Spatial Transcriptomics in Cancer Research

An overview of spatial transcriptomics technologies and how they are reshaping our understanding of tumor architecture and the spatial organization of the tumor microenvironment.

Why Space Matters

Standard scRNA-seq dissociates tissues, losing spatial context. Spatial transcriptomics technologies preserve the physical location of gene expression, revealing how cells communicate within their native tissue architecture.

Technology Landscape

Platform	Resolution	Approach
10x Visium	~55 µm spots	Capture-based
MERFISH	Subcellular	Imaging-based
Slide-seq	~10 µm beads	Capture-based
CODEX	Single-cell (protein)	Imaging-based

Applications in Cancer

• Tumor-immune interface mapping — Identifying spatial patterns of immune infiltration and exclusion
• Tertiary lymphoid structures — Characterizing organized immune aggregates associated with immunotherapy response
• Invasion fronts — Transcriptional programs at the leading edge of tumor expansion
• Cell-cell communication — Ligand-receptor analysis with spatial constraints

Computational Challenges

Spatial data require specialized tools: Squidpy for spatial statistics, cell2location and DestVI for deconvolution, and NICHE-NET for spatially-aware cell communication inference.

Outlook

As spatial technologies achieve higher resolution and multiplexing, they will become essential for understanding treatment resistance and designing combination therapies.