tooluniverse-spatial-omics-analysis

name

tooluniverse-spatial-omics-analysis

description

Computational analysis framework for spatial multi-omics data integration. Given spatially variable genes (SVGs), spatial domain annotations, tissue type, and disease context from spatial transcriptomics/proteomics experiments (10x Visium, MERFISH, DBiTplus, SLIDE-seq, etc.), performs comprehensive biological interpretation including pathway enrichment, cell-cell interaction inference, druggable target identification, immune microenvironment characterization, and multi-modal integration. Produces a detailed markdown report with Spatial Omics Integration Score (0-100), domain-by-domain characterization, and validation recommendations. Uses 70+ ToolUniverse tools across 9 analysis phases. Use when users ask about spatial transcriptomics analysis, spatial omics interpretation, tissue heterogeneity, spatial gene expression patterns, tumor microenvironment mapping, tissue zonation, or cell-cell communication from spatial data.

Spatial Multi-Omics Analysis Pipeline

Comprehensive biological interpretation of spatial omics data. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into actionable biological insights covering pathway enrichment, cell-cell interactions, druggable targets, immune microenvironment, and multi-modal integration.

KEY PRINCIPLES:

1. Report-first approach - Create report file FIRST, then populate progressively
2. Domain-by-domain analysis - Characterize each spatial region independently before comparison
3. Gene-list-centric - Analyze user-provided SVGs and marker genes with ToolUniverse databases
4. Biological interpretation - Go beyond statistics to explain biological meaning of spatial patterns
5. Disease focus - Emphasize disease mechanisms and therapeutic opportunities when disease context is provided
6. Evidence grading - Grade all evidence as T1 (human/clinical) to T4 (computational)
7. Multi-modal thinking - Integrate RNA, protein, and metabolite information when available
8. Validation guidance - Suggest experimental validation approaches for key findings
9. Source references - Every statement must cite tool/database source
10. Completeness checklist - Mandatory section showing analysis coverage
11. English-first queries - Always use English terms in tool calls. Respond in user's language

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When to Use This Skill

Apply when users:

• Provide spatially variable genes from spatial transcriptomics experiments
• Ask about biological interpretation of spatial domains/clusters
• Need pathway enrichment analysis of spatial gene expression data
• Want to understand cell-cell interactions from spatial data
• Ask about tumor microenvironment heterogeneity from spatial omics
• Need druggable targets in specific spatial regions
• Ask about tissue zonation patterns (liver, brain, kidney)
• Want to integrate spatial transcriptomics + proteomics data
• Ask about immune infiltration patterns from spatial data
• Need to compare healthy vs disease regions spatially
• Ask "What pathways are enriched in this tumor core vs tumor margin?"
• Ask "What cell-cell interactions occur in this spatial domain?"

NOT for (use other skills instead):

• Single gene interpretation without spatial context -> Use tooluniverse-target-research
• Variant interpretation -> Use tooluniverse-variant-interpretation
• Drug safety profiling -> Use tooluniverse-adverse-event-detection
• Disease-only analysis without spatial data -> Use tooluniverse-multiomic-disease-characterization
• GWAS analysis -> Use tooluniverse-gwas-* skills
• Bulk RNA-seq (non-spatial) -> Use tooluniverse-systems-biology

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Input Parameters

Parameter	Required	Description	Example
svgs	Yes	Spatially variable genes (gene symbols)	['EGFR', 'CDH1', 'VIM', 'MYC', 'CD3E']
tissue_type	Yes	Tissue/organ type	brain, liver, lung, breast, skin
technology	No	Spatial omics platform used	10x Visium, MERFISH, DBiTplus, SLIDE-seq
disease_context	No	Disease if applicable	breast cancer, Alzheimer disease, liver cirrhosis
spatial_domains	No	Dict mapping domain name to marker genes	{'Tumor core': ['MYC','EGFR'], 'Stroma': ['VIM','COL1A1']}
cell_types	No	Cell types identified in deconvolution	['Epithelial', 'T cell', 'Macrophage', 'Fibroblast']
proteins	No	Proteins detected (if multi-modal)	['CD3', 'CD8', 'PD-L1', 'Ki67']
metabolites	No	Metabolites detected (if SpatialMETA)	['glutamine', 'lactate', 'ATP']

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Spatial Omics Integration Score (0-100)

