Designing smart spatial omics experiments with S2Omics

Area of Science:

• Biomedical research
• Molecular biology
• Bioinformatics

Background:

• Spatial omics technologies offer high-resolution molecular profiling within native tissue architecture.
• Manual selection of regions of interest (ROIs) for spatial omics is subjective, inconsistent, and irreproducible.
• Histology images correlate with spatial molecular patterns, suggesting their utility for guiding ROI selection.

Purpose of the Study:

• To develop an automated workflow, S2Omics, for selecting ROIs from histology images.
• To maximize the molecular information content within selected ROIs for spatial omics experiments.
• To enhance the systematic, reproducible, and impactful nature of spatial omics-driven biological discovery.

Main Methods:

• S2Omics workflow integrates histology image analysis with spatial omics data.
• The system automatically identifies and selects ROIs based on predicted molecular information content.
• The approach was validated across diverse spatial omics platforms and tissue types.

Main Results:

• S2Omics enables automated and objective ROI selection, overcoming limitations of manual methods.
• The workflow demonstrated consistent performance across various experimental settings.
• Systematic ROI selection using S2Omics improved the robustness of downstream biological analyses.

Conclusions:

• S2Omics provides a powerful tool for optimizing experimental design in spatial omics.
• Automated ROI selection significantly enhances the reproducibility and discovery potential of spatial omics studies.
• This approach facilitates more efficient and impactful biomedical research using spatial omics data.