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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: Nov 25, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Protein subcellular localization based on deep image features and criterion learning strategy.

Ran Su¹, Linlin He¹, Tianling Liu¹

¹School of Computer Software, College of Intelligence and Computing, Tianjin University, China.

Briefings in Bioinformatics

|December 15, 2020

Summary

This summary is machine-generated.

This study introduces a deep learning method using convolutional neural networks (CNNs) for accurate protein subcellular localization from images. The approach effectively predicts single and multi-label locations, outperforming traditional feature methods.

Keywords:

criterion learning deep neural network phenotype features protein subcellular location

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Area of Science:

Cell Biology
Bioinformatics
Computational Biology

Background:

Understanding protein spatial distribution at the subcellular level is crucial for deciphering protein functions and advancing human biology and medicine.
Imaging techniques are vital for predicting protein subcellular localization, but deep learning applications remain underexplored.

Purpose of the Study:

To develop and evaluate a novel deep imaging-based approach for predicting protein subcellular localization.
To accurately predict both single-label and challenging multi-label protein locations using deep learning.

Main Methods:

Utilized convolutional neural networks (CNNs) to extract deep image features for protein localization.
Developed a criterion learning strategy to leverage label-attribute and label-label relevancy for multi-label prediction.
Identified an optimal CNN architecture for maximizing prediction accuracy.

Main Results:

The deep imaging-based approach accurately predicted protein subcellular locations.
Deep features extracted by CNNs provided more accurate predictions with fewer features compared to hand-crafted features.
The criterion learning strategy effectively addressed the complexities of multi-label protein localization.

Conclusions:

Deep learning, specifically CNNs, offers a powerful and efficient method for protein subcellular localization.
The developed approach enhances the accuracy and reduces feature requirements for predicting protein locations.
This work provides a valuable tool for biological and medical research by improving our understanding of protein spatial organization.