Establishing Common Coordinate Framework for Quantitative Cell Census in Developing Mouse Brains

建立小鼠大脑发育中定量细胞普查的通用坐标框架

• 批准号: 10088508
• 负责人: JAMES C GEE
• 金额: $ 379.79万
• 依托单位: PENNSYLVANIA STATE UNIV HERSHEY MED CTR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10088508
• 关键词: 3-Dimensional; Acute; Address; Adopted; Adult; Age; Anatomy; Animal Model; Apoptosis; Architecture; Atlases; BRAIN initiative; Birth; Brain; Brain Diseases; Brain Mapping; Brain region; C57BL/6 Mouse; Cell Density; Cell Nucleus; Cells; Censuses; Cessation of life; Communities; Complex; Computer Analysis; Coupled; Cre driver; Data; Development; Disease; Embryo; Embryology; Fluorescence Microscopy; Gene Expression; Genetic; Goals; Histologic; Image; Imaging technology; In Situ; Knowledge; Label; Lectin; Light; Link; Location; Magnetic Resonance Imaging; Maps; Methods; Microscopy; Modernization; Morphology; Mus; Neuroanatomy; Neurons; Neurosciences; Ontology; Parvalbumins; Pattern; Quantitative Evaluations; Reporter; Reproducibility; Resolution; Resources; Scientist; Somatostatin; Stains; Technology; Three-Dimensional Image; Three-Dimensional Imaging; Time; Tissues; Training; Vasoactive Intestinal Peptide; Visualization; analysis pipeline; base; brain cell; cell type; data integration; data resource; data sharing; improved; indexing; microscopic imaging; model development; mouse model; multimodality; myelination; neural circuit; neurodevelopment; neuroinformatics; novel; postnatal; programs; skills; spatiotemporal; three dimensional structure

Abstract Brain development is characterized by a diverse set of cell types that are born and connected into rapidly growing complex 3D structures across time. Quantitative understanding of cell type composition and distribution in different brain regions provides fundamental knowledge about the building blocks of the brain and serves as an essential baseline with which to assess changes that may occur in brain disorders. The importance of this information is reflected by the significant effort among the neuroscience community, including the creation of the BRAIN Initiative Cell Census Network, to improve our understanding of cell type compositions across different brain regions in the adult mouse brain. These efforts have been made possible and accelerated by technological advances in high-resolution 3D imaging coupled with computational analysis methods that can reveal cell type arrangement in the brain with unprecedented detail. For example, we developed a quantitative brain mapping method to uncover the spatial arrangement of GABAergic neuron subtypes in the adult mouse brain. For the adult mouse brain, the Allen Common Coordinate Framework (CCF) currently serves as the standard atlas resource with which to map and integrate results from different studies. The neuroscience community, on the other hand, does not have similar CCFs for the developing mouse brain. The lack of developmental CCFs significantly hinders progress on cell type mapping of the developing mouse brain by limiting the reproducibility and integration of data from different studies. To address this deficiency, we have assembled a highly synergistic, multi-institutional team with complementary skill sets to create developmental CCFs with associated ontology and true 3D anatomical labels while also demonstrating the application of our CCFs by generating quantitative mappings of GABAergic neurons in the developing mouse brain. Toward this end, we will first utilize MRI and light sheet fluorescent microscopy (LSFM) to develop high-resolution developmental CCFs at seven different developmental time points (E11.5, E13.5, E15.5, E18.5, P4, P14, and P56) with different cellular features, including total cell density, myelination, and neurovasculature. Second, we will create true 3D anatomical labels for the CCFs based on cellular and gene expression information, and build a comprehensive ontology that will allow anatomical region changes to be linked across development and maturation. Lastly, we will generate a cellular-resolution quantitative map of GABAergic neuronal subtypes using tissue clearing and LSFM imaging in developing mouse brains, which will serve as a substantial data resource to accelerate developmental neuroscience discovery. The successful completion of this project will enable a broad field of scientists to leverage modern brain mapping technologies more effectively in studying the developing mouse brain.

抽象的 大脑发育的特征是多种细胞类型，这些细胞类型出生并连接为快速生长 跨时间的复杂3D结构。对细胞类型组成和分布的定量理解 不同的大脑区域提供了有关大脑组成部分的基本知识，并充当 要评估可能发生的变化可能发生的变化的基本基线。这一点的重要性 神经科学界的巨大努力反映了信息，包括创建 大脑启动细胞普查网络，以提高我们对不同不同细胞类型组成的理解 成年小鼠大脑中的大脑区域。这些努力已经成为可能并通过技术加速 高分辨率3D成像的进步以及可以揭示细胞类型的计算分析方法 在大脑中以空前的细节进行排列。例如，我们开发了定量的大脑映射 揭示成年小鼠脑中GABA能神经元亚型的空间排列的方法。为了 成年小鼠大脑，艾伦公共坐标框架（CCF）当前是标准地图集 绘制和整合不同研究的结果的资源。神经科学社区， 另一方面，对于发育中的小鼠大脑没有类似的CCF。缺乏发展CCF 通过限制可重复性，显着阻碍了发育中的小鼠大脑的细胞类型映射的进展 以及来自不同研究的数据的整合。为了解决这一缺陷，我们组装了高度协同作用， 具有互补技能的多机构团队，以创建具有相关本体论的发展性CCF 和真实的3D解剖标签，同时还通过生成定量来证明我们的CCF的应用 发育中的小鼠大脑中GABA能神经元的映射。为此，我们将首先使用MRI和 光片荧光显微镜（LSFM）以七种不同的方式发展高分辨率的发育CCF 发育时间点（E11.5，E13.5，E15.5，E18.5，P4，P14和P56），具有不同的蜂窝特征， 包括总细胞密度，髓鞘形成和神经血管系统。其次，我们将创建真实的3D解剖标签 用于基于细胞和基因表达信息的CCF，并建立一个综合本体论 允许在开发和成熟过程中将解剖区域变化联系起来。最后，我们将生成一个 使用组织清除和LSFM成像的GABA能神经元亚型的细胞分辨率定量图 开发老鼠大脑，该大脑将作为加速发展的大量数据资源 神经科学发现。该项目的成功完成将使一大批科学家能够 在研究发育中的小鼠大脑方面，更有效地利用现代的大脑图技术。