Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Area of Science:

• Spatial transcriptomics
• Computational biology
• Systems biology

Background:

• Analyzing complex biological systems requires tracking cellular dynamics across conditions, time, and space.
• Integrating multi-sample spatial transcriptomics data to explore cellular heterogeneity is a significant challenge.

Purpose of the Study:

• To present Stereopy, a flexible framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics.
• To provide researchers with enhanced data interpretation abilities for multi-sample spatial transcriptomics data.

Main Methods:

• Development of a universal container, scope controller, and integrative transformer for multi-sample multimodal data.
• Implementation of Stereopy for modeling comparative and spatiotemporal patterns.
• Interactive data visualization for exploring cellular heterogeneity.

Main Results:

• Stereopy enables investigation of cell communities and genes in pathological changes.
• The framework facilitates detection of spatiotemporal gene patterns considering spatial and temporal features.
• Stereopy allows inference of 3D niche-based cell-gene interaction networks, bridging intercellular and intracellular regulations.

Conclusions:

• Stereopy is a comprehensive bioinformatics toolbox and extensible framework for multi-sample spatial transcriptomics.
• It empowers researchers with enhanced data interpretation and novel perspectives for data mining.
• The framework addresses the challenge of integrating multi-sample data for exploring cellular heterogeneity.