Loom: Multi-Region Analysis of Spatial Transcriptomics with Local Neighborhoods and Global Trajectories

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Spatial transcriptomics (ST) enables gene expression measurement within tissue sections, preserving spatial context.
• Integrating ST data with cell references and temporal simulations is crucial but challenging due to multi-modal registration and complex data patterns.

Purpose of the Study:

• To present Loom, a visual computing system designed for spatial transcriptomics analysis.
• To facilitate the exploration of pseudo-temporal trajectories, cross-sample comparisons, and spatiotemporal biological mechanisms within local microenvironments.

Main Methods:

• Loom utilizes a novel glyph and computational backbone for detailed pseudo-temporal exploration.
• The system integrates ST data with cell reference datasets and temporal simulations.
• Evaluation involved case studies with pathology experts and oncologists, plus an external usability study.

Main Results:

• Loom effectively supports the analysis of pseudo-temporal trajectories and comparative investigations across samples.
• The system aids in examining spatially structured processes within local microenvironments.
• Demonstrated effectiveness in discovering cellular transitions and spatiotemporal expression dynamics.

Conclusions:

• Loom is a valuable tool for advancing spatial transcriptomics research.
• The system enhances the understanding of cellular dynamics and gene expression in complex biological systems.
• Loom facilitates the discovery of spatiotemporal biological mechanisms crucial for tissue pathology and oncology research.