SpatialAgent

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

1. Tissue Organization Analysis: Decodes the structural logic of tissues (e.g., layers, niches).
2. Cellular Interaction Prediction: Identifies potential signaling pathways active at domain boundaries.
3. Hypothesis Proposal: Generates testable biological hypotheses based on spatial data.

Workflow

1. Input Analysis: Accepts processed ST data (e.g., cluster annotations, DEG lists per spatial domain).
2. Knowledge Retrieval: Queries biological knowledge bases regarding the observed cell types and genes.
3. 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:

1. Analyzes the gene expression of macrophages and adjacent tumor cells.
2. Checks for ligand-receptor pairs (e.g., CSF1-CSF1R).
3. Proposes: "Macrophages are likely recruited by CSF1 secreted by the tumor cells, forming an immunosuppressive barrier..."