菜单
AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development.
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| name | cellular-senescence-agent |
| description | AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development. |
The Cellular Senescence Agent provides comprehensive AI-driven analysis of cellular senescence signatures for aging research, cancer biology, and senolytic therapeutic development.
Senescence Scoring: Calculate senescence signatures from transcriptomic data.
SASP Profiling: Characterize senescence-associated secretory phenotype composition.
Single-Cell Detection: Identify senescent cells in scRNA-seq data.
Senolytic Prediction: Predict sensitivity to senolytic drugs.
Tissue Aging: Assess senescence burden across tissues.
Cancer Senescence: Analyze therapy-induced senescence.
| 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 |
Input: Bulk or single-cell RNA-seq, proteomics, imaging data.
Signature Scoring: Apply senescence gene signatures.
SASP Analysis: Profile secretory phenotype.
Cell Identification: Flag senescent cells (single-cell).
Senolytic Prediction: Match to drug sensitivity profiles.
Burden Estimation: Quantify senescence load.
Output: Senescence scores, SASP profile, drug recommendations.
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/
| 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 |
Pro-inflammatory:
Matrix Remodeling:
Effects on Microenvironment:
| 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) |
Senescence Classifier:
Drug Response:
Aging Clock Integration:
Therapy-Induced Senescence (TIS):
Senescence + Senolytics:
AI Group - Biomedical AI Platform