Score Components

Data Completeness (0-30 points):

• SVGs provided (>10 genes): 5 points
• Disease context provided: 5 points
• Spatial domains defined: 5 points
• Cell type composition available: 5 points
• Multi-modal data (protein/metabolite): 5 points
• Literature context found: 5 points

Biological Insight (0-40 points):

• Significant pathway enrichment (FDR < 0.05): 10 points
• Cell-cell interaction predictions: 10 points
• Disease mechanism identified: 10 points
• Druggable targets found in disease regions: 10 points

Evidence Quality (0-30 points):

• Cross-database validation (gene found in 3+ databases): 10 points
• Clinical validation (approved drugs for spatial targets): 10 points
• Literature support (PubMed evidence for spatial patterns): 10 points

Score Interpretation

Score	Tier	Interpretation
80-100	Excellent	Comprehensive spatial characterization, strong biological insights, druggable targets identified
60-79	Good	Good pathway and interaction analysis, some disease/therapeutic context
40-59	Moderate	Basic enrichment complete, limited spatial domain comparison or interaction analysis
0-39	Limited	Minimal data, gene-level annotation only

Evidence Grading System

Tier	Symbol	Criteria	Examples
T1	[T1]	Direct human evidence, clinical proof	FDA-approved drug for spatial target, validated biomarker
T2	[T2]	Experimental evidence	Validated spatial pattern in literature, known ligand-receptor pair
T3	[T3]	Computational/database evidence	PPI network prediction, pathway enrichment, expression correlation
T4	[T4]	Annotation/prediction only	GO annotation, text-mined association, predicted interaction

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Report Template

Create this file structure at the start: {tissue}_{disease}_spatial_omics_report.md

# Spatial Multi-Omics Analysis Report: {Tissue Type}

**Report Generated**: {date}
**Technology**: {platform}
**Tissue**: {tissue_type}
**Disease Context**: {disease or "Normal tissue"}
**Total SVGs Analyzed**: {count}
**Spatial Domains**: {count}
**Spatial Omics Integration Score**: (to be calculated)

---

## Executive Summary

(2-3 sentence synthesis of key spatial findings - fill after all phases complete)

---

## 1. Tissue & Disease Context

### Tissue Information
| Property | Value | Source |
|----------|-------|--------|
| Tissue type | | |
| Disease | | |
| Expected cell types | | HPA |

### Disease Identifiers (if applicable)
| System | ID | Source |
|--------|-----|--------|

**Sources**: (tools used)

---

## 2. Spatially Variable Gene Characterization

### 2.1 Gene ID Resolution
| Gene Symbol | Ensembl ID | Entrez ID | UniProt | Function | Source |
|-------------|------------|-----------|---------|----------|--------|

### 2.2 Tissue Expression Patterns
| Gene | Tissue Expression | Specificity | Source |
|------|-------------------|-------------|--------|

### 2.3 Subcellular Localization
| Gene | Location | Confidence | Source |
|------|----------|------------|--------|

### 2.4 Disease Associations
| Gene | Disease | Score | Evidence | Source |
|------|---------|-------|----------|--------|

**Sources**: (tools used)

---

## 3. Pathway Enrichment Analysis

### 3.1 STRING Functional Enrichment
| Category | Term | Description | P-value | FDR | Genes | Source |
|----------|------|-------------|---------|-----|-------|--------|

### 3.2 Reactome Pathway Analysis
| Pathway ID | Name | P-value | FDR | Genes Found | Total Genes | Source |
|------------|------|---------|-----|-------------|-------------|--------|

### 3.3 GO Biological Processes
| GO Term | Description | P-value | FDR | Genes | Source |
|---------|-------------|---------|-----|-------|--------|

### 3.4 GO Molecular Functions
| GO Term | Description | P-value | FDR | Genes | Source |
|---------|-------------|---------|-----|-------|--------|

### 3.5 GO Cellular Components
| GO Term | Description | P-value | FDR | Genes | Source |
|---------|-------------|---------|-----|-------|--------|

### Pathway Summary
- Top enriched pathways:
- Key biological processes:
- Spatial pathway implications:

**Sources**: (tools used)

---

## 4. Spatial Domain Characterization

### Domain: {domain_name}

#### Marker Genes
| Gene | Function | Pathways | Source |
|------|----------|----------|--------|

#### Enriched Pathways (domain-specific)
| Pathway | P-value | FDR | Genes | Source |
|---------|---------|-----|-------|--------|


| Cell Type | Marker Genes Present | Confidence |
|-----------|---------------------|------------|


