Agent skill
exosome-ev-analysis-agent
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/exosome-ev-analysis-agent
SKILL.md
---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
-
EV Cargo Profiling: Analyze exosomal RNA (miRNA, lncRNA, circRNA), proteins, and lipids.
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Tumor EV Identification: Distinguish tumor-derived EVs from normal EVs using surface markers and cargo.
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Biomarker Discovery: ML-driven identification of cancer-specific EV signatures.
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Communication Network: Map EV-mediated signaling between tumor and TME cells.
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Functional Prediction: Predict downstream effects of EV cargo on recipient cells.
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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
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Input: EV isolation method, cargo profiling data (RNA-seq, proteomics), characterization data.
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Quality Assessment: Evaluate EV purity and characterization (NTA, TEM, markers).
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Cargo Analysis: Profile RNA, protein, and lipid content.
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Source Deconvolution: Identify tumor vs stromal EV origin.
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Biomarker Selection: Identify cancer-specific signatures.
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Functional Prediction: Predict effects on recipient cells.
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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
- Early Detection: Cancer screening from blood EVs
- Prognosis: EV signatures predicting outcomes
- Therapy Response: Monitor treatment effect
- Metastasis: Predict metastatic potential
- 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
- EV-based Drug Delivery: Therapeutic cargo loading
- EV Engineering: Surface modification for targeting
- Tumor Vaccines: EV-based immunotherapy
- Companion Diagnostics: Treatment selection markers
Author
AI Group - Biomedical AI Platform
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