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Updated: May 29, 2025

Two- and Three-Dimensional Live Cell Imaging of DNA Damage Response Proteins

Two- and Three-Dimensional Live Cell Imaging of DNA Damage Response Proteins

Published on: September 28, 2012

Profiling DNA damage in 3D Histology Samples.

Kristofer E Delas Peñas^1,2,3, Ralf Haeusler^1,2, Sally Feng⁴

¹Department of Engineering Science, University of Oxford, United Kingdom.

Medical Optical Imaging and Virtual Microscopy Image Analysis : First International Workshop, MOVI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings. MOVI (Workshop) (1St : 2022 : Singapore)

|February 3, 2025

Summary

This summary is machine-generated.

This study uses representation learning to analyze cell morphology and DNA damage markers (γH2AX) in tumors. The approach aids in differentiating treated from control tissues, offering insights into DNA repair and immune responses.

Keywords:

3D histology DNA damage cell morphology representation learning

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Last Updated: May 29, 2025

Two- and Three-Dimensional Live Cell Imaging of DNA Damage Response Proteins

Two- and Three-Dimensional Live Cell Imaging of DNA Damage Response Proteins

Published on: September 28, 2012

Visualization of miniSOG Tagged DNA Repair Proteins in Combination with Electron Spectroscopic Imaging ESI

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Published on: September 24, 2015

Advanced Confocal Microscopy Techniques to Study Protein-protein Interactions and Kinetics at DNA Lesions

Advanced Confocal Microscopy Techniques to Study Protein-protein Interactions and Kinetics at DNA Lesions

Published on: November 12, 2017

Area of Science:

Computational Biology
Cancer Research
Biomedical Imaging

Background:

Cell morphology provides insights into biological states.
Interplay of cell morphologies in tumor microenvironments enhances characterization.
γH2AX is a key marker for DNA damage.

Purpose of the Study:

To develop a representation learning approach for profiling cells based on DNA damage.
To capture similarities and differences in γH2AX+ cells using texture analysis.
To investigate the interplay between immune response (CD8+) and DNA damage (γH2AX+).

Main Methods:

Utilized Gray-Level Co-occurrence Matrix (GLCM) for texture representation.
Employed Variational Autoencoder Generative Adversarial Network (VAE-GAN) for texture analysis.
Quantified CD8+ and γH2AX+ cells at various scales to assess interplay.

Main Results:

Texture representations effectively profiled cells in singular and local neighborhood contexts.
The approach successfully differentiated between treated and control tissue regions.
Demonstrated potential for quantitative measurement of DNA damage and repair.

Conclusions:

Representation learning offers a robust method for analyzing cell morphology and DNA damage.
The developed approach aids in understanding tumor microenvironment dynamics.
This method has potential applications in quantitative cancer research and treatment assessment.