TME and signaling

Profile tumor microenvironment composition and identify dominant cell-cell communication axes across immune, stromal, and malignant populations.

Research question

What cell types populate the tumor microenvironment, and which ligand-receptor signaling networks regulate immune activation, suppression, or exclusion?

Who this is for

• Tumor biology researchers at academic cancer centers and TME labs
• Solid tumor groups studying immune infiltration and stromal interactions
• Spatial biology teams connecting scRNA-seq findings to tissue context

Data requirements

Data	Required	Purpose
Single-cell RNA-seq	Yes (for TME)	Cell-type composition, state scoring, clustering
Per-cell expression + metadata CSV	Yes (for communication)	Ligand-receptor scoring across cell types
Condition labels in metadata	No	Responder/non-responder or pre/post treatment comparison
Spatial transcriptomics	No	Spatial TME mapping (via compbio spatial pipeline)
WSI slides	No	Morphology context (via Pathology area)

Single-cell ingestion and clustering run through the Computational Biology area. Oncology adds TME annotation, communication analysis, and cohort comparison on top.

Workflow

Upload scRNA-seq (compbio) → TME composition and cell states
  → Cell-cell communication across annotated populations
  → Condition comparison (optional)
  → AI interpretation

Step 1 — TME profiling

Link a compbio single-cell dataset to your oncology study. Run QC, normalization, clustering, and cell-type annotation with oncology reference signatures on the TME page.

Review compartment fractions — tumor, immune, stromal, vascular — and cell-state scores for exhaustion, activation, and regulatory suppression.

See Tumor microenvironment.

Step 2 — Cell-cell communication

Export annotated per-cell expression and metadata, then launch communication analysis:

curl -X POST http://localhost:8001/oncology/jobs/cell-communication \
  -H "Content-Type: application/json" \
  -d '{
    "study_id": "your-study-id",
    "parameters": {
      "expression_path": "/path/to/expression.csv",
      "metadata_path": "/path/to/metadata.csv",
      "cell_type_column": "cell_type",
      "condition_column": "condition",
      "condition_a": "responder",
      "condition_b": "non_responder",
      "permutations": 100
    }
  }'

Review sender/receiver heatmaps, pathway-level summaries, and condition-comparison deltas on the Communication page.

Step 3 — Spatial context (optional)

When Visium or Xenium data is available, use compbio spatial pipelines for neighborhood analysis and spatial deconvolution. Pathology WSI overlays provide tissue morphology context for tumor-stroma boundary analysis.

Step 4 — Interpretation

Launch interpretation with communication run IDs for a summary of dominant signaling axes and their immunological implications.

Expected outputs

• TME composition bar charts by cell type and treatment condition
• Scored ligand-receptor interaction table with sender/receiver cell types
• Pathway-level aggregated signaling scores
• Network adjacency for interactive graph visualization
• Condition-comparison deltas highlighting axes enriched in one group
• AI narrative explaining suppressive, co-stimulatory, and cytokine signaling patterns

Typical analyses

Analysis	Comparison	Question
IO resistance	Non-responder vs. responder	Which suppressive LR pairs dominate in non-responders?
Treatment effect	Pre- vs. post-treatment	How does signaling shift after therapy?
Compartment crosstalk	Tumor → T cell vs. myeloid → T cell	Which stromal populations drive T cell regulation?
Checkpoint axis	PD-L1/PD-1 and related pairs	Where is checkpoint signaling concentrated?

Related guides

• Tumor microenvironment
• Cell-cell communication
• Computational Biology Single-cell and Spatial guides