---name: exosome-ev-analysis-agent
description: AI-powered extracellular vesicle and exosome analysis for cancer biomarker discovery, liquid biopsy applications, and intercellular communication profiling.
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:
  - exosome-ev-analysis-agent
  - automation
  - biomedical
measurable_outcome: execute task with >95% success rate.
---"

# Exosome/EV Analysis Agent

The **Exosome/EV Analysis Agent** provides comprehensive AI-driven analysis of extracellular vesicles for cancer biomarker discovery, liquid biopsy applications, and tumor-microenvironment communication profiling.

## When to Use This Skill

* When analyzing exosome cargo (RNA, protein, lipids) for biomarker discovery.
* To identify tumor-derived EVs in liquid biopsy samples.
* For profiling EV-mediated intercellular communication in cancer.
* When predicting EV uptake and functional effects on recipient cells.
* To design EV-based diagnostic or therapeutic applications.

## Core Capabilities

1. **EV Cargo Profiling**: Analyze exosomal RNA (miRNA, lncRNA, circRNA), proteins, and lipids.

2. **Tumor EV Identification**: Distinguish tumor-derived EVs from normal EVs using surface markers and cargo.

3. **Biomarker Discovery**: ML-driven identification of cancer-specific EV signatures.

4. **Communication Network**: Map EV-mediated signaling between tumor and TME cells.

5. **Functional Prediction**: Predict downstream effects of EV cargo on recipient cells.

6. **Diagnostic Development**: Support EV-based diagnostic assay design.

## EV Classification

| Type | Size | Origin | Markers |
|------|------|--------|---------|
| Exosomes | 30-150 nm | MVB fusion | CD9, CD63, CD81 |
| Microvesicles | 100-1000 nm | Membrane budding | Annexin V, ARF6 |
| Apoptotic bodies | 500-5000 nm | Cell death | Annexin V, PS |
| Large oncosomes | 1-10 μm | Tumor-specific | Variable |

## Workflow

1. **Input**: EV isolation method, cargo profiling data (RNA-seq, proteomics), characterization data.

2. **Quality Assessment**: Evaluate EV purity and characterization (NTA, TEM, markers).

3. **Cargo Analysis**: Profile RNA, protein, and lipid content.

4. **Source Deconvolution**: Identify tumor vs stromal EV origin.

5. **Biomarker Selection**: Identify cancer-specific signatures.

6. **Functional Prediction**: Predict effects on recipient cells.

7. **Output**: EV profile, biomarker candidates, functional predictions.

## Example Usage

**User**: "Analyze exosomal miRNA profiles from plasma samples to identify pancreatic cancer biomarkers."

**Agent Action**:
```bash
python3 Skills/Oncology/Exosome_EV_Analysis_Agent/ev_analyzer.py \
    --ev_mirna exosome_smallrna.tsv \
    --ev_protein exosome_proteome.tsv \
    --sample_groups pancreatic_cancer,healthy \
    --normalization spike_in \
    --biomarker_discovery true \
    --output ev_biomarker_report/
```

## Exosomal miRNA Cancer Biomarkers

| Cancer Type | Elevated miRNAs | Clinical Use |
|-------------|-----------------|--------------|
| Pancreatic | miR-21, miR-17-5p, miR-155 | Early detection |
| Lung | miR-21, miR-126, miR-210 | Screening |
| Colorectal | miR-21, miR-92a, miR-29a | Detection |
| Prostate | miR-141, miR-375, miR-1290 | Prognosis |
| Ovarian | miR-21, miR-141, miR-200 family | Detection |
| Breast | miR-21, miR-155, miR-10b | Metastasis |

## EV Isolation Methods

| Method | Principle | Purity | Yield | Scalability |
|--------|-----------|--------|-------|-------------|
| Ultracentrifugation | Density | Moderate | High | Low |
| Size exclusion | Size | High | Moderate | Moderate |
| Immunocapture | Surface markers | Very high | Low | Low |
| Precipitation | Polymer | Low | Very high | High |
| Microfluidics | Various | Variable | Low | Low |

## AI/ML Components

**Biomarker Discovery**:
- Differential expression analysis
- Machine learning feature selection
- Multi-marker panel optimization
- Cross-validation and independent validation

**Source Deconvolution**:
- Marker-based classification
- ML models for tumor vs normal EVs
- Cell-type specific cargo signatures

**Functional Prediction**:
- miRNA target prediction
- Pathway enrichment
- Recipient cell effect modeling

## EV Characterization Quality

**MISEV Guidelines Requirements**:
- Particle concentration (NTA/TRPS)
- Size distribution (NTA/DLS/TEM)
- Protein markers (CD9/63/81, TSG101, ALIX)
- Negative markers (calnexin, albumin)
- Morphology (TEM)

## Clinical Applications

1. **Early Detection**: Cancer screening from blood EVs
2. **Prognosis**: EV signatures predicting outcomes
3. **Therapy Response**: Monitor treatment effect
4. **Metastasis**: Predict metastatic potential
5. **Resistance**: Identify resistance mechanisms

## Prerequisites

* Python 3.10+
* Small RNA analysis tools
* Proteomics analysis packages
* ML frameworks (scikit-learn, XGBoost)

## Related Skills

* Liquid_Biopsy_Analytics_Agent - For other liquid biopsy analytes
* Tumor_Microenvironment - For TME communication
* Cell-Free RNA Analysis - For plasma RNA

## Emerging Applications

1. **EV-based Drug Delivery**: Therapeutic cargo loading
2. **EV Engineering**: Surface modification for targeting
3. **Tumor Vaccines**: EV-based immunotherapy
4. **Companion Diagnostics**: Treatment selection markers

## Author

AI Group - Biomedical AI Platform