| name | cellular-senescence-agent |
| description | AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development. |
Cellular Senescence Agent
The Cellular Senescence Agent provides comprehensive AI-driven analysis of cellular senescence signatures for aging research, cancer biology, and senolytic therapeutic development.
When to Use This Skill
- When identifying senescent cells in tissue or single-cell data.
- To analyze senescence-associated secretory phenotype (SASP).
- For predicting senolytic drug sensitivity.
- When studying therapy-induced senescence in cancer.
- To assess senescence burden in aging and disease.
Core Capabilities
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Senescence Scoring: Calculate senescence signatures from transcriptomic data.
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SASP Profiling: Characterize senescence-associated secretory phenotype composition.
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Single-Cell Detection: Identify senescent cells in scRNA-seq data.
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Senolytic Prediction: Predict sensitivity to senolytic drugs.
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Tissue Aging: Assess senescence burden across tissues.
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Cancer Senescence: Analyze therapy-induced senescence.
Senescence Markers
| Category | Markers | Detection |
|---|
| Cell cycle | p16INK4a, p21CIP1, p53 | Expression, IHC |
| SA-β-gal | GLB1 (lysosomal) | Activity assay |
| SASP | IL-6, IL-8, MMP3, PAI-1 | Expression, secretion |
| DNA damage | γH2AX, 53BP1 foci | Immunofluorescence |
| Morphology | Enlarged, flattened | Imaging |
| Epigenetic | SAHF, SAHMs | Chromatin marks |
Workflow
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Input: Bulk or single-cell RNA-seq, proteomics, imaging data.
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Signature Scoring: Apply senescence gene signatures.
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SASP Analysis: Profile secretory phenotype.
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Cell Identification: Flag senescent cells (single-cell).
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Senolytic Prediction: Match to drug sensitivity profiles.
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Burden Estimation: Quantify senescence load.
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Output: Senescence scores, SASP profile, drug recommendations.
Example Usage
User: "Analyze senescence signatures in this aging tissue dataset and identify senolytic candidates."
Agent Action:
python3 Skills/Longevity_Aging/Cellular_Senescence_Agent/senescence_analyzer.py \
--rnaseq tissue_expression.tsv \
--singlecell tissue_scrnaseq.h5ad \
--signatures fridman_sasp,reactome_senescence \
--senolytic_prediction true \
--tissue liver \
--output senescence_report/
Senescence Gene Signatures
| Signature | Genes | Application |
|---|
| Fridman (2017) | CDKN1A, CDKN2A, SERPINE1... | Pan-senescence |
| SenMayo | 125 genes | Tissue senescence |
| SASP Core | IL6, IL8, CXCL1, MMP1... | Secretory phenotype |
| p16/p21 pathway | CDKN2A, CDKN1A, MDM2... | Cell cycle arrest |
SASP Components
Pro-inflammatory:
- Interleukins: IL-1α/β, IL-6, IL-8
- Chemokines: CXCL1, CXCL2, CCL2
- Growth factors: TGF-β, VEGF
Matrix Remodeling:
- MMPs: MMP1, MMP3, MMP10
- Serpins: PAI-1 (SERPINE1)
Effects on Microenvironment:
- Paracrine senescence spread
- Immune cell recruitment
- ECM remodeling
- Tumor promotion (chronic) vs suppression (acute)
Senolytic Drugs
| Drug | Target | Clinical Status |
|---|
| Dasatinib | Src/tyrosine kinases | Trials (with Q) |
| Quercetin | PI3K, serpins | Trials (with D) |
| Navitoclax | BCL-2/BCL-xL | Trials |
| Fisetin | Multiple | Early trials |
| UBX1325 | BCL-xL | Phase 2 (macular) |
AI/ML Components
Senescence Classifier:
- Multi-gene signature scoring
- ML classifiers on expression
- Single-cell senescence probability
Drug Response:
- GDSC/CCLE senescence sensitivity
- SASP-drug correlations
- Synergy predictions
Aging Clock Integration:
- Epigenetic age correlation
- Transcriptomic age
- Senescence-aging relationships
Cancer Applications
Therapy-Induced Senescence (TIS):
- Chemotherapy, radiation
- CDK4/6 inhibitors (palbociclib)
- Dual outcomes: tumor suppression vs SASP-driven recurrence
Senescence + Senolytics:
- Induce senescence → clear with senolytics
- "One-two punch" approach
- Clinical trials ongoing
Prerequisites
- Python 3.10+
- Gene signature tools (GSVA, ssGSEA)
- Single-cell analysis (Scanpy)
- Drug response databases
Related Skills
- Single_Cell - For scRNA-seq analysis
- Cancer_Metabolism_Agent - For metabolic senescence
- Tumor_Microenvironment - For SASP effects
Research Applications
- Aging Research: Quantify senescence burden
- Cancer Therapy: Monitor TIS response
- Drug Development: Senolytic efficacy
- Fibrosis: Senescence in fibrotic disease
- Regeneration: Senescence in tissue repair
Author
AI Group - Biomedical AI Platform
