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

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...

You might also read

Related Articles

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

Sort by

Same author

C26:0-lysophosphatidylcholine in X-linked adrenoleukodystrophy.

Pharmacology & therapeutics·2026

Same author

Author Correction: Development of the fluorescent probe CenSpark for labeling centrioles and cilia.

Nature chemical biology·2026

Same author

HAK-actin, a U-ExM-compatible probe to image the actin cytoskeleton.

Cell reports methods·2026

Same author

Development of the fluorescent probe CenSpark for labeling centrioles and cilia.

Nature chemical biology·2026

Same author

Tmn blocks phage spread via plasmolysis and triggers synergistic defence responses.

bioRxiv : the preprint server for biology·2026

Same author

The Grey Zone Project: Risk-Based Classification of ABCD1 Variants in X-Linked Adrenoleukodystrophy.

Journal of inherited metabolic disease·2026

Search research articles

Related Experiment Video

Updated: Apr 12, 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

Machine Learning-Assisted Quantification of Organelle Abundance.

Alexander James Long¹, Diogo Candeias^2,3, Nicki Frederick Coveña⁴

¹School of Biological Sciences, University of Southampton, Southampton, UK.

|March 13, 2026

Summary

This summary is machine-generated.

This study introduces an automated machine learning algorithm for quantifying organelle abundance using microscopy. The tool standardizes analysis of organelle number, area, and density, reducing user bias in cell biology research.

Keywords:

Abundance Density Fiji Machine learning Organelle PeroxiSPY Peroxisome Quantification Stress granule WEKA segmentation

More Related Videos

Author Spotlight: High-Throughput Image-Based Quantification of Mitochondrial DNA Synthesis and Distribution

Author Spotlight: High-Throughput Image-Based Quantification of Mitochondrial DNA Synthesis and Distribution

Published on: May 5, 2023

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Related Experiment Videos

Last Updated: Apr 12, 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

Author Spotlight: High-Throughput Image-Based Quantification of Mitochondrial DNA Synthesis and Distribution

Author Spotlight: High-Throughput Image-Based Quantification of Mitochondrial DNA Synthesis and Distribution

Published on: May 5, 2023

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Area of Science:

Cell Biology
Microscopy
Computational Biology

Background:

Organelle abundance is crucial for understanding organelle formation and function.
Current quantification methods using confocal microscopy rely on user-defined intensity thresholds, introducing bias and variability.
Automated analysis is needed to standardize organelle quantification.

Purpose of the Study:

To develop and present a machine learning-assisted algorithm for automated organelle abundance quantification.
To standardize threshold selection and minimize user intervention in microscopy-based organelle analysis.
To demonstrate the algorithm's utility across various organelle types and sample organisms.

Main Methods:

Utilized machine learning with the WEKA segmentation plugin on the open-source Fiji platform.
Developed an automated algorithm to quantify organelle number, area, and density from fluorescence intensity.
Trained the algorithm on sample datasets for application to images acquired with identical imaging parameters.

Main Results:

Successfully automated the quantification of organelle number, area, and density.
Demonstrated applicability to both membrane-bound (e.g., peroxisomes) and non-membrane-bound organelles (e.g., lipid droplets, stress granules).
Validated the approach in human cells and whole fish samples.

Conclusions:

The developed algorithm provides an automated, standardized, and user-independent method for organelle abundance quantification.
This open-source tool enhances the reliability and reproducibility of microscopy-based organelle studies.
The machine learning approach offers a robust solution for analyzing diverse cellular compartments.