Senescent Cell Clearance as Neurodegeneration Therapy¶
Analysis ID: SDA-2026-04-02-gap-senescent-clearance-neuro
Date: 2026-04-02
Domain: neurodegeneration
Hypotheses Generated: 5
Knowledge Graph Edges: 15
Key Hypotheses¶
- SASP-Mediated Complement Cascade Amplification (score: 0.712)
- Selective Senolytic Targeting of p16-high Astrocytes (score: 0.685)
- Senescence-Associated Mitochondrial Dysfunction Cascade (score: 0.654)
- SASP-Driven Blood-Brain Barrier Permeability (score: 0.621)
- Paracrine Senescence Propagation via Exosomes (score: 0.598)
This notebook presents a computational analysis including differential gene expression, pathway enrichment, and multi-dimensional hypothesis scoring. Data is simulated based on known biology from the Allen Brain Cell Atlas (SEA-AD) and published literature.
1. Setup and Data Generation¶
Generated expression data for 20 genes x 5 cell types Samples per condition: 50
2. Differential Expression Heatmap¶
Log2 fold change of gene expression between AD and control samples across cell types. Significance: * p<0.05, ** p<0.01, *** p<0.001 (Mann-Whitney U test).
3. Volcano Plot: Microglia Expression¶
Differential expression in microglia — the primary immune cells of the brain. Red = upregulated in AD, blue = downregulated. Dashed line = p=0.05 threshold.
4. Statistical Analysis¶
Comprehensive statistical testing including non-parametric Mann-Whitney U tests, effect sizes (Cohen's d), and one-way ANOVA for cell-type variation.
5. Pathway Enrichment Analysis¶
Hypergeometric test for enrichment of hypothesis target genes in curated biological pathways (Reactome/KEGG-style). Identifies which molecular processes are overrepresented.
6. Hypothesis Multi-Dimensional Scoring¶
Top hypotheses scored across 6 key dimensions: mechanistic plausibility, evidence strength, novelty, feasibility, therapeutic impact, and druggability.
7. Knowledge Graph Edges¶
Causal relationships extracted from the multi-agent debate:
| Source | Relation | Target | Confidence |
|---|---|---|---|
| CDKN2A | marker_of | cellular_senescence | 0.92 |
| IL6 | component_of | SASP | 0.88 |
| IL1B | component_of | SASP | 0.87 |
| SERPINE1 | marker_of | cellular_senescence | 0.85 |
| TP53 | regulates | cellular_senescence | 0.90 |
| BCL2 | inhibits | apoptosis | 0.88 |
| C3 | activates | complement_cascade | 0.82 |
| TREM2 | modulates | microglial_phagocytosis | 0.80 |
| SASP | drives | neuroinflammation | 0.85 |
| senescent_astrocytes | secrete | SASP | 0.78 |
| CDKN2A | therapeutic_target | neurodegeneration | 0.75 |
| BCL2L1 | therapeutic_target | senolytic_therapy | 0.72 |
| MMP3 | degrades | extracellular_matrix | 0.70 |
| LMNB1 | decreases_in | senescent_cells | 0.68 |
| NLRP3 | activates | inflammasome | 0.76 |
Total edges: 15
Methodology¶
This analysis was generated by SciDEX's multi-agent scientific debate system:
- Theorist generates novel hypotheses based on known biology
- Skeptic challenges assumptions and identifies weaknesses
- Domain Expert assesses druggability, feasibility, and clinical relevance
- Synthesizer ranks hypotheses and extracts knowledge graph edges
Gene expression data is simulated based on published SEA-AD atlas findings (Allen Institute for Brain Science).
Generated: 2026-04-02 23:46 UTC
Platform: SciDEX
Source: GitHub