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同一性的分子和解剖学特征联系起来 摘要 大脑包含不同类型的细胞，这些细胞的特征特性和功能在复合体中差异很大， 互连电路。对大脑中细胞特性的完整定义需要将两者结合在一起 分子和解剖学特性，包括基因表达、表观遗传调节、空间位置和 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和分子特征联系起来的能力 严重限制了列举大脑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绕过这些挑战并构建 脑细胞类型的多模式图谱。空间转录测量独一无二的桥梁成像和 分子模式，提供了推断由单细胞定义的细胞类型的解剖学性质的机会 转录组和表观基因组数据。全脑空间转录数据和投影的存在 艾伦研究所收集的数据提高了将分离的单细胞数据映射到 空间，从而将神经元的分子和解剖学特征联系起来。我们最近开发了Liger，一种算法 它可以集成多种类型的单细胞数据来定义脑细胞类型并恢复 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 数据作为连接不同分子和解剖学数据的桥梁。具体来说，我们将：(1)将Liger应用于联合 根据单细胞转录和表观基因组数据确定小鼠脑细胞类型；(2)绘制这个分离的细胞图 使用来自艾伦小鼠脑图谱的原位杂交数据作为桥梁的空间数据；以及(3)推断细胞- 根据公共坐标框架内的空间位置键入特定的投影图案。我们的工作 将现有数据和计算方法结合在一个创新的分析策略中，以弥补差距 分子和解剖学数据之间的联系，代表着向列举大脑的 组件单元类型和电路。

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.