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Super-resolution Fluorescence Microscopy

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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...

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Related Experiment Video

Updated: May 13, 2025

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

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Published on: July 15, 2020

A modular artificial intelligence framework to facilitate fluorophore design.

Yuchen Zhu¹, Jiebin Fang^2,3, Shadi Ali Hassen Ahmed¹

¹Institute of Drug Metabolism and Pharmaceutical Analysis, Research Center for Clinical Pharmacy, College of Pharmaceutical Sciences, State Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou, 310058, China.

Nature Communications

|April 15, 2025

Summary

This summary is machine-generated.

Researchers developed FLAME, an AI framework to accelerate fluorophore design for fluorescence imaging. This tool integrates databases and predictive models, enabling faster discovery of high-performance fluorescent materials.

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Last Updated: May 13, 2025

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

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Area of Science:

Chemistry
Materials Science
Computational Chemistry

Background:

Fluorescence imaging is crucial in research and medicine.
Current fluorophore development relies heavily on inefficient trial-and-error methods.
Existing fluorophores often have suboptimal performance due to complex structure-property and solvent effects.

Purpose of the Study:

To develop an AI-driven framework, FLAME, for accelerating the design of novel fluorophores.
To create a comprehensive open-source database of fluorophore-solvent pairs.
To improve the prediction accuracy and efficiency of fluorophore optical properties.

Main Methods:

Construction of FluoDB, the largest open-source fluorophore database (55,169 pairs).
Development of FLSF (Fluorescence prediction with fluoroScaFfold-driven model) using domain-knowledge fingerprints.
Integration of a molecule generator for novel compound synthesis.

Main Results:

FLAME framework successfully accelerates fluorophore design.
FLSF model accurately predicts optical properties with interpretability.
Synthesis of novel 3,4-oxazole-fused coumarins, including a highly fluorescent compound.

Conclusions:

FLAME offers a powerful AI solution for rational fluorophore design.
The developed database and prediction models significantly advance fluorophore discovery.
This work paves the way for next-generation fluorescent imaging agents.