brain. Connectomics approaches using modern electron microscopy (EM) techniques have provided not only anatomical connectivity, but also mechanistic understanding of underlying neuronal computation, when combined with physiological or behavioral evidence. Larval zebrafish is a promising system for combining EM-based connectomics and functional imaging, due to its small brain size, as well as its optical and genetic accessibility. We propose to capitalize on these advantages to reconstruct a visual circuit with a defined function and behavior, namely, the processing of horizontal optic flow. Our previous imaging study demonstrated that neurons in the zebrafish pretectum perform binocular computation of optic flow. Notably, functional response types we identified allowed us to predict a wiring diagram that was able to explain how different patterns of binocular optic flow are computed. This wiring diagram predicts prerequisite circuit motifs that remain to be experimentally verified.In this proposal, we will directly test our predicted wiring diagram by undertaking the following two work programmes. First, we will reconstruct the optic flow responsive neurons using a whole brain serial block-face electron microscopy (SBEM) stack of larval zebrafish that we have recently acquired at synapse resolution. Importantly, this stack was obtained from an animal previously recorded with two-photon calcium imaging with a battery of monocular and binocular optic flow stimuli and contains physiological information of about two hundred neurons. By tracing these physiologically characterized neurons, we will determine their interconnectivity via direct synapses. Secondly, we will examine the neurotransmitter type of optic flow-responsive cells at light microscopy (LM) level using a genetic tool that combines calcium imaging and fluorescent labeling by photoactivatable GFP. This tool, when driven by neurotransmitter-specific transgenic lines, will link physiological response to optic flow with cellular morphology of excitatory and inhibitory cells at the single-cell level. By comparing morphological features of the EM reconstructed cells and neurotransmitter-specific single cells revealed by the LM, we will be able to assign the excitatory or inhibitory identity to reconstructed neurons. Finally, all experimental data will be integrated to perform a computational modeling and simulation of the binocular optic flow processing circuit.Taking two complementary approaches, our function-guided connectomics analysis will allow us to understand connectivity mechanisms underlying the computation of binocular motion vision. We plan to make our SBEM dataset and its reconstruction results accessible to the research community in the hope of fostering future collaborations between different disciplines in neuroscience.

神经元之间相互连接的复杂性对我们理解大脑的功能和功能障碍提出了重大挑战。使用现代电子显微镜（EM）技术的连接组学方法不仅提供了解剖学连接，而且当与生理或行为证据相结合时，还提供了对潜在神经元计算的机械理解。斑马鱼幼鱼是一个很有前途的系统，结合EM为基础的connectomics和功能成像，由于其小的大脑尺寸，以及其光学和遗传的可访问性。我们建议利用这些优势来重建具有定义功能和行为的视觉回路，即水平光流的处理。我们先前的影像学研究显示，斑马鱼前顶盖的神经元执行双眼视流计算。值得注意的是，我们确定的功能反应类型使我们能够预测一个接线图，该接线图能够解释如何计算双眼光流的不同模式。该接线图预测了需要实验验证的必要电路图案。在本提案中，我们将通过以下两个工作程序直接测试我们预测的接线图。首先，我们将重建的光流反应神经元，使用全脑连续块面电子显微镜（SBEM）堆栈的幼虫斑马鱼，我们最近获得的突触分辨率。重要的是，该堆栈是从先前用双光子钙成像记录的动物中获得的，该双光子钙成像具有一组单眼和双眼视流刺激，并且包含约200个神经元的生理信息。通过追踪这些具有生理特征的神经元，我们将确定它们通过直接突触的相互连接。其次，我们将研究的神经递质类型的视流反应细胞在光学显微镜（LM）水平使用的遗传工具，结合钙成像和荧光标记的光活化GFP。当由神经递质特异性转基因系驱动时，该工具将在单细胞水平上将对光流的生理反应与兴奋性和抑制性细胞的细胞形态联系起来。通过比较EM重建细胞和LM显示的神经递质特异性单细胞的形态学特征，我们将能够将兴奋性或抑制性身份分配给重建的神经元。最后，所有的实验数据将被整合到执行计算建模和仿真的双目光流处理circuit.Taking两个互补的方法，我们的功能引导的连接组学分析将使我们能够理解连接机制的计算双目运动视觉。我们计划将SBEM数据集及其重建结果提供给研究界，希望促进神经科学不同学科之间的未来合作。