Integrating gene expression and imaging data across Visium capture areas with visiumStitched

Area of Science:

• Spatial transcriptomics
• Genomics
• Bioinformatics

Background:

• Visium (10x Genomics) is a key spatial transcriptomics assay.
• Standard capture areas limit tissue structure analysis.
• Existing software has limitations in merging multiple capture areas.

Purpose of the Study:

• To develop a method for combining multiple Visium capture areas.
• To enable analysis of larger tissue sections in spatial transcriptomics.
• To overcome limitations of current multi-capture area merging software.

Main Methods:

• Developed the R/Bioconductor package visiumStitched.
• Utilized Fiji (ImageJ) for image stitching.
• Constructed SpatialExperiment R objects with combined data.
• Created an artificial hexagonal array grid for analysis.

Main Results:

• Successfully stitched partially overlapping and adjacent Visium capture areas.
• visiumStitched enables seamless downstream analyses, including spatially-aware clustering.
• No data from overlapping spots were discarded.

Conclusions:

• visiumStitched offers a flexible framework for multi-capture area spatial transcriptomics studies.
• The package resolves data processing challenges without disrupting analysis workflows.
• Provides an accessible solution to expand multi-capture area study designs.