Spatial omics context

Ground spatial transcriptomics in tissue morphology — pathology provides the WSI tissue context layer that makes Visium, Xenium, and MERFISH results interpretable in histological space.

Research question

Where do spatial gene expression signals originate relative to tissue structure, and how can morphology-guided regions connect molecular data to what pathologists see on the slide?

Who this is for

• Spatial biology labs running Visium or Xenium who need tissue morphology context
• Computational pathology groups linking segmented cell tables to deconvolved cell types
• Oncology and translational teams integrating histology with spatial omics in multi-modal studies

Data requirements

Data	Modality owner	Purpose
Whole-slide H&E or IHC	Pathology	Tissue structure, segmentation, region context
Visium/Xenium .h5ad	Computational Biology	Spot/cell expression, spatial domains, gene overlays
Aligned coordinates	Both	Mapping morphology to omics coordinates

Pathology does not duplicate compbio spatial omics ingestion or neighborhood analysis. Each area owns its layer; integration happens at the overlay and sample-key alignment level.

Workflow

Pathology: WSI → tiles → tissue detect → segment → quantify
Compbio: spatial .h5ad → spatial domains → spot viewer → DE/enrichment
Integration: align sample keys → compare region metrics with spatial clusters

Step 1 — Pathology tissue context

In a pathology study, process the H&E or IHC slide matched to the spatial sample:

1. Upload and tile the WSI
2. Run tissue detection for tumor/stroma region boundaries
3. Segment cells and run spatial quantification
4. Optionally import pathologist annotations for compartment labels

See Slide quantification and TME infiltration.

Step 2 — Compbio spatial analysis

In a linked compbio study, upload the spatial .h5ad and run:

1. Spatial domain clustering
2. Spatially variable gene identification
3. Gene expression overlays in Explore → Spatial

See Spatial transcriptomics.

Step 3 — Cross-modal interpretation

Connect findings across layers using shared sample identifiers:

Pathology output	Compbio output	Integrated question
Infiltration phenotype	Immune-high spatial domain	Does histological infiltration match immune-enriched spots?
Tumor/stroma annotations	Domain DE results	Are DEGs concentrated in tumor-labeled regions?
Cell density heatmap	Spot deconvolution fractions	Do segmented cell counts align with deconvolved immune proportions?
Region metrics CSV	Cluster marker genes	Which genes define morphology-distinct compartments?

Step 4 — Oncology overlay (optional)

The Oncology area adds clinical endpoint integration — survival, immune profiling, and mutation landscape — when spatial and pathology findings feed translational cohort analysis.

Expected outputs

• Pathology: tissue mask, cell overlay, density heatmap, region metrics
• Compbio: spatial domains, SVG rankings, spot viewer with gene overlays
• Cross-study: aligned sample keys and comparable region/cluster summaries for multi-modal reports

Planned extensions

• Tiled H&E image overlay in the compbio spatial viewer
• Direct morphology-to-expression alignment pipelines
• Spatial deconvolution mapped to segmented cell objects
• Platform-specific ingest (Xenium, MERFISH, CosMx) with pathology co-registration

See products/pathology/plan.md and products/pathology/product.md for the multimodal integration roadmap.

Related guides

• TME infiltration
• Computational Biology Spatial transcriptomics
• Oncology Multi-omic integration