A Community Framework for Data-driven Brain Transcriptomic Cell Type Definition, Ontology, and Nomenclature

数据驱动的脑转录组细胞类型定义、本体论和命名法的社区框架

• 批准号: 10012886
• 负责人: Michael Hawrylycz
• 金额: $ 277.04万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-04 至 2023-09-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10012886
• 关键词: Adopted; Adoption; Anatomy; Atlases; BRAIN initiative; Base of the Brain; Brain; Cell Nucleus; Cells; Censuses; Characteristics; Classification; Code; Communities; Complex; Computational Biology; Data; Data Set; Development; Disease; Educational workshop; Electrophysiology (science); Ensure; Evaluation; Feedback; Foundations; Genes; Genomics; Goals; Group Structure; Human; Human BioMolecular Atlas Program; Human body; Individual; Informatics; International; Investments; Link; Location; Maps; Measures; Metadata; Methods; Modality; Modeling; Monkeys; Morphology; Mus; Names; Neurobiology; Neuroglia; Neurons; Neurosciences; Nomenclature; Ontology; Organ; Periodicity; Phenotype; Recommendation; Reference Standards; Research; Research Personnel; Resources; Seminal; Shapes; Standardization; Taxonomy; Time; Tissues; Transcript; United States National Institutes of Health; Variant; Vocabulary; Work; base; brain cell; cell type; data framework; design; electrical property; epigenomics; human reference genome; member; neuroinformatics; ontology development; prototype; reference genome; structured data; symposium; tool; transcriptome; transcriptome sequencing; transcriptomics; working group

Project Summary: A Data-driven Framework for Brain Transcriptomic Cell Type Definition, Ontology, and Nomenclature Defining the complete census of neuronal and non-neuronal cell types in the brain is a major priority for the NIH BRAIN Initiative, since cellular complexity is a major barrier to understanding brain function and the mechanistic underpinnings of disease. Single cell transcriptomics has revolutionized this field with the scale and information content to characterize complex tissues, and is leading quickly to a brain-wide classification of cell types in mouse, monkey and human. Transcriptomics is also uniquely suited to allow quantitative comparisons across species, across developmental time, and between brain and other organs, and is the common denominator with other large-scale efforts to characterize the entire human body in the Human Cell Atlas and HuBMAP consortia. The opportunity is now here to create a new quantitative framework for defining cell types in the brain, generating a new data-driven cell type ontology and a nomenclature convention similar in concept to the reference genomes that unify genomics. Importantly, the design principles should be extensible beyond brain to other organs so that the schema can be adopted across the other major consortium projects, but also to incorporate other important cellular phenotypes important for neurobiological function. The proposed project aims to bring together a team of experts in single-cell transcriptomics, informatics, ontology development and computational biology who are also leaders and members of the major cell type consortia to develop a data-driven framework of brain cell types. First, the project aims to develop standards for quantitative definitions of transcriptomic-based cell types from the BRAIN Initiative Cell Census Network (BICCN) datasets, and tools for mapping other datasets (other data types or data from other researchers) to this reference. This will create reference data structures for features of transcriptomic cell types and taxonomies that will be deployed through the BICCN portal. Secondly, the project aims to build on prior work on developing cell and phenotype ontologies to develop a new, data-driven formal cell ontology for the whole brain reference. Part of this ontology is a nomenclature convention for systematic naming of cell types that allows similar naming of homologous cell types across species. Finally, a major goal is to engage the international cell type community in developing and refining these standards and reference classification to ensure their usefulness and widespread adoption. This will involve active engagement of the community through a working group structure, and periodic domain expert workshops with the BICCN, HCA, HuBMAP and INCF consortia. All standards, ontologies and tools will be deployed on the BICCN portal with mechanisms for community feedback and vetting.

项目摘要：一个数据驱动的脑转录组细胞类型定义，本体论和 命名法 定义大脑中神经元和非神经元细胞类型的完整普查是NIH BRAIN的主要优先事项 主动性，因为细胞的复杂性是理解大脑功能和机制基础的主要障碍 疾病。单细胞转录组学已经彻底改变了这一领域的规模和信息内容， 描述复杂组织的特征，并迅速导致小鼠，猴子和 人类转录组学也是唯一适合允许跨物种，跨发育的定量比较。 时间，大脑和其他器官之间，是与其他大规模努力的共同点， 在Human Cell Atlas和HuBMAP联合体中描述整个人体的特征。现在有机会 创建一个新的定量框架来定义大脑中的细胞类型，生成一个新的数据驱动的细胞类型本体 以及在概念上类似于统一基因组学的参考基因组的命名惯例。重要的是 设计原则应该是可扩展的，超越大脑到其他器官，以便该模式可以在其他器官中采用。 主要的联盟项目，而且还纳入其他重要的细胞表型重要的神经生物学 功能拟议的项目旨在汇集一个单细胞转录组学，信息学， 本体开发和计算生物学，他们也是主要细胞类型联盟的领导者和成员， 开发脑细胞类型的数据驱动框架。首先，该项目旨在制定量化标准， 来自BRAIN Initiative Cell Census Network（BICCN）数据集和工具的基于转录组学的细胞类型定义 用于将其他数据集（其他数据类型或来自其他研究人员的数据）映射到该参考。这将创建 转录组细胞类型和分类学特征的参考数据结构， BICCN门户网站。其次，该项目旨在建立在先前的工作，发展细胞和表型本体， 一个新的，数据驱动的正式细胞本体论的全脑参考。这个本体的一部分是一个命名法 细胞类型的系统命名惯例，允许跨物种的同源细胞类型的类似命名。 最后，一个主要目标是让国际细胞类型社区参与制定和完善这些标准， 参考分类，以确保其实用性和广泛采用。这将涉及积极参与 通过工作组结构和与BICCN、HCA、HuBMAP定期举办的领域专家研讨会， 和INCF财团。所有标准、本体和工具都将部署在BICCN门户网站上， 社区反馈和审查。