tooluniverse-spatial-transcriptomics
Spatial Transcriptomics Analysis
Comprehensive analysis of spatially-resolved transcriptomics data to understand gene expression patterns in tissue architecture context. Combines expression profiling with spatial coordinates to reveal tissue organization, cell-cell interactions, and spatially variable genes.
When to Use This Skill
Triggers:
- User has spatial transcriptomics data (Visium, MERFISH, seqFISH, etc.)
- Questions about tissue architecture or spatial organization
- Spatial gene expression pattern analysis
- Cell-cell proximity or neighborhood analysis requests
- Tumor microenvironment spatial structure questions
- Integration of spatial with single-cell data
- Spatial domain identification
- Tissue morphology correlation with expression
Example Questions:
- "Analyze this 10x Visium dataset to identify spatial domains"
- "Which genes show spatially variable expression in this tissue?"
- "Map the tumor microenvironment spatial organization"
- "Find genes enriched at tissue boundaries"
- "Identify cell-cell interactions based on spatial proximity"
- "Integrate spatial transcriptomics with scRNA-seq annotations"
Core Capabilities
| Capability | Description |
|---|---|
| Data Import | 10x Visium, MERFISH, seqFISH, Slide-seq, STARmap, Xenium formats |
| Quality Control | Spot/cell QC, spatial alignment verification, tissue coverage |
| Normalization | Spatial-aware normalization accounting for tissue heterogeneity |
| Spatial Clustering | Identify spatial domains with similar expression profiles |
| Spatial Variable Genes | Find genes with non-random spatial patterns |
| Neighborhood Analysis | Cell-cell proximity, spatial neighborhoods, niche identification |
| Spatial Patterns | Gradients, boundaries, hotspots, expression waves |
| Integration | Merge with scRNA-seq for cell type mapping |
| Ligand-Receptor Spatial | Map cell communication in tissue context |
| Visualization | Spatial plots, heatmaps on tissue, 3D reconstruction |
Supported Platforms
| Platform | Resolution | Genes | Notes |
|---|---|---|---|
| 10x Visium | 55um spots (~50 cells/spot) | Genome-wide | Most common, includes H&E image |
| MERFISH/seqFISH | Single-cell | 100-10,000 (targeted) | Imaging-based, absolute coordinates |
| Slide-seq/V2 | 10um beads | Genome-wide | Higher resolution than Visium |
| Xenium | Single-cell, subcellular | 300+ (targeted) | 10x single-cell spatial |
Workflow Overview
Input: Spatial Transcriptomics Data + Tissue Image
|
v
Phase 1: Data Import & QC
|-- Load spatial coordinates + expression matrix
|-- Load tissue histology image
|-- Quality control per spot/cell (min 200 genes, 500 UMI, <20% MT)
|-- Align spatial coordinates to tissue
|
v
Phase 2: Preprocessing
|-- Normalization (spatial-aware methods)
|-- Highly variable gene selection (top 2000)
|-- Dimensionality reduction (PCA)
|-- Spatial lag smoothing (optional)
|
v
Phase 3: Spatial Clustering
|-- Build spatial neighbor graph (squidpy)
|-- Graph-based clustering with spatial constraints (Leiden)
|-- Annotate domains with marker genes (Wilcoxon)
|-- Visualize domains on tissue
|
v
Phase 4: Spatial Variable Genes
|-- Test spatial autocorrelation (Moran's I, Geary's C)
|-- Filter significant spatial genes (FDR < 0.05)
|-- Classify pattern types (gradient, hotspot, boundary, periodic)
|
v
Phase 5: Neighborhood Analysis
|-- Define spatial neighborhoods (k-NN, radius)
|-- Calculate neighborhood composition (squidpy nhood_enrichment)
|-- Identify interaction zones between domains
|
v
Phase 6: Integration with scRNA-seq
|-- Cell type deconvolution (Cell2location, Tangram, SPOTlight)
|-- Map cell types to spatial locations
|-- Validate with marker genes
|
v
Phase 7: Spatial Cell Communication
|-- Identify proximal cell type pairs
|-- Query ligand-receptor database (OmniPath)
|-- Score spatial interactions (squidpy ligrec)
|-- Map communication hotspots
|
v
Phase 8: Generate Spatial Report
|-- Tissue overview with domains
|-- Spatially variable genes
|-- Cell type spatial maps
|-- Interaction networks in tissue context
Phase Summaries
Phase 1: Data Import & QC
Load platform-specific data (scanpy read_visium for Visium). Apply QC filters: min 200 genes/spot, min 500 UMI/spot, max 20% mitochondrial. Verify spatial alignment with tissue image overlay.
