💊 Spatial coexpression of disease genes in enriched structures suggests a new avenue:
→ Spatially informed drug repurposing strategies.

🧠 Top Alzheimer’s genes, identified with STEAM, were enriched among upregulated DEGs in specific cell types—Microglia showing the strongest signal.

In sc/snRNAseq from brain we found enrichment for Alzheimer’s pointing towards immune and barrier dysfunction. Here using a sliding window approach to enrichment, scanning through the ranked gene list of genetically associated genes.

In the pancreas, we saw reorganization of disease-relevant cell types for type 1 diabetes—implicating both endocrine & exocrine compartments.

In developing human spinal cord, we found anterior horn disorder enriched in ventral horn grey matter— this pattern was consistent across multiple developmental time points.

Chronic kidney disease → Enrichment in proximal tubule & glomerulus.
🔄 These two structures are known to engage in crosstalk: damage to one can affect the other.

🫀 Coronary artery disease → Enrichment in heart vessels.

📊 Up to 20% of trait-tissue pairs show structure-specific enrichment — reinforcing the power of spatial resolution.

🧮 For ranked gene lists, STEAM uses a sliding window to compare signal vs. null distribution — finding sets of top enriched genes.
💡 We used @opentargets.org for curated gene lists with genetic associations.

🔬 Study scale:
• 8 organs
• 31 spatial datasets
• 32 human diseases
• 152 trait-tissue pairs
• 2 brain sc/snRNA-seq datasets

🚀 New method: Identify putative causal tissue structures and cell types for complex traits using your Seurat object + a ranked gene list.
🧰 Our tool: the upcoming R package STEAM.

Thank you Embla ❤️