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

Search research articles

Related Experiment Videos

RAMTaB: robust alignment of multi-tag bioimages.

Shan-e-Ahmed Raza¹, Ahmad Humayun, Sylvie Abouna

¹Department of Computer Science, University of Warwick, Coventry, United Kingdom.

|February 25, 2012

Summary

We developed a novel method for aligning multi-tag fluorescence images, improving protein co-localization studies. Our approach ensures accurate image registration and maximizes overlap, crucial for understanding cellular protein networks.

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...

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

You might also read

Related Articles

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

Sort by

Same author

ShapeSpaceExplorer: Analysis of morphological transitions in migrating cells using similarity-based shape space mapping.

PLoS computational biology·2026

Same author

Novel growth pattern-specific digital marker of TILs improves stratification of lung adenocarcinoma patients.

The Journal of pathology·2025

Same author

From traditional to deep learning approaches in whole slide image registration: A methodological review.

Journal of pathology informatics·2025

Same author

Out of sight, but not out of mind: Key issues regarding seafloor macrolitter monitoring.

Marine pollution bulletin·2025

Same author

Exploring the feasibility of AI-based analysis of histopathological variability in salivary gland tumours.

Scientific reports·2025

Same author

Development and validation of an artificial intelligence-based pipeline for predicting oral epithelial dysplasia malignant transformation.

Communications medicine·2025

Area of Science:

Biophysics
Cell Biology
Bioimaging

Background:

Advanced microscopy enables visualization of multiple biomolecules within a single field.
Accurate co-localization analysis relies on molecules being in close proximity.
Existing methods lack simultaneous registration and optimal reference image selection for multi-tag bioimages.

Purpose of the Study:

To present a novel approach for aligning images in multi-tag fluorescence image stacks.
To address the challenge of simultaneous registration and reference image selection.
To maximize overall overlap in multi-tag bioimaging.

Main Methods:

A block-based registration method with a confidence measure for accuracy.
A shift metric to select the Reference Image with Maximal Overlap (RIMO).

Related Experiment Videos

The Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework.

Main Results:

RAMTaB demonstrates robustness to contrast and illumination variations.
The method achieves sub-pixel accuracy in image registration.
Successful reference image selection minimizes non-overlapping signal and improves co-localization studies.

Conclusions:

Accurate alignment of multi-tag fluorescence images is essential for discovering protein complexes and networks.
The proposed framework delivers precise alignment for both real and synthetic data.
Future work will apply this to analyze molecular co-expression in normal and diseased tissues.