Linking molecular and anatomical features of brain cell identity through computational data integration

通过计算数据集成将脑细胞身份的分子和解剖特征联系起来

• 批准号: 10009608
• 负责人: Joshua Welch
• 金额: $ 112.13万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10009608
• 关键词: Algorithms; Anatomy; Archives; Atlases; Axon; BRAIN initiative; Brain; Cells; Cellular Structures; Characteristics; Chromatin; Complex; DNA Methylation; Data; Data Set; Gene Expression; Goals; Image; In Situ Hybridization; Individual; Institutes; Link; Location; Maps; Measurement; Measures; Modality; Molecular; Motor Cortex; Mus; Neurons; Neurosciences; Online Systems; Pattern; Positioning Attribute; Probability; Property; System; Testing; Work; base; brain cell; cell type; data integration; epigenetic regulation; epigenome; epigenomics; excitatory neuron; innovation; multimodality; multiple omics; nervous system disorder; relating to nervous system; single-cell RNA sequencing; three-dimensional visualization; transcriptome; transcriptomics

Linking molecular and anatomical features of brain cell identity through computational data integration Abstract The brain contains diverse cell types that vary widely in characteristic properties and function in complex, interconnected circuits. A complete definition of cellular identity in the brain requires incorporating both molecular and anatomical properties, including gene expression, epigenetic regulation, spatial position, and axonal projection patterns. However, existing experimental approaches cannot measure all of these features simultaneously within individual cells. This inability to link anatomical and molecular features of cellular identity severely limits efforts to enumerate the brain’s component cell types and assemble them into a circuit diagram. Computational integration of existing data provides a way to circumvent these challenges and construct a multi-modal atlas of brain cell types. Spatial transcriptomic measurements uniquely bridge imaging and molecular modalities, offering an opportunity to infer anatomical properties of cell types defined from single-cell transcriptome and epigenome data. The existence of whole-brain spatial transcriptomic data and projection data collected by the Allen Institute raises the exciting possibility of mapping dissociated single-cell data into space, thus linking molecular and anatomical features of neurons. We recently developed LIGER, an algorithm that can integrate multiple types of single-cell data to define brain cell types and recover the spatial positions of dissociated cells1. Here, we will leverage our computational integration framework and use existing spatial transcriptomic data as a bridge to link disparate molecular and anatomical data. Specifically, we will: (1) apply LIGER to jointly define mouse brain cell types from single-cell transcriptomic and epigenomic data; (2) map this dissociated cell data in space using in situ hybridization data from the Allen Mouse Brain Atlas as a bridge; and (3) infer cell- type specific projection patterns based on spatial position within a common coordinate framework. Our work combines existing data and computational approaches in an innovative analysis strategy to bridge the gap between molecular and anatomical data and represents an important step toward enumerating the brain’s component cell types and circuits.

通过计算数据整合链接脑细胞身份的分子和解剖学特征 摘要 大脑包含不同的细胞类型，这些细胞类型在复杂的特性和功能方面差异很大， 互联电路大脑中细胞身份的完整定义需要将两者结合起来 分子和解剖学特性，包括基因表达，表观遗传调控，空间位置， 轴突投射模式然而，现有的实验方法不能测量所有这些特征 同时在单个细胞内。这种无法将细胞身份的解剖学和分子特征联系起来的能力 严重限制了列举大脑的组成细胞类型并将它们组装成电路图的努力。 现有数据的计算集成提供了一种规避这些挑战并构建 脑细胞类型的多模态图谱。空间转录组学测量独特地桥接成像和 分子模式，提供了一个机会，推断从单细胞定义的细胞类型的解剖学特性 转录组和表观基因组数据。全脑空间转录组数据的存在和投影 艾伦研究所收集的数据提出了将解离的单细胞数据映射到 空间，从而将神经元的分子和解剖特征联系起来。我们最近开发了LIGER算法， 它可以整合多种类型的单细胞数据来定义脑细胞类型，并恢复脑细胞的空间位置。 分离的细胞1. 在这里，我们将利用我们的计算集成框架，并使用现有的空间转录组学 数据作为桥梁连接不同的分子和解剖数据。具体而言，我们将：（1）应用LIGER联合 从单细胞转录组学和表观基因组学数据定义小鼠脑细胞类型;（2）绘制该解离细胞的图谱 使用来自艾伦小鼠脑图谱的原位杂交数据作为桥梁，在空间中获得数据;以及（3）推断细胞- 基于公共坐标框架内的空间位置的类型特定投影图案。我们的工作 将现有的数据和计算方法结合在一个创新的分析策略中，以弥合差距 分子和解剖学数据之间的联系，代表了枚举大脑的重要一步。 元件细胞类型和电路。