Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A human liver organoid platform for hepatotoxicity assessment: evaluation using reference compounds.

Frontiers in toxicology·2026

Same author

Glutamate excreted by LepR⁺ BM-MSCs mitigates alcohol-associated liver disease by promoting IL-1R2⁺ monocyte migration.

Clinical and molecular hepatology·2026

Same author

Efficient Direct Recycling of Spent Batteries: Integrated Lithiation and Delamination.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

The inhibitory effects of <i>Melicope pahangenesis</i> T. G. Hartley extract on LPS-induced acute lung injury.

Food science and biotechnology·2026

Same author

Highly Stable Chip-Scale Tellurium Memtransistor Based on Field-Induced Oxygen Vacancy Migration.

ACS applied materials & interfaces·2026

Same author

Gender-affirming robot-assisted sigmoid vaginoplasty: Outcomes and complications from a retrospective review of 12 cases.

Medicine·2026

Search research articles

Related Experiment Video

Updated: Jun 17, 2025

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis.

Hyung-Jun Lim¹, Gye Wan Kim², Geon Hyeock Heo²

¹Department of Chemistry, Hanyang University, Seoul, 04763, Republic of Korea.

Biosensors & Bioelectronics

|August 6, 2024

Summary

This summary is machine-generated.

A new AI tool uses super-resolution microscopy and deep learning to analyze single nanoparticles (vesicles) more accurately and faster than older methods. This advances understanding of cell communication in health and disease.

Keywords:

Cluster analysis Deep learning algorithm Exosome Super-resolution fluorescence microscopy

More Related Videos

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Super-resolution Imaging of Neuronal Dense-core Vesicles

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

Related Experiment Videos

Last Updated: Jun 17, 2025

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Published on: April 9, 2017

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Super-resolution Imaging of Neuronal Dense-core Vesicles

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

Area of Science:

Nanotechnology
Cell Biology
Artificial Intelligence

Background:

Nanoscale analysis of membrane vesicles is vital for understanding intercellular communication in health and disease.
Challenges in vesicle analysis include their small size and complex biological fluid environments.
Current methods struggle with accuracy and computational demands for single-particle vesicle analysis.

Purpose of the Study:

To develop and evaluate a novel vesicle analysis tool combining super-resolution microscopy (SRM) and deep learning.
To compare the efficacy of deep learning algorithms against traditional clustering methods for vesicle detection.
To assess the potential of AI-enhanced SRM for dissecting vesicle heterogeneity.

Main Methods:

Utilized super-resolution microscopy (SRM) for high-resolution imaging of exosomes.
Implemented and compared various deep-learning algorithms (YOLO, DETR, Deformable DETR, Faster R-CNN) against classical clustering (k-means, DBSCAN, SR-Tesseler).
Applied combined Deformable DETR and ConvNeXt-S algorithms to analyze differently labeled exosome populations.

Main Results:

The deep-learning algorithm Deformable DETR demonstrated superior accuracy and reduced processing time for detecting individual vesicles in SRM images.
AI-enhanced image-based methods significantly outperformed traditional coordinate-based clustering techniques.
The combined Deformable DETR and ConvNeXt-S algorithms successfully differentiated between differently labeled exosomes, highlighting potential for population heterogeneity analysis.

Conclusions:

Deep learning integrated with SRM offers a powerful and efficient solution for nanoscale vesicle analysis.
This AI-driven approach overcomes limitations of traditional methods, improving accuracy and reducing computational load.
The findings pave the way for advancements in vesicle biology, diagnostics, and therapeutics by enabling detailed analysis of vesicle populations.