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.
Tumor EV Identification: Distinguish tumor-derived EVs from normal EVs using surface markers and cargo.
Biomarker Discovery: ML-driven identification of cancer-specific EV signatures.
Communication Network: Map EV-mediated signaling between tumor and TME cells.
Functional Prediction: Predict downstream effects of EV cargo on recipient cells.
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
Input: EV isolation method, cargo profiling data (RNA-seq, proteomics), characterization data.
Quality Assessment: Evaluate EV purity and characterization (NTA, TEM, markers).
Cargo Analysis: Profile RNA, protein, and lipid content.
Source Deconvolution: Identify tumor vs stromal EV origin.
Biomarker Selection: Identify cancer-specific signatures.
Functional Prediction: Predict effects on recipient cells.
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
1---2name: exosome-ev-analysis-agent3description: AI-powered extracellular vesicle and exosome analysis for cancer biomarker discovery, liquid biopsy applications, and intercellular communication profiling.4---56<!--7# COPYRIGHT NOTICE8# This file is part of the "Universal Biomedical Skills" project.9# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>10# All Rights Reserved.11#12# This code is proprietary and confidential.13# Unauthorized copying of this file, via any medium is strictly prohibited.14#15# Provenance: Authenticated by MD BABU MIA1617-->18192021# Exosome/EV Analysis Agent2223The **Exosome/EV Analysis Agent** provides comprehensive AI-driven analysis of extracellular vesicles for cancer biomarker discovery, liquid biopsy applications, and tumor-microenvironment communication profiling.2425## When to Use This Skill2627* When analyzing exosome cargo (RNA, protein, lipids) for biomarker discovery.28* To identify tumor-derived EVs in liquid biopsy samples.29* For profiling EV-mediated intercellular communication in cancer.30* When predicting EV uptake and functional effects on recipient cells.31* To design EV-based diagnostic or therapeutic applications.3233## Core Capabilities34351. **EV Cargo Profiling**: Analyze exosomal RNA (miRNA, lncRNA, circRNA), proteins, and lipids.36372. **Tumor EV Identification**: Distinguish tumor-derived EVs from normal EVs using surface markers and cargo.38393. **Biomarker Discovery**: ML-driven identification of cancer-specific EV signatures.40414. **Communication Network**: Map EV-mediated signaling between tumor and TME cells.42435. **Functional Prediction**: Predict downstream effects of EV cargo on recipient cells.44456. **Diagnostic Development**: Support EV-based diagnostic assay design.4647## EV Classification4849| Type | Size | Origin | Markers |50|------|------|--------|---------|51| Exosomes | 30-150 nm | MVB fusion | CD9, CD63, CD81 |52| Microvesicles | 100-1000 nm | Membrane budding | Annexin V, ARF6 |53| Apoptotic bodies | 500-5000 nm | Cell death | Annexin V, PS |54| Large oncosomes | 1-10 μm | Tumor-specific | Variable |5556## Workflow57581. **Input**: EV isolation method, cargo profiling data (RNA-seq, proteomics), characterization data.59602. **Quality Assessment**: Evaluate EV purity and characterization (NTA, TEM, markers).61623. **Cargo Analysis**: Profile RNA, protein, and lipid content.63644. **Source Deconvolution**: Identify tumor vs stromal EV origin.65665. **Biomarker Selection**: Identify cancer-specific signatures.67686. **Functional Prediction**: Predict effects on recipient cells.69707. **Output**: EV profile, biomarker candidates, functional predictions.7172## Example Usage7374**User**: "Analyze exosomal miRNA profiles from plasma samples to identify pancreatic cancer biomarkers."7576**Agent Action**:77```bash78python3 Skills/Oncology/Exosome_EV_Analysis_Agent/ev_analyzer.py \79 --ev_mirna exosome_smallrna.tsv \80 --ev_protein exosome_proteome.tsv \81 --sample_groups pancreatic_cancer,healthy \82 --normalization spike_in \83 --biomarker_discovery true \84 --output ev_biomarker_report/85```8687## Exosomal miRNA Cancer Biomarkers8889| Cancer Type | Elevated miRNAs | Clinical Use |90|-------------|-----------------|--------------|91| Pancreatic | miR-21, miR-17-5p, miR-155 | Early detection |92| Lung | miR-21, miR-126, miR-210 | Screening |93| Colorectal | miR-21, miR-92a, miR-29a | Detection |94| Prostate | miR-141, miR-375, miR-1290 | Prognosis |95| Ovarian | miR-21, miR-141, miR-200 family | Detection |96| Breast | miR-21, miR-155, miR-10b | Metastasis |9798## EV Isolation Methods99100| Method | Principle | Purity | Yield | Scalability |101|--------|-----------|--------|-------|-------------|102| Ultracentrifugation | Density | Moderate | High | Low |103| Size exclusion | Size | High | Moderate | Moderate |104| Immunocapture | Surface markers | Very high | Low | Low |105| Precipitation | Polymer | Low | Very high | High |106| Microfluidics | Various | Variable | Low | Low |107108## AI/ML Components109110**Biomarker Discovery**:111- Differential expression analysis112- Machine learning feature selection113- Multi-marker panel optimization114- Cross-validation and independent validation115116**Source Deconvolution**:117- Marker-based classification118- ML models for tumor vs normal EVs119- Cell-type specific cargo signatures120121**Functional Prediction**:122- miRNA target prediction123- Pathway enrichment124- Recipient cell effect modeling125126## EV Characterization Quality127128**MISEV Guidelines Requirements**:129- Particle concentration (NTA/TRPS)130- Size distribution (NTA/DLS/TEM)131- Protein markers (CD9/63/81, TSG101, ALIX)132- Negative markers (calnexin, albumin)133- Morphology (TEM)134135## Clinical Applications1361371. **Early Detection**: Cancer screening from blood EVs1382. **Prognosis**: EV signatures predicting outcomes1393. **Therapy Response**: Monitor treatment effect1404. **Metastasis**: Predict metastatic potential1415. **Resistance**: Identify resistance mechanisms142143## Prerequisites144145* Python 3.10+146* Small RNA analysis tools147* Proteomics analysis packages148* ML frameworks (scikit-learn, XGBoost)149150## Related Skills151152* Liquid_Biopsy_Analytics_Agent - For other liquid biopsy analytes153* Tumor_Microenvironment - For TME communication154* Cell-Free RNA Analysis - For plasma RNA155156## Emerging Applications1571581. **EV-based Drug Delivery**: Therapeutic cargo loading1592. **EV Engineering**: Surface modification for targeting1603. **Tumor Vaccines**: EV-based immunotherapy1614. **Companion Diagnostics**: Treatment selection markers162163## Author164165AI Group - Biomedical AI Platform166167168<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
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