An extensible brain knowledge base and toolset spanning modalities for multi-species data-driven cell types

可扩展的大脑知识库和工具集，涵盖多物种数据驱动细胞类型的模式

• 批准号: 10686977
• 负责人: Satrajit Sujit Ghosh
• 金额: $ 213.47万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10686977
• 关键词: 3-Dimensional; Acceleration; Adult; Anatomy; Atlases; BRAIN initiative; Brain; Cells; Censuses; Classification; Communities; Data; Data Set; Derivation procedure; Development; Dimensions; Disease; Disparate; Ecosystem; Educational workshop; Event; Evolution; FAIR principles; Feedback; Fostering; Generations; Germ Cells; Goals; Graph; Health; Human; Information Resources; Infrastructure; Ingestion; Knowledge; Libraries; Link; Longevity; Machine Learning; Maps; Modality; Molecular; Morphology; Motor Cortex; Mus; Neurosciences; Ontology; Protocols documentation; Publications; Pythons; Research Personnel; Resources; Science; Scientist; Source; Standardization; Structure; Taxonomy; Training; Transgenic Organisms; Update; Viral; Visualization; Work; brain cell; cell type; community based participatory research; data archive; data integration; data modeling; data visualization; epigenomics; flexibility; improved; knowledge base; knowledge graph; knowledge integration; multimodal data; multimodality; nonhuman primate; programs; prototype; tool; transcriptomics; trustworthiness; web app

Project Summary BRAIN Initiative Cell Census Network (BICCN) is completing a comprehensive cell census of the adult mouse brain, and BRAIN Initiative Cell Atlas Network (BICAN) will extend this work with emphasis on human and non- human primates. Effectively organizing, summarizing, accessing, and refining these atlases is critical to maximizing their impact on science. This proposal is to develop an extensible Brain Cell Knowledge Base (BCKB) to ingest and standardize comprehensive cell type information from BICAN’s development of a multimodal, multi-species brain cell atlas and disseminate that atlas as an open and interactive community resource for advancing knowledge of the brain. The BCKB will be initialized during this project with multi- dimensional brain cell type classifications from BICCN and will expand as data and knowledge are produced by BICAN researchers. Under Aim 1, we will create an adaptive knowledge graph for linking brain cell information. Spatial aggregation will be done using common coordinate frameworks. A flexible graph-based data model will capture discrete and continuous cell type relationships. The work will start with cross-species MOp data and comprehensive whole- brain mouse datasets from BICCN and later extend into BICAN’s whole-brain molecular and spatial transcriptomics data in human and non-human primates as such data becomes available. Anchoring of taxonomies in single cell molecular and spatial transcriptomics provides a robust framework for integrating multimodal data that is spatially mapped and/or cell types mapped. An ecosystem of tools for curating, annotating, mapping, and visualization of cell type data will be created in Aim 2. We will build and extend tools, such as the initial Cell Types Cards showcasing BICCN’s MOp results, so BICAN teams and public labs can share and refine brain cell type taxonomies and anatomical integration. The tools framework developed for this aim will provide a central hub, akin to an “app store,” to access our tools and others from the community to interact with cell type data. This connected tools framework will streamline scientific workflows and encourage FAIR practices. As part of Aim 3, we will develop an infrastructure to link brain cell data and knowledge. This infrastructure will enable ingesting, storing, searching, and curating neuroscientific information from multiple sources into a linked information platform. This knowledge infrastructure will help connect disparate pieces of cell type information using expert annotations, machine-learning inferences, and derivations using provenance mechanisms. We will use Allen Institute’s Brain Knowledge Platform for initial implementation. Finally, in Aim 4 we will gather, curate, and integrate information and knowledge from BICAN teams by conducting annual hands- on training and feedback workshops. These events will create engagement within and outside BICAN projects and foster community-based evolution, sustainability, and governance.

