cellular-senescence-agent

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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.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

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

1. Senescence Scoring: Calculate senescence signatures from transcriptomic data.

1. SASP Profiling: Characterize senescence-associated secretory phenotype composition.

1. Single-Cell Detection: Identify senescent cells in scRNA-seq data.

1. Senolytic Prediction: Predict sensitivity to senolytic drugs.

1. Tissue Aging: Assess senescence burden across tissues.

1. 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

1. Input: Bulk or single-cell RNA-seq, proteomics, imaging data.

1. Signature Scoring: Apply senescence gene signatures.

1. SASP Analysis: Profile secretory phenotype.

1. Cell Identification: Flag senescent cells (single-cell).

1. Senolytic Prediction: Match to drug sensitivity profiles.

1. Burden Estimation: Quantify senescence load.

1. 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

1. Aging Research: Quantify senescence burden
2. Cancer Therapy: Monitor TIS response
3. Drug Development: Senolytic efficacy
4. Fibrosis: Senescence in fibrotic disease
5. Regeneration: Senescence in tissue repair

Author

AI Group - Biomedical AI Platform

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