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Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets

Multimodal Analytical Platform on a Multiplexed Surface Plasmon Resonance Imaging Chip for the Analysis of Extracellular Vesicle Subsets

Published on: March 17, 2023

Extracellular Vesicle Analysis: Recent Technological Advances and Emerging Opportunities.

Yunjie Wen¹, Jingzhu Shi¹, Yuxin Deng¹

¹Department of Chemistry, University of Florida, Gainesville, Florida, USA;

Annual Review of Analytical Chemistry (Palo Alto, Calif.)

|February 17, 2026

Summary

Extracellular vesicles (EVs) are key for cell communication and show promise as biomarkers and therapeutics. This review covers recent advances in EV analysis, detection, and AI integration for improved diagnostics.

Keywords:

EV detection EV isolation artificial intelligence extracellular vesicle single EV analysis

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Characterizing Extracellular Vesicles from Biological Fluids

Characterizing Extracellular Vesicles from Biological Fluids

Published on: February 28, 2025

Area of Science:

Biochemistry
Cell Biology
Biotechnology

Background:

Extracellular vesicles (EVs) are crucial for intercellular communication.
EVs are increasingly recognized for their potential as diagnostic biomarkers and therapeutic agents.
Advances in EV analysis are vital for realizing their clinical potential.

Purpose of the Study:

To review recent progress in Extracellular Vesicle (EV) analysis.
To highlight emerging technologies in EV detection and single-EV analysis.
To discuss the role of artificial intelligence in EV research.

Main Methods:

Comprehensive literature review of recent studies on EV analysis.
Overview of current EV isolation, enrichment, and detection methodologies.
Exploration of single-EV analysis techniques and AI applications.

Main Results:

Significant advancements in EV isolation and detection technologies have been reported.
Single-EV analysis offers higher resolution for understanding EV heterogeneity.
Artificial intelligence is increasingly integrated to enhance EV data analysis.

Conclusions:

The field of EV analysis is rapidly evolving, with significant potential for clinical translation.
Improved analytical rigor in EV research is essential for developing reliable diagnostics and therapeutics.
Future directions include refining analytical techniques and integrating AI for deeper insights into EV biology.