STAPLE: automating spatial transcriptomics analysis and AI interpretation

Area of Science:

• Computational Biology
• Bioinformatics
• Spatial Transcriptomics

Background:

• Current spatial transcriptomics workflows are fragmented across multiple tools.
• This fragmentation hinders scalability, interpretation, and reproducibility of analyses.
• Separate tools exist for cell typing, neighborhood analysis, and cell-cell communication.

Purpose of the Study:

• To systematize spatial transcriptomics analyses.
• To create a modular framework for integrating distinct analytical methods.
• To improve scalability, interpretation, and reproducibility in spatial transcriptomics.

Main Methods:

• Developed STAPLE, a modular framework for spatial transcriptomics.
• Unified data structures and ensured cross-tool interoperability.
• Implemented end-to-end analyses with a single invocation.
• Integrated a novel AI-enabled reporting layer for result synthesis.

Main Results:

• STAPLE systematizes analyses across distinct spatial transcriptomics tools.
• Achieved unified data structures and cross-tool interoperability.
• Enabled unassisted, end-to-end analyses for rigorous and reproducible results.
• AI reporting layer synthesizes quantitative results into biological summaries.

Conclusions:

• STAPLE fosters rigorous, reproducible spatial transcriptomics analysis.
• The framework facilitates interpretation of complex biological findings.
• Enhances scalability and integration of diverse spatial biology tools.