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)