exosome-ev-analysis-agent

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---

name: 'exosome-ev-analysis-agent'

description: 'AI-powered extracellular vesicle and exosome analysis for cancer biomarker discovery, liquid biopsy applications, and intercellular communication profiling.'

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

allowed-tools:

• read_file
• run_shell_command

---

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.

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

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

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

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

1. 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.

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

1. Cargo Analysis: Profile RNA, protein, and lipid content.

1. Source Deconvolution: Identify tumor vs stromal EV origin.

1. Biomarker Selection: Identify cancer-specific signatures.

1. Functional Prediction: Predict effects on recipient cells.

1. Output: EV profile, biomarker candidates, functional predictions.

Example Usage

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

Agent Action:

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

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