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, a

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 Tumor microenvironment spatial structure questions Integration of spatial with single cell data Spatial domain identification Tissue morphology correlation with expression Example Questions : 1. "Analyze this 10x Visium dataset to identify spatial domains" 2. "Which genes show spatially variable expression in this tissue?" 3. "Map the tumor microenvironment spatial organization" 4. "Find genes enriched at tissue boundaries" 5. "Identify cell cell interactions based on spatial proximity" 6. "Integrate spatial transcriptomics with scRNA seq annotations" Core Capabilities 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 Integration : Merge with scRNA seq for cell type mapping (Cell2location, Tangram, SPOTlight) Ligand Receptor Spatial : Map cell communication in tissue context via OmniPath Supported Platforms 10x Visium : 55um spots (~50 cells/spot), genome wide, includes H&E image — most common MERFISH/seqFISH : Single cell resolution, 100 10,000 targeted genes, imaging based Slide seq/V2 : 10um beads, genome wide — higher resolution than Visium Xenium : Single cell/subcellular, 300+ targeted genes (10x platform) Workflow Overview 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): geo search datasets / OmicsDI search datasets / NCBI SRA search runs — find spatial TX datasets UniProt get function by accession — protein function for stroma/immune markers STRING get network — protein interaction networks for key markers kegg search pathway / kegg get pathway info — relevant metabolic/signaling pathways 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. Phase 8: Report Generation See [report template.md](report template.md) for full example output. Integration with ToolUniverse Skills 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") Organ codes: LK/RK=Kidney, LI=Large Intestine, SI=Small Intestine, HT=Heart, LV=Liver, LU=Lung, SP=Spleen, BR=Brain, PA=Pancreas, SK=Skin. HuBMAP biospecimen + spatial registration layer (Samples & Donors) 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 dimension s + 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. When to use : 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 : 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: 1. 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) 2. 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

tooluniverse-spatial-transcriptomics