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

新的遗传工具以及成像和模拟技术的持续快速发展使得神经科学在数据采集、数据集成、解释和设计方面日益成为数据密集型的研究学科。然而，这些元素通常在研究工作流程中形成一个闭环。另一方面，这个过程本身产生的数据必须与其特定的方法相关。然而，更重要的是，新获得的数据需要与现有的数据资源相联系和关联，因为单个实验或数据通常只能揭示整体情况的一小部分。因此，本项目将建立Larvalbrain2.0，作为黑腹果蝇幼虫结构、分子、生理和行为结果的动态多尺度多层次图谱和数据收集。果蝇幼虫似乎特别适合这种方法，因为它的大脑非常简单，只有大约1万个神经元，这是几个主要方法的主题，以重建每个细胞，甚至在光学和电子显微镜水平上重建整个连接组。与此同时，基于监测和操纵神经元活动的新技术的整合，在确定在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回路方面取得了重大进展。具体来说，我们将建立Larvalbrain2.0来研究幼虫对盐的感知和加工的选择行为、摄食和学习记忆。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)将所有基因驱动系表达模式信息整合到Larvalbrain2.0中。3)构建整合果蝇幼虫脑结构、行为、分子和生理数据的Larvalbrain2.0协同数据研究平台。4)整合分子、行为和生理数据集，建立首个盐信息处理幼虫脑网络模型。

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.