Larvalbrain2.0: A research platform for the larval brain of Drosophila melanogaster

新型遗传工具以及成像和模拟技术的持续快速发展使神经科学在数据获取、数据整合、解释和设计方面成为一个日益数据密集的研究学科。然而，这些元素往往在研究工作流程中形成一个闭环。另一方面，这一过程本身也产生数据，这必须与其特定的方法有关。然而，更重要的是，新获得的数据需要与现有的数据资源相关联，因为单个实验或数据通常只能揭示全局的一小部分，因此，在本项目中，我们将建立Larvalbrain2.0作为一个动态的多尺度多层次图谱和数据收集果蝇幼虫的结构，分子，生理和行为结果。果蝇幼虫似乎特别适合这种方法，因为它的简单大脑仅由大约10.000个神经元组成，这是几种主要方法的主题，可以在光学和电子显微镜水平上重建每个细胞甚至整个连接体。与此同时，基于监测和操纵神经元活动的新技术的整合，在识别不同分辨率水平和数据空间中选择适当行为的大脑机制和电路方面取得了重大进展。具体来说，我们将建立Larvalbrain2.0来研究幼虫的选择行为、摄食以及依赖于盐感知和加工的学习和记忆。该项目包括四个基石：1）LM标准脑图谱的改进版本与EM连接器的集成。2)将所有关于遗传驱动线表达模式的可用信息整合到Larvalbrain2.0中。3)构建Larvalbrain2.0，这是一个协作数据和研究平台，整合了果蝇幼虫大脑的结构，行为，分子和生理数据。4)整合分子、行为和生理数据集，首次建立了盐信息处理的幼虫脑网络模型。

The ongoing rapid development of novel genetic tools as well as imaging and simulation technologies makes neuroscience an increasingly data-intensive research discipline with respect to data acquisition, data integration, interpretation and design. However, these elements often form a closed loop in a research workflow. On the other hand, this process in itself generates data, which has to be related to its particular approach. However, even more importantly, the newly acquired data needs to be linked and related to existing data resources, since a single experiment or piece of data usually reveals only a tiny fraction of the overall picture.Accordingly, in this project we will establish Larvalbrain2.0 as a dynamic multi-scale multi-level atlas and data collection of structural, molecular, physiological and behavioral results of Drosophila melanogaster larvae. The Drosophila larva seems to be particularly suited for such an approach due to its simplistic brain that consists of only about 10.000 neurons, which is the subject of several major approaches to reconstruct each cell and even its entire connectome at the light and electron microscopy level. At the same time, based on the integration of new technologies for monitoring and manipulating neuronal activity, significant progress was made to identify brain mechanisms and circuits underlying the selection of appropriate behaviors across different resolution levels and data spaces. In detail, we will establish Larvalbrain2.0 to study larval choice behavior, feeding and learning and memory depending on salt perception and processing. The project comprises four cornerstones: 1) The integration of an improved version of the LM standard brain atlas with the EM connector. 2) Integrating all available information on genetic driver-line expression patterns into Larvalbrain2.0. 3) Building Larvalbrain2.0, a collaborative data and research platform that integrates structural, behavioral, molecular and physiological data from the larval brain of Drosophila. 4) The integration of molecular, behavioral and physiological data sets to establish a first larval brain network model for salt information processing.