(Narrative interpretation of this domain)

(Repeat for each domain)


| Feature | Domain 1 | Domain 2 | Domain 3 |
|---------|----------|----------|----------|
| Top pathway | | | |
| Cell types | | | |
| Disease relevance | | | |

: (tools used)

---




| Protein A | Protein B | Score | Type | Source |
|-----------|-----------|-------|------|--------|


| Ligand | Receptor | Domain (Ligand) | Domain (Receptor) | Evidence | Source |
|--------|----------|-----------------|-------------------|----------|--------|


| Pathway | Components in Data | Spatial Distribution | Source |
|---------|--------------------|---------------------|--------|


 Key interaction hubs:
 Cross-domain interactions:
 Predicted cell-cell communication axes:

: (tools used)

---




| Gene | Disease Association Score | Evidence Type | Source |
|------|--------------------------|---------------|--------|


| Gene | Domain | Tractability | Modality | Approved Drugs | Source |
|------|--------|-------------|----------|----------------|--------|


| Drug | Target | Mechanism | Phase | Source |
|------|--------|-----------|-------|--------|


| NCT ID | Title | Target Gene | Phase | Status | Source |
|--------|-------|-------------|-------|--------|--------|


 Druggable genes in disease regions:
 Approved therapies:
 Pipeline drugs:
 Novel opportunities:

: (tools used)

---




| Gene/Protein | RNA Pattern | Protein Pattern | Concordance | Source |
|-------------|-------------|-----------------|-------------|--------|


| Gene | mRNA Location (spatial) | Protein Location (HPA) | Concordance | Source |
|------|------------------------|----------------------|-------------|--------|


| Gene | Metabolic Pathway | Metabolites Detected | Spatial Pattern | Source |
|------|-------------------|---------------------|-----------------|--------|

: (tools used)

---




| Cell Type | Marker Genes | Spatial Domain | Source |
|-----------|-------------|----------------|--------|


| Checkpoint | Gene | Expression Pattern | Source |
|------------|------|--------------------|--------|


| Feature | Finding | Evidence | Source |
|---------|---------|----------|--------|


 Immune infiltration pattern:
 Key immune checkpoints:
 Immunotherapy implications:

: (tools used)

---




| PMID | Title | Relevance | Year | Source |
|------|-------|-----------|------|--------|


(Known tissue architecture/zonation from literature)


| Priority | Gene/Target | Method | Rationale |
|----------|-------------|--------|-----------|
| High | | IHC / smFISH | |
| Medium | | IF / ISH | |

: (tools used)

---



| Component | Points | Max | Details |
|-----------|--------|-----|---------|
| SVGs provided | | 5 | |
| Disease context | | 5 | |
| Spatial domains | | 5 | |
| Cell types | | 5 | |
| Multi-modal data | | 5 | |
| Literature context | | 5 | |
| Pathway enrichment | | 10 | |
| Cell-cell interactions | | 10 | |
| Disease mechanism | | 10 | |
| Druggable targets | | 10 | |
| Cross-database validation | | 10 | |
| Clinical validation | | 10 | |
| Literature support | | 10 | |
|  | |  | |

: XX/100 - [Tier]

---



 [ ] Gene ID resolution complete
 [ ] Tissue expression patterns analyzed (HPA)
 [ ] Subcellular localization checked (HPA)
 [ ] Pathway enrichment complete (STRING + Reactome)
 [ ] GO enrichment complete (BP + MF + CC)
 [ ] Spatial domains characterized individually
 [ ] Domain comparison performed
 [ ] Protein-protein interactions analyzed (STRING)
 [ ] Ligand-receptor pairs identified
 [ ] Disease associations checked (OpenTargets)
 [ ] Druggable targets identified (OpenTargets tractability)
 [ ] Drug mechanisms reviewed
 [ ] Multi-modal integration performed (if data available)
 [ ] Immune microenvironment characterized (if relevant)
 [ ] Literature search completed
 [ ] Validation recommendations provided
 [ ] Spatial Omics Integration Score calculated
 [ ] Executive summary written
 [ ] All sections have source citations

---




| # | Tool | Parameters | Section | Items Retrieved |
|---|------|------------|---------|-----------------|


 OpenTargets: (current)
 STRING: v12.0
 Reactome: (current)
 HPA: (current)
 GTEx: v10

---

Phase 0: Input Processing & Disambiguation (ALWAYS FIRST)

Objective: Parse user input, resolve tissue/disease identifiers, establish analysis context.

