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

Tagging and Fusion Proteins

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...

You might also read

Related Articles

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

Sort by

Same author

Modeling Parkinson's pathology in human iPSC dopaminergic neurons uncovers key mechanisms of Lewy body formation and heterogeneity.

Science advances·2026

Same author

Emission dipole orientation reveals dynamic single-molecule interactions with 2D crystals at solvent interfaces.

Nature communications·2026

Same author

All-water supercapacitor enabled by 1-nm clay channels.

Nature communications·2026

Same author

Cracking the code of native amyloid fibrils: advances and next steps to enable pathology-informed therapeutic and diagnostic.

Nature structural & molecular biology·2026

Same author

A genomics health strategy for the Arabian Gulf.

Nature medicine·2026

Same author

Nanopore Trap for Label-Free Fingerprinting of Surface-modified Single Nanoparticles.

Small methods·2025

Search research articles

Related Experiment Video

Updated: Jul 1, 2026

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Published on: August 3, 2018

Label-free identification of protein aggregates using deep learning.

Khalid A Ibrahim^1,2, Kristin S Grußmayer³, Nathan Riguet²

¹Laboratory of Nanoscale Biology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Nature Communications

|November 28, 2023

Summary

Researchers developed a label-free method using deep learning to detect Huntington's disease protein aggregates in living cells. This technique avoids altering protein properties, offering a more accurate way to study neurodegenerative disease dynamics.

More Related Videos

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Published on: September 12, 2019

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Published on: April 25, 2021

Related Experiment Videos

Last Updated: Jul 1, 2026

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Quantitative Cell Biology of Neurodegeneration in Drosophila Through Unbiased Analysis of Fluorescently Tagged Proteins Using ImageJ

Published on: August 3, 2018

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Characterizing Individual Protein Aggregates by Infrared Nanospectroscopy and Atomic Force Microscopy

Published on: September 12, 2019

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy

Published on: April 25, 2021

Area of Science:

Neurobiology
Biophysics
Computational Biology

Background:

Protein misfolding and aggregation are key in neurodegenerative diseases (NDDs).
Current methods using fluorescent labels can alter protein properties and aggregate characteristics.
Huntington's disease involves aggregation of the Huntingtin protein exon 1 (Httex1).

Purpose of the Study:

To develop a label-free method for identifying and analyzing neurodegenerative disease-associated protein aggregates.
To overcome limitations of fluorescent labeling in studying protein aggregation dynamics.
To enable high-fidelity analysis of Huntington's disease protein aggregation in living cells.

Main Methods:

Utilized deep learning algorithms for image analysis.
Developed a label-free identification of NDD-associated aggregates (LINA) approach.
Trained models on transmitted-light images of unlabeled Httex1 aggregates in living cells.

Main Results:

Successfully detected unlabeled and unaltered Httex1 aggregates using deep learning.
Demonstrated robustness of LINA models across various imaging conditions and Httex1 constructs.
Enabled dynamic measurement of label-free aggregate dry mass and area changes during growth.

Conclusions:

LINA provides a high-speed, specific, and simple method for analyzing protein aggregation dynamics.
This label-free approach offers high-fidelity information without altering native protein properties.
LINA is a valuable tool for studying the pathogenesis of NDDs like Huntington's disease.