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 BRAIN Initiative Cell Census Network（BICCN）正在完成对成年小鼠的全面细胞普查。 大脑和大脑倡议细胞图谱网络（BICAN）将扩展这项工作，重点是人类和非人类 人类灵长类动物有效地组织、总结、访问和精炼这些地图集对于 最大化他们对科学的影响。这个建议是开发一个可扩展的脑细胞知识库 （BCKB）从BICAN的开发中摄取和标准化全面的细胞类型信息， 多模式、多物种脑细胞图谱，并将该图谱作为一个开放和互动社区进行传播 促进大脑知识的资源。BCKB将在本项目期间初始化， BICCN的三维脑细胞类型分类，并将随着数据和知识的产生而扩展， BICAN研究人员。 在目标1下，我们将创建一个自适应知识图，用于连接脑细胞信息。空间聚集 将使用共同的协调框架。一个灵活的基于图形的数据模型将捕获离散的， 连续的细胞类型关系。这项工作将从跨物种MOp数据和全面的整体- 来自BICCN的脑小鼠数据集，后来扩展到BICAN的全脑分子和空间 人类和非人类灵长类动物的转录组学数据，因为这样的数据变得可用。锚定 单细胞分子和空间转录组学中的分类学提供了整合 被空间映射和/或单元类型映射的多模态数据。一个策展工具的生态系统， Aim 2中将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， 例如展示BICCN MOp结果的初始细胞类型卡，因此BICAN团队和公共实验室可以 分享和完善脑细胞类型分类和解剖整合。为此开发的工具框架 aim将提供一个类似于“应用程序商店”的中心枢纽，以访问我们的工具和社区中的其他工具， 与细胞类型数据交互。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。作为目标3的一部分，我们将开发一个基础设施，将脑细胞数据和知识联系起来。这 基础设施将使人们能够从多个神经系统中获取、存储、搜索和管理神经科学信息。 将资源整合到一个信息平台上。这一知识基础设施将有助于将不同的 使用专家注释的细胞类型信息、机器学习推断和使用来源的推导 机制等我们将使用艾伦研究所的大脑知识平台进行初步实施。最后，目标4 我们将收集，策划和整合来自BICAN团队的信息和知识，通过每年的手- 培训和反馈研讨会。这些活动将在BICAN项目内外创造参与度 并促进以社区为基础的发展、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