By name: 'spatial-agent' description: 'An agent that interprets spatial transcriptomics data to propose mechanistic hypotheses and analyze tissue organization.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command
SpatialAgent
SpatialAgent focuses on the biological interpretation of spatial transcriptomics data, specifically aiming to propose mechanistic hypotheses about tissue organization and cellular interactions.
When to Use This Skill
- Mechanistic Interpretation: When you have clusters or spatial domains and need to understand why they are organized that way.
- Cell-Cell Interaction: To predict and interpret ligand-receptor interactions in a spatial context.
- Hypothesis Generation: To propose biological mechanisms driving the observed spatial heterogeneity.
Core Capabilities
- Tissue Organization Analysis: Decodes the structural logic of tissues (e.g., layers, niches).
- Cellular Interaction Prediction: Identifies potential signaling pathways active at domain boundaries.
- Hypothesis Proposal: Generates testable biological hypotheses based on spatial data.
Workflow
- Input Analysis: Accepts processed ST data (e.g., cluster annotations, DEG lists per spatial domain).
- Knowledge Retrieval: Queries biological knowledge bases regarding the observed cell types and genes.
- Synthesis: Constructs a narrative explaining the spatial arrangement (e.g., "The proximity of fibroblasts and tumor cells suggests a desmoplastic reaction mediated by TGF-beta signaling...").
Example Usage
User: "Why are the macrophages located at the boundary of the tumor core in this sample?"
Agent Action:
- Analyzes the gene expression of macrophages and adjacent tumor cells.
- Checks for ligand-receptor pairs (e.g., CSF1-CSF1R).
- Proposes: "Macrophages are likely recruited by CSF1 secreted by the tumor cells, forming an immunosuppressive barrier..."