Single-cell-level spatial gene expression in the embryonic neural differentiation niche.

Area of Science:

• Developmental Biology
• Neuroscience
• Computational Biology

Background:

• High-throughput in situ hybridization generates vast amounts of image data.
• Analyzing these high-resolution images for global patterns and hypotheses is challenging.

Purpose of the Study:

• To develop a semi-automated pipeline for analyzing in situ hybridization images at single-cell resolution.
• To investigate spatial gene expression profiles during mouse embryonic cerebral cortex development.

Main Methods:

• Developed a semi-automated image analysis pipeline.
• Analyzed in situ hybridization images of E14.5 mouse embryos from the Eurexpress database.
• Examined spatial gene expression for over 1600 telencephalon-expressed genes.

Main Results:

• Derived single-cell resolution spatial gene expression profiles across cortical layers.
• Observed high spatial modularity, recapitulated known differentiation zones, and identified novel transition zones/cellular states.
• Revealed a spatial transition phase for chromatin remodeling and identified a role for mitotic checkpoints.

Conclusions:

• The pipeline offers a novel approach for analyzing spatial gene expression dynamics and inferring regulatory events in vivo.
• The findings provide insights into key processes during cerebral cortex development, including chromatin remodeling and mitotic checkpoint roles.
• This high-resolution, single-cell level analysis is applicable to various systems for studying dynamic differentiation.