Tools Used

OpenTargets_get_disease_id_description_by_name (if disease context provided):

• Input: diseaseName (string) - Disease name
• Output: {data: {search: {hits: [{id, name, description}]}}}
• Use: Get MONDO/EFO IDs for disease queries

OpenTargets_get_disease_description_by_efoId:

• Input: efoId (string) - Disease ID (e.g., MONDO_0007254)
• Output: {data: {disease: {id, name, description, dbXRefs}}}
• Use: Get full disease description

HPA_search_genes_by_query (tissue cell type context):

• Input: query (string) - Search term
• Output: List of gene entries matching query
• Use: Verify tissue-relevant genes

Workflow

1. Parse SVG list from user input (ensure valid gene symbols)
2. Identify tissue type and map to standard ontology term
3. If disease provided, resolve to MONDO/EFO ID using OpenTargets
4. Get disease description and cross-references
5. Determine analysis scope:
   • Cancer? -> Include immune microenvironment, somatic mutations, druggable targets
   • Neurological? -> Include brain region specificity, neuronal markers
   • Metabolic? -> Include metabolic zonation, enzyme distribution
   • Normal tissue? -> Focus on tissue architecture and cell type composition
6. Set up report file with header information

Decision Logic

• Cancer tissue: Enable immune microenvironment phase, CIViC/cBioPortal queries, immuno-oncology analysis
• Normal tissue: Skip disease phases, focus on tissue zonation and cell type composition
• Liver/kidney/brain: Enable zonation-specific analysis
• No disease context: Proceed with tissue biology only
• Small gene list (<20): Warn about limited enrichment power, emphasize gene-level analysis
• Large gene list (>500): Suggest filtering to top SVGs by significance before enrichment

---

Phase 1: Gene Characterization

Objective: Resolve gene identifiers, annotate functions, tissue specificity, and subcellular localization.

Tools Used

MyGene_query_genes (gene ID resolution):

• Input: query (string) - Gene symbol
• Output: {hits: [{_id, symbol, name, ensembl: {gene}, entrezgene}]}
• Use: Resolve gene symbol to Ensembl ID, Entrez ID
• NOTE: First hit may not be exact match - filter by symbol field

UniProt_get_function_by_accession (gene function):

• Input: accession (string) - UniProt accession
• Output: List of function description strings
• Use: Get protein function annotation

UniProt_get_subcellular_location_by_accession (protein localization):

• Input: accession (string)
• Output: Subcellular location information
• Use: Where the protein is located in the cell

HPA_get_subcellular_location (validated localization):

• Input: gene_name (string) - Gene symbol
• Output: {gene_name, main_locations: [], additional_locations: [], location_summary}
• Use: Experimentally validated protein subcellular location

HPA_get_rna_expression_by_source (tissue expression):

• Input: gene_name (string), source_type (string: 'tissue'), source_name (string)
• Output: {data: {gene_name, source_type, source_name, expression_value, expression_level}}
• Use: Check expression in the specific tissue of interest
• NOTE: All 3 parameters are REQUIRED

HPA_get_comprehensive_gene_details_by_ensembl_id (full HPA data):

• Input: ensembl_id (string), include_isoforms (bool), include_images (bool), include_antibodies (bool), include_expression (bool) - ALL 5 parameters REQUIRED
• Output: {ensembl_id, gene_name, uniprot_ids, summary, protein_classes, tissue_expression, cell_line_expression, ...}
• Use: One-stop gene characterization from HPA
• NOTE: Use include_expression=True for tissue data; set others to False for faster response

HPA_get_cancer_prognostics_by_gene (cancer prognosis):

• Input: ensembl_id (string) - Ensembl gene ID (NOT gene_name)
• Output: {gene_name, prognostic_cancers_count, prognostic_summary: [{cancer_type, prognostic_type, p_value}]}
• Use: Prognostic significance in cancer (if cancer context)

UniProtIDMap_gene_to_uniprot (ID mapping):

