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

点击翻译按钮获取中文摘要

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.