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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: Jun 7, 2025

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

PunctaFinder: An algorithm for automated spot detection in fluorescence microscopy images.

Hanna M Terpstra¹, Rubén Gómez-Sánchez², Annemiek C Veldsink³

¹Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, 9747 AG, Groningen, The Netherlands.

Molecular Biology of the Cell

|November 13, 2024

Summary

This summary is machine-generated.

PunctaFinder is a new Python algorithm that automatically detects small bright spots (puncta) in fluorescence microscopy images. It also defines cellular regions, enabling quantification of molecule localization, even in low-quality images.

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Last Updated: Jun 7, 2025

Rapid Analysis and Exploration of Fluorescence Microscopy Images

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Published on: March 19, 2014

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

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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

Area of Science:

Cell Biology
Biophysics
Image Analysis

Background:

Fluorescence microscopy generates complex data requiring automated analysis.
Existing methods struggle with raw, low-signal, or low-resolution images.

Purpose of the Study:

Introduce PunctaFinder, a Python algorithm for puncta detection in raw fluorescence microscopy images.
Enable quantification of target molecule localization within cellular contexts.

Main Methods:

Developed a novel Python-based algorithm, PunctaFinder.
Algorithm processes raw images without denoising or enhancement.
Detects puncta and defines cytoplasmic regions.

Main Results:

PunctaFinder successfully detected various puncta (e.g., vesicles, lipid droplets, aggregates, nuclear pore complexes).
The algorithm excels in low-resolution and low signal-to-noise ratio images.
Identified dim puncta and puncta of dynamic target molecules.

Conclusions:

PunctaFinder provides robust and efficient punctum quantification in fluorescence microscopy.
Enables automated analysis where previously not possible.
A valuable tool for researchers studying subcellular structures and molecular localization.