• Input: gene_name (string), organism (string, default 'human')
• Output: UniProt accession for the gene
• Use: Map gene symbol to UniProt accession

Workflow

1. For each SVG (batch if >20, sample top genes): a. Query MyGene to get Ensembl ID, Entrez ID b. Map to UniProt accession c. Get subcellular location from HPA d. Get tissue expression from HPA e. If cancer: check cancer prognostics
2. Compile gene characterization table
3. Identify genes with tissue-specific expression
4. Note genes with nuclear vs membrane vs secreted localization (relevant for spatial patterns)

Batch Strategy for Large Gene Lists

• 10-50 genes: Characterize all individually
• 50-200 genes: Characterize top 50 by priority (known disease genes first), summarize rest
• 200+ genes: Characterize top 30, use enrichment for the full list
• Always run pathway enrichment on the FULL list regardless

---

Phase 2: Pathway & Functional Enrichment

Objective: Identify biological pathways and functions enriched in SVGs and per-domain gene sets.

Tools Used

STRING_functional_enrichment (primary enrichment):

• Input: protein_ids (array of gene symbols), species (int, 9606 for human)
• Output: {status: 'success', data: [{category, term, number_of_genes, number_of_genes_in_background, p_value, fdr, description, inputGenes, preferredNames}]}
• Use: Comprehensive enrichment across GO, KEGG, Reactome, COMPARTMENTS, DISEASES
• Categories: Process (GO:BP), Function (GO:MF), Component (GO:CC), KEGG, Reactome, COMPARTMENTS, DISEASES, Keyword, PMID
• NOTE: This is the PRIMARY enrichment tool. Returns all categories in one call

ReactomeAnalysis_pathway_enrichment (Reactome-specific):

• Input: identifiers (string, space-separated gene symbols, NOT array)
• Output: {data: {token, pathways_found, pathways: [{pathway_id, name, p_value, fdr, entities_found, entities_total}]}}
• Use: Detailed Reactome pathway analysis with hierarchy
• NOTE: identifiers is a SPACE-SEPARATED STRING, not array

Reactome_map_uniprot_to_pathways (individual gene):

• Input: id (string) - UniProt accession
• Output: Plain list of pathway objects (no data wrapper)
• Use: Map individual proteins to Reactome pathways

GO_get_annotations_for_gene (individual gene GO):

• Input: gene_id (string) - Gene symbol or ID
• Output: Plain list of GO annotation objects
• Use: Get GO annotations for individual genes

kegg_search_pathway (KEGG pathway search):

• Input: query (string) - Pathway name or keyword
• Output: Pathway search results
• Use: Find KEGG pathways relevant to spatial findings

WikiPathways_search (WikiPathways):

• Input: query (string) - Search term
• Output: WikiPathways search results
• Use: Additional pathway context

Workflow

1. Global SVG enrichment: Run STRING_functional_enrichment on ALL SVGs
   • Filter results by FDR < 0.05
   • Separate by category (Process, Function, Component, KEGG, Reactome)
   • Report top 10-15 per category
2. Reactome detailed analysis: Run ReactomeAnalysis_pathway_enrichment
   • Report top pathways with FDR < 0.05
3. Per-domain enrichment (if spatial domains provided):
   • Run STRING_functional_enrichment on each domain's gene set
   • Compare enriched pathways across domains
   • Identify domain-specific vs shared pathways
4. Compile pathway tables: Merge results from all enrichment tools

Enrichment Interpretation

• Signaling pathways (RTK, Wnt, Notch, Hedgehog): Cell-cell communication
• Metabolic pathways: Tissue metabolic zonation
• Immune pathways: Immune infiltration/exclusion
• ECM/adhesion pathways: Tissue structure and remodeling
• Cell cycle/proliferation: Growth zones
• Apoptosis/stress: Damage zones

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Phase 3: Spatial Domain Characterization

Objective: Characterize each spatial domain biologically and compare between domains.