项目摘要 脑启动细胞普查网络（BICCN）正在完成成年小鼠的全面细胞普查 大脑和大脑启动细胞图集网络（BICAN）将扩展这项工作，重点是人类和非人类和非 人类隐私。有效地组织，总结，访问和完善这些图书馆对 最大化他们对科学的影响。该建议是建立可扩展的脑细胞知识基础 （BCKB）从Bican开发的摄入和标准化全面的单元类型信息 多模式的多种物种脑细胞地图集，并将其作为一个开放式互动社区传播 促进大脑知识的资源。 BCKB将在此项目中初始化 BICCN的维度脑细胞类型分类，并将随着数据和知识而扩展 比肯研究人员。 在AIM 1下，我们将创建一个自适应知识图，以连接脑细胞信息。空间聚集 将使用通用坐标框架完成。灵活的基于图的数据模型将捕获离散和 连续的细胞类型关系。这项工作将从跨物种的拖把数据和全面的整体开始 BICCN的脑鼠标数据集，后来延伸到Bican的全脑分子和空间 作为此类数据的人类和非人类素数中的转录组学数据可用。锚定 单细胞分子和空间转录组学中的分类学为整合提供了强大的框架 在空间映射和/或单元格映射的多模式数据。一个用于策划工具的生态系统， 将在AIM 2中创建细胞类型数据的注释，映射和可视化。我们将构建和扩展工具， 例如最初的单元类型卡展示了BICCN的拖把结果，因此Bican团队和公共实验室可以 共享和完善脑细胞类型分类法和解剖整合。为此开发的工具框架 AIM将提供一个类似于“应用商店”的中央枢纽，以访问我们的工具以及从社区到 与单元类型数据相互作用。这种连接的工具框架将简化科学工作流程并鼓励 公平的做法。作为AIM 3的一部分，我们将开发一个基础架构，以将脑细胞数据和知识联系起来。这 基础架构将使来自多个的摄入，存储，搜索和策划神经科学信息 源用于链接的信息平台。这些知识基础设施将有助于连接不同的部分 使用专家注释，机器学习信息和派生的细胞类型信息 机制。我们将使用艾伦学院的大脑知识平台进行初步实施。最后，在目标4中 我们将通过每年的动手来收集，策划和整合BICAN团队的信息和知识。 关于培训和反馈研讨会。这些事件将在BICAN项目内部和外部创造参与度 以及基于社区的发展，可持续性和治理。

项目成果

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain.

• DOI: 10.1038/s41586-023-06812-z
• 发表时间: 2023-12
• 期刊: NATURE
• 影响因子: 64.8
• 作者: Yao, Zizhen;van Velthoven, Cindy T. J.;Kunst, Michael;Zhang, Meng;Mcmillen, Delissa;Lee, Changkyu;Jung, Won;Goldy, Jeff;Abdelhak, Aliya;Aitken, Matthew;Baker, Katherine;Baker, Pamela;Barkan, Eliza;Bertagnolli, Darren;Bhandiwad, Ashwin;Bielstein, Cameron;Bishwakarma, Prajal;Campos, Jazmin;Carey, Daniel;Casper, Tamara;Chakka, Anish Bhaswanth;Chakrabarty, Rushil;Chavan, Sakshi;Chen, Min;Clark, Michael;Close, Jennie;Crichton, Kirsten;Daniel, Scott;Divalentin, Peter;Dolbeare, Tim;Ellingwood, Lauren;Fiabane, Elysha;Fliss, Timothy;Gee, James;Gerstenberger, James;Glandon, Alexandra;Gloe, Jessica;Gould, Joshua;Gray, James;Guilford, Nathan;Guzman, Junitta;Hirschstein, Daniel;Ho, Windy;Hooper, Marcus;Huang, Mike;Hupp, Madie;Jin, Kelly;Kroll, Matthew;Lathia, Kanan;Leon, Arielle;Li, Su;Long, Brian;Madigan, Zach;Malloy, Jessica;Malone, Jocelin;Maltzer, Zoe;Martin, Naomi;Mccue, Rachel;Mcginty, Ryan;Mei, Nicholas;Melchor, Jose;Meyerdierks, Emma;Mollenkopf, Tyler;Moonsman, Skyler;Nguyen, Thuc Nghi;Otto, Sven;Pham, Trangthanh;Rimorin, Christine;Ruiz, Augustin;Sanchez, Raymond;Sawyer, Lane;Shapovalova, Nadiya;Shepard, Noah;Slaughterbeck, Cliff;Sulc, Josef;Tieu, Michael;Torkelson, Amy;Tung, Herman;Valera Cuevas, Nasmil;Vance, Shane;Wadhwani, Katherine;Ward, Katelyn;Levi, Boaz;Farrell, Colin;Young, Rob;Staats, Brian;Wang, Ming-Qiang Michael;Thompson, Carol L.;Mufti, Shoaib;Pagan, Chelsea M.;Kruse, Lauren;Dee, Nick;Sunkin, Susan M.;Esposito, Luke;Hawrylycz, Michael J.;Waters, Jack;Ng, Lydia;Smith, Kimberly;Tasic, Bosiljka;Zhuang, Xiaowei;Zeng, Hongkui
• 通讯作者: Zeng, Hongkui