Phase 2: Preprocessing
Normalize to median total counts, log-transform, select top 2000 HVGs. Optional spatial smoothing via neighbor averaging (useful for noisy data but blurs boundaries).
Phase 3: Spatial Clustering
PCA (50 components) followed by spatial neighbor graph construction (squidpy). Leiden clustering with spatial constraints yields spatial domains. Find domain markers via Wilcoxon rank-sum test.
Phase 4: Spatially Variable Genes
Moran's I statistic tests spatial autocorrelation: I > 0 = clustering, I ~ 0 = random, I < 0 = checkerboard. Filter by FDR < 0.05. Classify patterns as gradient, hotspot, boundary, or periodic.
Phase 5: Neighborhood Analysis
Neighborhood enrichment analysis (squidpy) tests whether cell types/domains are co-localized beyond random expectation. Identify interaction zones at domain boundaries using k-NN spatial graphs.
Phase 6: scRNA-seq Integration
Cell type deconvolution maps single-cell annotations to spatial spots. Methods: Cell2location (recommended for Visium), Tangram, SPOTlight. Produces cell type fraction estimates per spot.
Phase 7: Spatial Cell Communication
Combine spatial proximity with ligand-receptor databases (OmniPath). Score interactions by co-expression of L-R pairs in proximal cells. Map hotspots where interaction scores peak.
Phase 8: Report Generation
See report_template.md for full example output.
Integration with ToolUniverse Skills
| Skill | Used For | Phase |
|---|---|---|
tooluniverse-single-cell |
scRNA-seq reference for deconvolution | Phase 6 |
tooluniverse-single-cell (Phase 10) |
L-R database for communication | Phase 7 |
tooluniverse-gene-enrichment |
Pathway enrichment for spatial domains | Phase 3 |
tooluniverse-multi-omics-integration |
Integrate with other omics | Phase 8 |
Example Use Cases
Use Case 1: Tumor Microenvironment Mapping
Question: "Map the spatial organization of tumor, immune, and stromal cells" Workflow: Load Visium -> QC -> Spatial clustering -> Deconvolution -> Interaction zones -> L-R analysis -> Report
Use Case 2: Developmental Gradient Analysis
Question: "Identify spatial gene expression gradients in developing tissue" Workflow: Load spatial data -> SVG analysis -> Classify gradient patterns -> Map morphogens -> Correlate with cell fate -> Report
Use Case 3: Brain Region Identification
Question: "Automatically segment brain tissue into anatomical regions" Workflow: Load Visium brain -> High-resolution clustering -> Match to known regions -> Validate with Allen Brain Atlas -> Report
Quantified Minimums
| Component | Requirement |
|---|---|
| Spots/cells | At least 500 spatial locations |
| QC | Filter low-quality spots, verify alignment |
| Spatial clustering | At least one method (graph-based or spatial) |
| Spatial genes | Moran's I or similar spatial test |
| Visualization | Spatial plots on tissue images |
| Report | Domains, spatial genes, visualizations |
Limitations
- Resolution: Visium spots contain multiple cells (not single-cell)
- Gene coverage: Imaging methods have limited gene panels
- 3D structure: Most platforms are 2D sections
- Tissue quality: Requires well-preserved tissue for imaging
- Computational: Large datasets require significant memory
- Reference dependency: Deconvolution quality depends on scRNA-seq reference
References
Methods:
- Squidpy: https://doi.org/10.1038/s41592-021-01358-2
- Cell2location: https://doi.org/10.1038/s41587-021-01139-4
- SpatialDE: https://doi.org/10.1038/nmeth.4636
Platforms:
- 10x Visium: https://www.10xgenomics.com/products/spatial-gene-expression
- MERFISH: https://doi.org/10.1126/science.aaa6090
- Slide-seq: https://doi.org/10.1126/science.aaw1219
Reference Files
- code_examples.md - Python code for all phases (scanpy, squidpy, cell2location)
- report_template.md - Full example report (breast cancer TME)