Tools Used

Uses the same tools as Phase 2 (STRING_functional_enrichment, ReactomeAnalysis) applied per-domain, plus:

HPA_get_biological_processes_by_gene (per-gene processes):

• Input: gene_name (string)
• Output: Biological processes associated with the gene
• Use: Annotate domain marker genes

HPA_get_protein_interactions_by_gene (gene interactions):

• Input: gene_name (string)
• Output: Known protein interaction partners
• Use: Build domain-specific interaction context

Workflow

1. For each spatial domain: a. Get marker gene list b. Run STRING_functional_enrichment on domain genes c. Identify top pathways, GO terms d. Assign likely cell type(s) based on marker genes:
   • Epithelial: CDH1, EPCAM, KRT18, KRT19
   • Mesenchymal/Fibroblast: VIM, COL1A1, COL3A1, FAP, ACTA2
   • Immune T cell: CD3E, CD3D, CD4, CD8A, CD8B
   • Immune B cell: CD19, CD20 (MS4A1), CD79A
   • Macrophage: CD68, CD163, CSF1R
   • Endothelial: PECAM1, VWF, CDH5
   • Neuronal: SNAP25, SYP, MAP2, NEFL
   • Hepatocyte: ALB, HNF4A, CYP3A4 e. Generate biological interpretation narrative
2. Compare domains:
   • Differential pathways
   • Unique vs shared genes
   • Disease-relevant vs homeostatic regions
   • Transition zones (shared genes between adjacent domains)

Cell Type Assignment Rules

When user does not provide cell type annotations, infer from marker genes:

• Check each gene against known cell type markers
• Use HPA tissue/cell type expression data for validation
• Report confidence level (high: 3+ markers match, medium: 2 markers, low: 1 marker)

---

Phase 4: Cell-Cell Interaction Inference

Objective: Predict cell-cell communication from spatial gene expression patterns.

Tools Used

STRING_get_interaction_partners (PPI network):

• Input: protein_ids (array), species (int, 9606), limit (int), confidence_score (float, 0.7)
• Output: {status: 'success', data: [{preferredName_A, preferredName_B, score, nscore, fscore, pscore, ascore, escore, dscore, tscore}]}
• Use: Find protein-protein interactions among SVGs
• Score types: nscore=neighborhood, fscore=fusion, pscore=phylogenetic, ascore=coexpression, escore=experimental, dscore=database, tscore=textmining

STRING_get_protein_interactions (pairwise interactions):

• Input: protein_ids (array), species (int, 9606)
• Output: Interaction data between specified proteins
• Use: Get interactions within a specific gene set

intact_search_interactions (IntAct database):

• Input: query (string), max (int)
• Output: Interaction data from IntAct
• Use: Complement STRING with IntAct interactions

Reactome_get_interactor (Reactome interactions):

• Input: Protein/gene identifier
• Output: Reactome interaction data
• Use: Pathway-level interaction context

DGIdb_get_drug_gene_interactions (drug-gene interactions):

• Input: genes (array of strings)
• Output: Drug-gene interaction data
• Use: Identify druggable interaction nodes

Ligand-Receptor Analysis

Known ligand-receptor pairs to check in SVG list:

• Growth factors: EGF-EGFR, HGF-MET, VEGF-KDR, FGF-FGFR, PDGF-PDGFRA/B
• Cytokines: TNF-TNFR, IL6-IL6R, IFNG-IFNGR, TGFB1-TGFBR1/2
• Chemokines: CXCL12-CXCR4, CCL2-CCR2, CXCL10-CXCR3
• Immune checkpoints: CD274(PD-L1)-PDCD1(PD-1), CD80/CD86-CTLA4, LGALS9-HAVCR2(TIM-3)
• Notch signaling: DLL1/3/4-NOTCH1/2/3/4, JAG1/2-NOTCH1/2
• Wnt signaling: WNT ligands-FZD receptors
• Adhesion: CDH1-CDH1 (homotypic), ITGA/B integrins-ECM
• Hedgehog: SHH-PTCH1

Workflow

1. Run STRING_get_interaction_partners on all SVGs
   • Filter interactions with score > 0.7
   • Identify hub genes (most connections)
2. Check for known ligand-receptor pairs in gene list
   • Cross-reference with spatial domain assignments
   • Identify potential cross-domain signaling
3. Build interaction network:
   • Intra-domain interactions (within same spatial region)
   • Inter-domain interactions (between different regions)
   • Identify signaling axes (e.g., tumor-stroma, immune-tumor)
4. Map interactions to Reactome signaling pathways

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Extended Reference: For detailed tool tables, examples, and templates, read REFERENCE.md in this skill directory. The agent can access it via: read skills/tooluniverse-spatial-omics-analysis/REFERENCE.md