---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

| 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.

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**:
```bash
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