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Subcellular Fractionation

Subcellular Fractionation

The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
Differential centrifugation is...

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Updated: Mar 30, 2026

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

Quantitative Approaches for Studying Cellular Structures and Organelle Morphology in Caenorhabditis elegans

Published on: July 5, 2019

Subcellular object quantification with Squassh3C and SquasshAnalyst.

Aurélien Rizk¹, Maysam Mansouri¹, Kurt Ballmer-Hofer¹

¹Paul Scherrer Institute, Biomolecular Research, Molecular Cell Biology, Villigen, Switzerland.

|November 12, 2015

Summary

This summary is machine-generated.

Squassh3C and SquasshAnalyst enhance quantitative image analysis for subcellular structures. These tools automate detection, segmentation, and colocalization analysis in biomedical research.

Keywords:

co-localization image analysis live-cell imaging segmentation

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Area of Science:

Biomedical research
Quantitative image analysis
Cell biology

Background:

Quantitative image analysis is crucial in modern biomedical research.
Accurate detection, segmentation, and colocalization of subcellular structures are essential.
Existing methods may lack the capacity for multi-channel or live-cell imaging analysis.

Purpose of the Study:

To introduce Squassh3C and SquasshAnalyst, advanced tools for subcellular structure analysis.
To extend the capabilities of the Squassh method for three-channel fluorescence and live-cell imaging.
To provide an integrated, user-friendly platform for comprehensive image data exploration and statistical analysis.

Main Methods:

Squassh3C: A plugin for ImageJ enabling three-channel fluorescence and live-cell movie analysis.
SquasshAnalyst: An interactive web interface for analyzing Squassh3C output data.
Integration with the R statistical environment for advanced data exploration and figure generation.

Main Results:

Squassh3C facilitates automated detection, segmentation, and quantification of subcellular structures across three channels.
SquasshAnalyst offers interactive visualization, data filtering, and statistical testing for image analysis results.
The combined workflow is compatible with Linux, MacOS X, and Microsoft Windows.

Conclusions:

Squassh3C and SquasshAnalyst provide a powerful, integrated solution for advanced quantitative image analysis.
These tools streamline the process of subcellular structure analysis, including colocalization studies.
The user-friendly interface and broad compatibility make these tools accessible for diverse biomedical research applications.