Image Analysis Platform for Comprehensive Quantification of Extracellular Vesicle Morphology

Extracellular vesicles (EVs) are membrane-encapsulated nanoparticles with key roles in cell biology and growing importance in biomedicine. However, EVs are challenging to characterize because they are highly heterogeneous. Single EV analysis methods are valuable tools for addressing this challenge. Here, we developed single EV analysis pipeline for assessment of transmission electron microscopy (TEM) and single molecule localization microscopy (SMLM) images. Our analysis of morphology and localization/density patterns from these imaging techniques identified two primary EV classes which we termed dense and complex. Compared to dense EVs, complex EVs were larger, less circular, and more elongated. For recombinant and cell culture-derived EVs, analysis revealed overall good agreement between TEM and SMLM data. Using SMLM, we further compared plasma EVs enriched in common markers, tetraspanins CD9, CD63, and CD81, with the total EV population. While both groups showed similar size and shape properties, tetraspanin-enriched EVs were on average slightly larger and exhibited a higher fraction of complex EVs. Our findings underscore the morphological diversity of EVs; highlight the benefits of multimodal imaging; and demonstrate quantitative performance of the data analysis pipeline. Our comprehensive analysis of EV morphology may ultimately help clarify EV biogenesis and advance their potential in biomedicine. STATEMENT OF SIGNIFICANCE OF THE STUDY: Extracellular vesicles (EVs) have significant roles in biology and biomedicine. However, their inherent heterogeneity in size, shape, and molecular composition presents a major challenge for their characterization. To help address this challenge, we advanced a robust image analysis pipeline. We used this pipeline to analyze transmission electron microscopy and single molecule localization microscopy images, and we comprehensively quantified morphology of single EVs. By classifying EVs into distinct morphological subtypes, dense and complex, we reveal consistent trends across recombinant, cell line-derived, and plasma EVs. Importantly, we demonstrate strong agreement between imaging modalities. Our approach enables high-resolution analysis of individual EVs and offers insights into their structural diversity. This work advances the methodological toolkit for EV research and lays the foundation for improved characterization of EVs in biology and biomedicine.

Authors

Institutions

Publication Details

Journal
PROTEOMICS
Published
2026-02-26
DOI
https://doi.org/10.1002/pmic.70113
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Image Analysis Platform for Comprehensive Quantification of Extracellular Vesicle Morphology

B. A. Purnell, Alexandre Kitching, András Saftics, Eugenia Romano et al.
PROTEOMICS
Extracellular vesicles in disease
article

Image Analysis Platform for Comprehensive Quantification of Extracellular Vesicle Morphology

B. A. Purnell, Alexandre Kitching, András Saftics, Eugenia Romano, Jiang Nan, Tijana Jovanovic-Talisman, Alexander Spark, Mihajlo Radmilovic
article en

Abstract

Extracellular vesicles (EVs) are membrane-encapsulated nanoparticles with key roles in cell biology and growing importance in biomedicine. However, EVs are challenging to characterize because they are highly heterogeneous. Single EV analysis methods are valuable tools for addressing this challenge. Here, we developed single EV analysis pipeline for assessment of transmission electron microscopy (TEM) and single molecule localization microscopy (SMLM) images. Our analysis of morphology and localization/density patterns from these imaging techniques identified two primary EV classes which we termed dense and complex. Compared to dense EVs, complex EVs were larger, less circular, and more elongated. For recombinant and cell culture-derived EVs, analysis revealed overall good agreement between TEM and SMLM data. Using SMLM, we further compared plasma EVs enriched in common markers, tetraspanins CD9, CD63, and CD81, with the total EV population. While both groups showed similar size and shape properties, tetraspanin-enriched EVs were on average slightly larger and exhibited a higher fraction of complex EVs. Our findings underscore the morphological diversity of EVs; highlight the benefits of multimodal imaging; and demonstrate quantitative performance of the data analysis pipeline. Our comprehensive analysis of EV morphology may ultimately help clarify EV biogenesis and advance their potential in biomedicine. STATEMENT OF SIGNIFICANCE OF THE STUDY: Extracellular vesicles (EVs) have significant roles in biology and biomedicine. However, their inherent heterogeneity in size, shape, and molecular composition presents a major challenge for their characterization. To help address this challenge, we advanced a robust image analysis pipeline. We used this pipeline to analyze transmission electron microscopy and single molecule localization microscopy images, and we comprehensively quantified morphology of single EVs. By classifying EVs into distinct morphological subtypes, dense and complex, we reveal consistent trends across recombinant, cell line-derived, and plasma EVs. Importantly, we demonstrate strong agreement between imaging modalities. Our approach enables high-resolution analysis of individual EVs and offers insights into their structural diversity. This work advances the methodological toolkit for EV research and lays the foundation for improved characterization of EVs in biology and biomedicine.

PROTEOMICS
City of Hope (US), ZeptoMetrix (United States) (US)
Openalex Percentile: Top 39%
Extracellular vesicles in disease
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.