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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

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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:

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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