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.

KEY PRINCIPLES

:

Report-first approach

- Create report file FIRST, then populate progressively

Domain-by-domain analysis

- Characterize each spatial region independently before comparison

Gene-list-centric

- Analyze user-provided SVGs and marker genes with ToolUniverse databases

Biological interpretation

- Go beyond statistics to explain biological meaning of spatial patterns

Disease focus

- Emphasize disease mechanisms and therapeutic opportunities when disease context is provided

Evidence grading

- Grade all evidence as T1 (human/clinical) to T4 (computational)

Multi-modal thinking

- Integrate RNA, protein, and metabolite information when available

Validation guidance

- Suggest experimental validation approaches for key findings

Source references

- Every statement must cite tool/database source

English-first queries

- Always use English terms in tool calls

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 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

NOT for

Single gene interpretation (use target-research), variant interpretation, drug safety, bulk RNA-seq, GWAS analysis.

Input Parameters

Parameter

Required

Description

Example

svgs

Yes

Spatially variable genes

['EGFR', 'CDH1', 'VIM', 'MYC', 'CD3E']

tissue_type

Yes

Tissue/organ type

brain

,

liver

,

lung

,

breast

technology

No

Spatial omics platform

10x Visium

,

MERFISH

,

DBiTplus

disease_context

No

Disease if applicable

breast cancer

,

Alzheimer disease

spatial_domains

No

Domain -> marker genes dict

{'Tumor core': ['MYC','EGFR']}

cell_types

No

Cell types from deconvolution

['Epithelial', 'T cell']

proteins

No

Proteins detected (multi-modal)

['CD3', 'PD-L1', 'Ki67']

metabolites

No

Metabolites (SpatialMETA)

['glutamine', 'lactate']

Spatial Omics Integration Score (0-100)

Data Completeness (0-30)

SVGs (5), Disease context (5), Spatial domains (5), Cell types (5), Multi-modal (5), Literature (5)

Biological Insight (0-40)

Pathway enrichment FDR<0.05 (10), Cell-cell interactions (10), Disease mechanism (10), Druggable targets (10)

Evidence Quality (0-30)

Cross-database validation 3+ DBs (10), Clinical validation (10), Literature support (10) Score Tier Interpretation 80-100 Excellent Comprehensive characterization, strong insights, druggable targets 60-79 Good Good pathway/interaction analysis, some therapeutic context 40-59 Moderate Basic enrichment, limited domain comparison 0-39 Limited Minimal data, gene-level annotation only Evidence Grading Tier Criteria Examples [T1] Direct human/clinical evidence FDA-approved drug, validated biomarker [T2] Experimental evidence Validated spatial pattern, known L-R pair [T3] Computational/database evidence PPI prediction, pathway enrichment [T4] Annotation/prediction only GO annotation, text-mined association Analysis Phases Overview Phase 0: Input Processing & Disambiguation (ALWAYS FIRST) Resolve tissue/disease identifiers, establish analysis context. Get MONDO/EFO IDs for disease queries. Tools: OpenTargets_get_disease_id_description_by_name , OpenTargets_get_disease_description_by_efoId , HPA_search_genes_by_query Phase 1: Gene Characterization Resolve gene IDs, annotate functions, tissue specificity, subcellular localization. Tools: MyGene_query_genes , UniProt_get_function_by_accession , HPA_get_subcellular_location , HPA_get_rna_expression_by_source , HPA_get_comprehensive_gene_details_by_ensembl_id , HPA_get_cancer_prognostics_by_gene , UniProtIDMap_gene_to_uniprot Phase 2: Pathway & Functional Enrichment Identify enriched pathways globally and per-domain. Filter FDR < 0.05. Tools: STRING_functional_enrichment (PRIMARY), ReactomeAnalysis_pathway_enrichment , GO_get_annotations_for_gene , kegg_search_pathway , WikiPathways_search Phase 3: Spatial Domain Characterization Characterize each domain biologically, assign cell types from markers, compare domains. Tools: Phase 2 tools + HPA_get_biological_processes_by_gene , HPA_get_protein_interactions_by_gene Phase 4: Cell-Cell Interaction Inference Predict communication from spatial patterns. Check ligand-receptor pairs across domains. Tools: STRING_get_interaction_partners , STRING_get_protein_interactions , intact_search_interactions , Reactome_get_interactor , DGIdb_get_drug_gene_interactions Phase 5: Disease & Therapeutic Context Connect to disease mechanisms, identify druggable targets, find clinical trials. Tools: OpenTargets_get_associated_targets_by_disease_efoId , OpenTargets_get_target_tractability_by_ensemblID , OpenTargets_get_associated_drugs_by_target_ensemblID , clinical_trials_search , DGIdb_get_gene_druggability , civic_search_genes Phase 6: Multi-Modal Integration Integrate protein/RNA/metabolite data. Compare spatial RNA with protein detection. Tools: HPA_get_subcellular_location , HPA_get_rna_expression_in_specific_tissues , Reactome_map_uniprot_to_pathways , kegg_get_pathway_info Phase 7: Immune Microenvironment (Cancer/Inflammation only) Classify immune cells, check checkpoint expression, assess Hot vs Cold vs Excluded patterns. Tools: STRING_functional_enrichment , OpenTargets_get_target_tractability_by_ensemblID , iedb_search_epitopes Phase 8: Literature & Validation Context Search published evidence, suggest validation experiments (smFISH, IHC, PLA). Tools: PubMed_search_articles , openalex_literature_search See phase-procedures.md for detailed workflows, decision logic, and tool parameter specifications per phase. Report Structure Create file: {tissue}_{disease}_spatial_omics_report.md

