Spatial immune neighborhoods

Use this workflow to study immune organization in tissue coordinate data. It turns cell or spot coordinates into graph neighborhoods, neighborhood enrichment, TLS-like candidate records, region summaries, and cytokine or immune-state gradients that can be interpreted with disease context.

Research question

Which immune cell neighborhoods are enriched in tissue, where do TLS-like regions appear, and how do cytokine or immune-state gradients vary by region?

Use case

Use this for tissue immunology studies where spatial organization matters: vaccine-draining lymphoid tissue, inflamed autoimmune biopsies, infection lesions, allergy/atopy tissue, and immunotherapy samples with TLS-like structures. The workflow is most useful when cell labels, coordinates, cytokine or state scores, and optional tissue-region labels are available.

Suggested path

1. Register a spatial cell or spot table with x, y, cell type, immune state, and optional region labels.
2. Run disease_workflows with a spatial radius appropriate for the coordinate scale.
3. Review niche tables, graph neighborhoods, neighborhood enrichment, TLS-like candidate records, cytokine/state gradients, region summaries, and proximity outputs.
4. Use interpretation or reports once the spatial workflow and relevant communication runs are complete.

Outputs to cite

• graph degree and neighbor cell-type composition
• observed and expected neighborhood edges
• neighborhood enrichment and z-style score
• TLS-like candidate score and criteria
• local cytokine and immune-state gradients
• region-level mean cytokine and state-axis scores