From synapses to genes through morphology: an integrated characterization of cell types based on connectomics and transcriptomics data

从突触到形态学的基因：基于连接组学和转录组学数据的细胞类型的综合表征

• 批准号: 10360840
• 负责人: Forrest Christie Collman
• 金额: $ 144.12万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10360840
• 关键词: Affect; Anatomy; BRAIN initiative; Back; Base of the Brain; Brain; Candidate Disease Gene; Catalogs; Cells; Cellular Morphology; Classification; Communities; Data; Data Reporting; Data Set; Descriptor; Disease; Electron Microscopy; Electrophysiology (science); Environment; Fostering; Gene Expression; Gene Expression Profile; Genes; Genetic; Goals; Gold; Grant; Individual; Institutes; Link; Maps; Measurement; Measures; Metadata; Methods; Mitochondria; Modality; Molecular; Morphology; Mus; Nature; Neurons; Pattern; Property; Public Domains; Research Personnel; Role; Shapes; Structure; Surveys; Synapses; System; Taxonomy; Time; Translating; Variant; Vertebral column; Visual Cortex; Work; analytical tool; area striata; base; brain cell; cell type; comparative; connectome; connectome data; data archive; data integration; data repository; density; experimental study; insight; large scale data; multimodality; neural circuit; patch sequencing; programs; reconstruction; relating to nervous system; tool; transcriptomics

Project Summary The goal of this project is to create a unified framework for understanding the relationship between neuronal gene expression and connectivity in mouse visual cortex, by using morphology as a key linking modality. There now exist publicly available large-scale data sets that measure both these modalities in mouse visual cortex. One dataset is a large set of Patch-seq experiments from single cells, which provide measurements of gene expression, electrophysiological properties and morphology for individual cells. A second dataset is from large scale Connectomics using electron microscopy, which provides neuronal morphology; fine-scale and detailed cellular and ultrastructural properties; and measurements of the connectivity between individual neurons. Our first aim is to analyze these two datasets in an explicitly integrative fashion, in order to build better classifiers of neuronal types, along with tools to translate type predictions between each data modality. Our second aim builds on the first, by using those tools to characterize cell-type specific connectivity of mouse visual cortex. This will allow us to describe how that connectivity relates to the likely molecular composition of individual cells and provide insight into which molecular distinctions drive differences in cell type specific connectivity patterns. Our third aim is to redistribute the results of our analysis back into publicly available data repositories and create tools that allow other researchers to query the gene expression, connectivity, electrophysiological and morphological descriptors of neurons in the datasets, as well as apply those same tools to their own data. Like a Rosetta stone for cell types, this will enable researchers using disparate methods to integrate their data with other modalities and foster a rich environment for understanding the role of cell types in brain function and disease.

项目摘要 这个项目的目标是创建一个统一的框架，以了解 小鼠视皮层神经元基因表达和连接，以形态学为关键 连接方式现在有公开可用的大规模数据集来衡量这两个方面 小鼠视觉皮层的模式。一个数据集是来自单个细胞的大量Patch-seq实验， 细胞，提供基因表达，电生理特性和 单个细胞的形态。第二个数据集来自使用电子的大规模Connectomics。 显微镜，提供神经元形态;精细尺度和详细的细胞和超微结构 属性;以及单个神经元之间的连接性的测量。我们的首要目标是 以明确的整合方式分析这两个数据集，以便构建更好的分类器 神经元类型，沿着工具在每个数据模态之间转换类型预测。我们的第二 aim建立在第一个基础上，通过使用这些工具来表征小鼠细胞类型特异性连接， 视觉皮层这将使我们能够描述这种连接性如何与可能的分子 单个细胞的组成，并提供洞察哪些分子差异驱动差异 在小区类型特定的连接模式中。我们的第三个目标是重新分配我们的分析结果 回到公开的数据库，并创建工具，让其他研究人员查询 神经元的基因表达、连接性、电生理和形态学描述符， 数据集，并将这些相同的工具应用于自己的数据。就像罗塞塔石碑的细胞类型， 这将使研究人员能够使用不同的方法将他们的数据与其他模式相结合， 为理解细胞类型在脑功能和疾病中的作用创造了丰富的环境。