Spatial Multi-Omics Analysis Report:

Report Generated: {date} | Technology: {platform} Tissue: {tissue_type} | Disease: {disease or "Normal tissue"} Total SVGs: {count} | Spatial Domains: {count} Spatial Omics Integration Score: (calculated after analysis)

Executive Summary

1. Tissue & Disease Context

2. Spatially Variable Gene Characterization

• 2.1 Gene ID Resolution
• 2.2 Tissue Expression Patterns
• 2.3 Subcellular Localization
• 2.4 Disease Associations

3. Pathway Enrichment Analysis

• 3.1 STRING, 3.2 Reactome, 3.3-3.5 GO (BP, MF, CC)

4. Spatial Domain Characterization (per-domain + comparison)

5. Cell-Cell Interaction Inference

• 5.1 PPI, 5.2 Ligand-Receptor, 5.3 Signaling Pathways

6. Disease & Therapeutic Context

• 6.1 Disease Gene Overlap, 6.2 Druggable Targets, 6.3 Drug Mechanisms, 6.4 Trials

7. Multi-Modal Integration (if data available)

8. Immune Microenvironment (if relevant)

9. Literature & Validation Context

Spatial Omics Integration Score (breakdown table)

Completeness Checklist

References (tools used, database versions)

See

report-template.md

for full template with table structures.

Completeness Checklist

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

PPI analyzed (STRING)

Ligand-receptor pairs identified

Disease associations checked (OpenTargets)

Druggable targets identified

Multi-modal integration performed (if data available)

Immune microenvironment characterized (if relevant)

Literature search completed

Validation recommendations provided

Integration Score calculated

Executive summary written

All sections have source citations

Common Use Cases

Cancer Spatial Heterogeneity

Visium with tumor/stroma/immune domains -> pathways, immune infiltration, druggable targets, checkpoints

Brain Tissue Zonation

MERFISH with neuronal subtypes -> synaptic signaling, receptors, hippocampal zonation

Liver Metabolic Zonation

Periportal vs pericentral -> CYP450, Wnt gradient, drug metabolism enzymes

Tumor-Immune Interface

DBiTplus RNA+protein -> checkpoint L-R pairs, immune exclusion, multi-modal concordance

Developmental Patterns

Morphogen gradients (Wnt, BMP, FGF, SHH), TF patterns, cell fate genes

Disease Progression

Disease gradient -> inflammatory response, neuronal loss, therapeutic windows

Reference Files

phase-procedures.md

- Detailed phase workflows, decision logic, tool usage per phase

tool-reference.md

- Tool parameter names, response formats, fallback strategies, limitations

reference-data.md

- Cell type markers, ligand-receptor pairs, immune checkpoint reference

report-template.md

- Full report template with all table structures

test_spatial_omics.py

- Test suite

Summary

Spatial Multi-Omics Analysis

provides:

Gene characterization (ID resolution, function, localization, tissue expression)

Pathway & functional enrichment (STRING, Reactome, GO, KEGG)

Spatial domain characterization (per-domain and cross-domain)

Cell-cell interaction inference (PPI, ligand-receptor, signaling)

Disease & therapeutic context (disease genes, druggable targets, trials)

Multi-modal integration (RNA-protein concordance, metabolic pathways)

Immune microenvironment (cell types, checkpoints, immunotherapy)

Literature context & validation recommendations

Outputs

Markdown report with Spatial Omics Integration Score (0-100)

Uses

70+ ToolUniverse tools across 9 analysis phases

Time

~10-20 minutes depending on gene list size