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Related Concept Videos

In-situ Hybridization02:31

In-situ Hybridization

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In situ hybridization (ISH) is a technique used to detect and localize specific DNA or RNA molecules in cells, tissue, or tissue sections using a labeled probe. The technique was first used in 1969 for the investigation of nucleic acids. It is currently an essential tool in scientific research and clinical settings, especially for diagnostic purposes.
Types of probes and labels
A probe is a complementary strand of DNA or RNA that binds to corresponding nucleotide sequences in a cell. Many...
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FISH - Fluorescent In-situ Hybridization02:07

FISH - Fluorescent In-situ Hybridization

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Fluorescence in situ hybridization, or FISH, was developed in the early 1980s and has quickly become one of the most widely used techniques in cytogenetics. Labeled probes are used to bind complementary DNA or RNA sequences on a chromosome or in a region within a cell. Earlier, the probes could only be obtained by cloning or reverse transcription of a DNA template. Currently, the probe oligonucleotides can be synthesized synthetically. Additionally, with the advancement of optical techniques,...
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Related Experiment Video

Updated: Jun 11, 2025

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae
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Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.

Zaka Ur Rehman1, Mohammad Faizal Ahmad Fauzi1, Wan Siti Halimatul Munirah Wan Ahmad1,2

  • 1Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia.

Diagnostics (Basel, Switzerland)
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

Automated computational methods for analyzing in situ hybridization (ISH) images significantly improve breast cancer diagnosis. These techniques, especially deep learning, enhance accuracy and reduce manual labor in HER2 gene amplification assessment.

Keywords:
deep learningfluorescent in situ hybridization (FISH)human epidermal growth factor receptor 2 (HER2)pathologiessilver-enhanced in situ hybridization (SISH)

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Area of Science:

  • Digital pathology
  • Computational imaging
  • Cancer biomarker analysis

Background:

  • HER2 amplification is a critical prognostic marker in 20-25% of breast cancers.
  • In situ hybridization (ISH) imaging is used to assess HER2 status, but faces challenges due to image heterogeneity and complex biomarker detection.
  • Computational techniques are increasingly applied to digital pathology for enhanced cancer diagnosis.

Purpose of the Study:

  • To review semi-automated and fully automated computational methods for analyzing ISH images, focusing on HER2 gene amplification.
  • To compare conventional machine learning with deep learning approaches for ISH image analysis.
  • To assess the potential of these methods for routine pathology and clinical adoption.

Main Methods:

  • Comprehensive literature review from 1997 to 2023 on computational analysis of ISH images, with emphasis on silver-enhanced in situ hybridization (SISH).
  • Analysis of image processing, machine learning, and deep learning techniques applied to HER2 gene amplification detection.
  • Comparison of automated versus manual methods in terms of cost-effectiveness, scalability, and diagnostic accuracy.

Main Results:

  • Automated ISH analysis, particularly with bright-field microscopy, offers a cost-effective and scalable solution for pathology.
  • Deep learning techniques show potential for improving diagnostic accuracy in HER2 status evaluation compared to conventional methods.
  • Challenges remain in handling data variability and computational demands for deep learning in ISH analysis.

Conclusions:

  • Automated ISH analysis reduces manual workload and enhances diagnostic accuracy for HER2 gene amplification.
  • Deep learning integration holds promise for advancing computational pathology, but requires further refinement.
  • Future research should focus on optimizing computational methods for complex HER2 evaluation and promoting clinical integration.