---name: cellular-senescence-agent description: AI-powered analysis of cellular senescence for aging research, cancer therapy response, and senolytic drug development. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

keywords:

• cellular-senescence-agent
• automation
• biomedical measurable_outcome: execute task with >95% success rate. ---"

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.

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

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

4. Senolytic Prediction: Predict sensitivity to senolytic drugs.

5. Tissue Aging: Assess senescence burden across tissues.

6. Cancer Senescence: Analyze therapy-induced senescence.

Senescence Markers

Workflow

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

2. Signature Scoring: Apply senescence gene signatures.

3. SASP Analysis: Profile secretory phenotype.

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

5. Senolytic Prediction: Match to drug sensitivity profiles.

6. Burden Estimation: Quantify senescence load.

7. 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

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

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