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Spatial transcriptomics analysis — Visium, MERFISH, seqFISH, Slide-seq. Maps gene expression to tissue architecture, identifies spatially variable genes (SVGs), tissue-domain segmentation, and cell-cell interaction inference. Use for spatial gene-expression questions, tissue architecture analysis, and SVG identification.
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.
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
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
Input: Spatial Transcriptomics Data + Tissue Image
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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
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Phase 2: Preprocessing
|-- Normalization (spatial-aware methods)
|-- Highly variable gene selection (top 2000)
|-- Dimensionality reduction (PCA)
|-- Spatial lag smoothing (optional)
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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
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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)
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Phase 5: Neighborhood Analysis
|-- Define spatial neighborhoods (k-NN, radius)
|-- Calculate neighborhood composition (squidpy nhood_enrichment)
|-- Identify interaction zones between domains
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Phase 6: Integration with scRNA-seq
|-- Cell type deconvolution (Cell2location, Tangram, SPOTlight)
|-- Map cell types to spatial locations
|-- Validate with marker genes
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Phase 7: Spatial Cell Communication
|-- Identify proximal cell type pairs
|-- Query ligand-receptor database (OmniPath)
|-- Score spatial interactions (squidpy ligrec)
|-- Map communication hotspots
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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. Key ToolUniverse tools:
OmniPath_get_ligand_receptor_interactions — 14,000+ L-R pairs from CellPhoneDB, CellChatDB, etc. Use partners param for specific genes.
OmniPath_get_intercell_roles — classify proteins as ligand/receptor/ECM. Use proteins param.
OmniPath_get_cell_communication_annotations — CellPhoneDB/CellChatDB pathway annotations. Use proteins param.
OmniPath_get_signaling_interactions — intracellular signaling downstream of receptors.
Score interactions by co-expression of L-R pairs in proximal cells. Map hotspots where interaction scores peak.
Phase 7.5: Data Discovery & Gene Context (ToolUniverse API tools)
For dataset discovery and gene annotation (API-based, no local computation needed):
DGIdb_get_drug_gene_interactions — druggable targets in the spatial context
PubMed_search_articles — literature for spatial biology context
API tools vs. local computation: Phases 1-2 (data import, QC) and Phases 3-6 (clustering, SVGs, neighborhoods, deconvolution) require local Python with squidpy/scanpy. Phase 7 L-R databases and Phase 7.5 gene context use ToolUniverse API tools.
tooluniverse-single-cell: scRNA-seq reference for deconvolution (Phase 6) and L-R database (Phase 7)
tooluniverse-gene-enrichment: Pathway enrichment for spatial domain marker genes (Phase 3)
tooluniverse-multi-omics-integration: Integration with other omics layers (Phase 8)
ToolUniverse Data Retrieval Tools
HuBMAP Spatial Atlas Tools
Use HuBMAP tools to discover published spatial biology datasets for reference, validation, or cross-study comparison.
Availability Note: HuBMAP_search_datasets, HuBMAP_list_organs, and HuBMAP_get_dataset may not be registered in your ToolUniverse instance. Verify with tu.list_tools() before use. If unavailable, use OmicsDI (OmicsDI_search_datasets(query="spatial transcriptomics kidney")) or CELLxGENE (CELLxGENE_get_cell_metadata) as reliable alternatives for spatial dataset discovery.
HuBMAP_search_datasets: Search published datasets by organ (code, e.g. "LK"=Left Kidney, "BR"=Brain), dataset_type, query, limit
HuBMAP_list_organs: List all organs with codes and UBERON IDs (no required params)
HuBMAP_get_dataset: Get detailed metadata for a specific hubmap_id (e.g. "HBM626.FHJD.938")
Below the dataset level, HuBMAP indexes the physical tissue Samples (anatomical blocks/sections/organs/suspensions) and the human Donors. Use these to inspect CCF/RUI spatial registration and donor demographics — context the dataset tools do not expose.
HuBMAP_search_samples: Find tissue Samples by organ (2-letter code), sample_category ("block"/"section"/"organ"/"suspension"), registered_only (only CCF/RUI-registered specimens), limit. Each result flags spatially_registered and links the parent donor.
HuBMAP_get_sample: Full record for one Sample hubmap_id (e.g. "HBM658.BXNB.873"), including parsed rui_location — the CCF placement_target reference organ, x/y/z dimensions + units, and ccf_annotations (UBERON/FMA structures the block overlaps).
HuBMAP_search_donors: Find Donors by group_name (e.g. "Stanford") or free-text query, with normalized demographics (age, sex, race, body_mass_index, cause_of_death) extracted from UMLS/SNOMED-coded metadata. Use to build demographically-matched tissue cohorts.
