BRAIN CONNECTS: A Center for High-throughput Integrative Mouse Connectomics

项目总结/摘要 该项目将证明生成完整的突触级大脑地图的可行性 （连接组）通过开发一个连续切片的电子显微镜管道，可以扩展到整个小鼠 个脑袋这项工作将图像10立方毫米，本身是一个前所未有的大数据集，可能超过几十 的PB。然而，老鼠的大脑要大50倍。实现这一宏伟目标需要在以下方面取得进展： 全脑染色、成像、图像处理、分析和传播工具。我们将扩大规模并测试 这些工具通过产生海马结构的连接体，海马结构是记忆的关键大脑区域， 空间导航具体来说，我们将通过microCT扫描整个大脑来定义我们的感兴趣体积。 然后我们将其切成半薄的连续切片，并用多束扫描电子显微镜对其进行成像 和离子束铣削。这种技术对一层薄薄的组织成像，然后将其移除以显示下一层 直到每个切片完全成像，最大限度地减少以前超薄切片方法造成的失真。 成像数据将由我们最先进的管道的改进版本处理。质量后 监控和图像压缩，我们的自动化系统将从成像切片组装完整的体积 然后标记组织成分：神经元，神经胶质，血管，髓磷脂，细胞体和突触。这 然后，重建将被校对并登记到艾伦研究所的大脑图谱中，使我们能够将我们的大脑与大脑的结构联系起来。 其他类型的数据。我们的分析将通过区域和层识别细胞类型，并揭示详细的 海马结构电路的连接。使用定制软件，我们将整合这些结构结果 与其他基于光学显微镜和单细胞基因表达的方法相比， 将我们的结果与大量关于海马结构和功能的文献联系起来。促进 不同的观点，我们将涉及来自代表性不足的背景在校对本科生 和科学发现阶段的工作，为他们提供指导以及研究经验。 我们将通过扩大规模，将这些数据转化为科学界和公众的持久资源 通过在线共享工具免费访问，允许任何感兴趣的一方渲染，校对或以其他方式分析 细胞和电路在这卷。为了说明这种资源如何与其他发现相结合，我们将 根据细胞的形态和连通性定义细胞类型， 作业和基于转录组学的分类，并将这些信息与以前的工作相结合。 最后，我们将在我们的数据中定义本地和远程微电路图案，并使用它来识别电路原理 以及记忆和空间认知的机制，通过测试和改进海马的模型， 阵在整个项目中，我们将监控关键性能参数，例如 一个单一的显微镜，以评估扩大到整个小鼠大脑连接体的可行性和成本。

Project Summary/Abstract The proposed project will demonstrate the feasibility of generating a complete synapse-level brain map (connectome) by developing a serial-section electron microscopy pipeline that could scale to a whole mouse brain. This work will image 10 cubic millimeters, itself an unprecedentedly large dataset that may exceed tens of petabytes. Yet the mouse brain is 50 times larger. Reaching this ambitious goal will require advances in whole-brain staining, imaging, image-processing, analysis, and dissemination tools. We will scale and test these tools by producing a connectome of the hippocampal formation, a critical brain region for memory and spatial navigation. Specifically, we will define our volume of interest via microCT scanning of a whole brain. Then we will cut it into semithin serial sections and image them with multibeam scanning electron microscopy and ion beam milling. This technique images a thin layer of tissue and then removes it to reveal the next layer until each section is fully imaged, minimizing distortions caused by previous ultra-thin sectioning approaches. The imaging data will be processed by an improved version of our state-of-the-art pipeline. After quality monitoring and image compression, our automated system will assemble the full volume from imaged slices and then label tissue elements: neurons, glia, blood vessels, myelin, cell bodies, and synapses. This reconstruction will then be proofread and registered to the Allen Institute brain atlas, allowing us to relate our data to other types of data. Our analysis will identify cell types by region and layer, and reveal the detailed connectivity of hippocampal formation circuits. Using custom software, we will integrate these structural results on cell types with other approaches based on light microscopy and single-cell gene expression, allowing us to relate our results to the extensive literature on hippocampal formation structure and function. To promote diverse perspectives, we will involve undergraduates from underrepresented backgrounds in the proofreading and scientific discovery phases of our work, offering them mentoring as well as research experience. We will turn these data into a lasting resource for the scientific community and the public by scaling up free access via online sharing tools to allow any interested party to render, proofread, or otherwise analyze the cells and circuits in this volume. To illustrate how this resource can be combined with other discoveries, we will define cell types based on their morphology and connectivity, characterize the relationship between these assignments and transcriptomic-based classifications, and integrate this information with previous work. Finally, we will define local and long-range microcircuit motifs in our data and use it to identify circuit principles and mechanisms of memory and spatial cognition, by testing and improving models of the hippocampal formation. Throughout the project, we will monitor key performance parameters, such as imaging throughput of a single microscope, to evaluate the feasibility and cost of scaling up to a whole mouse brain connectome.