Spotiphy enables single-cell spatial whole transcriptomics across an entire section.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Spatial transcriptomics (ST) enables gene expression visualization in tissues.
• Current ST methods struggle to achieve single-cell resolution while maintaining whole-genome coverage.

Purpose of the Study:

• To introduce Spotiphy, a computational toolkit for transforming ST data into single-cell-resolved whole-transcriptome images.
• To enable precise cellular proportion analysis and visualization of gene expression at single-cell resolution within tissue sections.

Main Methods:

• Development of Spotiphy, a computational toolkit.
• Transformation of sequencing-based ST data into single-cell-resolved whole-transcriptome images.
• Benchmarking of Spotiphy against existing methods for cellular proportion accuracy.

Main Results:

• Spotiphy achieves the most precise cellular proportions in benchmarking evaluations.
• Inferred single-cell profiles from Spotiphy reveal regional astrocyte and microglia specifications in mouse brains.
• Spotiphy identifies spatial domains and tumor-microenvironment interactions in human breast ST data.

Conclusions:

• Spotiphy bridges the resolution gap in spatial transcriptomics, enabling single-cell level analysis.
• The toolkit offers an innovative pipeline for visualizing cell localization and transcriptomic profiles in complex biological systems.
• Spotiphy enhances the exploration of tissue regionalization and cellular interactions in various disease contexts.