Finding reference spatial datasets for a given organ/tissue
Identifying available spatial assay types (Visium, CODEX, MERFISH) for a tissue
Cross-referencing donor metadata (age, sex) for spatial datasets
Retrieving DOI links for published spatial atlas datasets
Fallback if HuBMAP tools unavailable:
# Use OmicsDI for spatial dataset discovery
result = tu.tools.OmicsDI_search_datasets(query="spatial transcriptomics kidney Visium")
# Use CELLxGENE for cell-level expression context
result = tu.tools.CELLxGENE_get_cell_metadata(tissue="kidney")
# Example: Find spatial datasets for kidney (if HuBMAP tools available)
result = tu.tools.HuBMAP_search_datasets(organ="LK", limit=5)
# Returns: {data: {total, returned, datasets: [{hubmap_id, title, dataset_type, organ, doi_url, ...}]}}# Example: Get all available organs
organs = tu.tools.HuBMAP_list_organs()
# Returns: {data: {total, organs: [{code, term, organ_uberon, rui_supported}]}}
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
At least 500 spatial locations after QC
Filter low-quality spots (min 200 genes, min 500 UMI, <20% MT) and verify alignment
At least one spatial clustering method (graph-based with spatial constraints)
Spatially variable genes tested with Moran's I or equivalent (FDR < 0.05)
Spatial plots on tissue images for all major findings
Report covers: domains, spatial genes, cell type maps, key interactions
Reasoning Framework
Starting Point: What Is the Spatial Question?
Spatial data adds location to expression. The key question: is the spatial pattern driven by cell type composition (trivial) or by spatially-regulated gene expression within the same cell type (interesting)? Deconvolution helps distinguish these.
Before interpreting any spatially variable gene (SVG), ask:
Does this gene simply mark a cell type that is spatially restricted? (e.g., a T-cell marker enriched in immune infiltrate zones — expected, not informative)
Or is the gene differentially expressed within the same cell type depending on its spatial position? (e.g., a fibroblast gene upregulated at the tumor-stroma boundary — spatially regulated, biologically interesting)
To distinguish these: (a) run deconvolution (Cell2location, Tangram) to get cell type fractions per spot; (b) regress SVG expression against cell type fraction; (c) if the spatial pattern persists after controlling for cell type composition, it reflects genuine spatial regulation. Always look up the gene's known biology before interpreting — check UniProt function and STRING interactions rather than guessing.
Evidence Grading
T1: Validated by orthogonal method (IHC, smFISH, known anatomy) — e.g., spatial domain matches histology-confirmed tumor margin
T2: Statistically significant, biologically consistent — SVG with Moran's I > 0.3 and FDR < 0.01 in expected tissue region
T3: Computationally identified, awaiting validation — novel spatial domain from clustering with no histological correlate
T4: Exploratory or artifact-prone — low-UMI edge spots, domains driven by batch effects
Interpretation Guidance
Spatial domains: Domains represent regions of coherent gene expression, often corresponding to tissue architecture (tumor core, stroma, immune infiltrate, necrosis). A domain is biologically meaningful when its marker genes align with known cell type signatures. Domains at tissue boundaries (e.g., tumor-stroma interface) are particularly informative for microenvironment studies.
Cell-cell proximity significance: Neighborhood enrichment z-scores > 2 indicate cell types co-localize more than expected by chance. Negative z-scores indicate spatial avoidance. Interpret in biological context: immune cell enrichment near tumor cells may indicate active immune response or immunosuppressive niche depending on the cell types involved (e.g., CD8+ T cells vs. Tregs).
Spatially variable genes (SVGs): Moran's I > 0.3 with FDR < 0.05 indicates strong spatial patterning. Classify SVGs by pattern: gradients (morphogen signaling, e.g., WNT along crypt-villus axis), hotspots (focal expression in immune aggregates), boundary genes (enriched at domain interfaces, e.g., epithelial-mesenchymal transition markers). SVGs with known spatial biology roles (e.g., tissue polarity genes) are higher confidence than novel candidates.
Synthesis Questions
A complete spatial transcriptomics report should answer:
What spatial domains were identified, and do they correspond to known tissue architecture?
Which genes show significant spatial variability, and what pattern types do they exhibit?
Are specific cell type pairs enriched or depleted in spatial proximity?
What ligand-receptor interactions are active at domain boundaries or cell-cell interfaces?
How do spatial findings compare to bulk or single-cell data from the same tissue type?
Programmatic Access (Beyond Tools)
When ToolUniverse tools return metadata but you need the actual expression matrices:
import scanpy as sc, pandas as pd, requests, io
# Load h5ad (most common format for spatial/single-cell)
adata = sc.read_h5ad("spatial_data.h5ad")
# Load 10X Visium output directory
adata = sc.read_visium("path/to/spaceranger_output/")
# Download from GEO supplementary files
geo_id = "GSE123456"# Check for h5ad or MTX in supplementary files
url = f"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={geo_id}&targ=gsm&view=data"# Load 10X MTX format (matrix + barcodes + features)
adata = sc.read_10x_mtx("filtered_feature_bc_matrix/", var_names="gene_symbols")
# HuBMAP data portal# Search at https://portal.hubmapconsortium.org/search then download via globus or direct link# Human Cell Atlas: https://data.humancellatlas.org/ — download h5ad/loom files
See tooluniverse-data-wrangling skill for format cookbook and bulk download patterns.
