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FISH - Fluorescent In-situ Hybridization02:07

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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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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.
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Irradiation of a spin-active nucleus causes an increase or decrease in the signal intensity of neighboring nuclei that are not necessarily chemically bonded or involved in J-coupling. This phenomenon, called the nuclear Overhauser enhancement (NOE), results from through-space interactions between the nuclear spins. The NOE effect decreases with increasing internuclear distance and is generally not observed beyond 4 angstroms. In NOE, dipole-dipole interactions between neighboring spin-active...
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When cells are placed in a hypotonic (low-salt) fluid, they can swell and burst. Meanwhile, cells in a hypertonic solution—with a higher salt concentration—can shrivel and die. How do fish cells avoid these gruesome fates in hypotonic freshwater or hypertonic seawater environments?
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Detection of Viral RNA by Fluorescence in situ Hybridization FISH
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Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear

Annamaria Csizmadia1,2, Bela Molnar2,3, Marianna Dimitrova Kucarov4

  • 1Doctoral School of Pathological Sciences, Semmelweis University, H-1085 Budapest, Hungary.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

AI-based 3D nuclear segmentation significantly improves fluorescence in situ hybridization (FISH) accuracy in challenging lymphoma samples. This advanced approach enhances nuclear detection and gene aberration classification in digital pathology workflows.

Keywords:
CellposeFISHQuantNucleAIzerStarDistfluorescence in situ hybridizationfollicular lymphomanuclear segmentationoverlapping nuclei

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Area of Science:

  • Digital pathology
  • Biomedical imaging
  • Genomics

Background:

  • Accurate nuclear segmentation is crucial for interpreting fluorescence in situ hybridization (FISH) results.
  • Traditional 2D automated algorithms struggle with dense or overlapping nuclei in samples like lymphomas, losing vital spatial depth information.
  • This limitation impacts the reliability of diagnostic FISH analyses.

Purpose of the Study:

  • To evaluate if AI-based 3D nuclear segmentation can enhance the accuracy, reproducibility, and diagnostic reliability of FISH analysis.
  • To compare the performance of different AI algorithms (NucleAIzer, StarDist, Cellpose) and traditional methods for nuclear segmentation in FISH.

Main Methods:

  • Formalin-fixed follicular lymphoma sections were labeled for BCL2 gene rearrangements using FISH.
  • Sections were scanned in multilayer Z-stacks to capture 3D information.
  • AI algorithms (NucleAIzer, StarDist, Cellpose) and FISHQuant were compared against manual eye control for nuclear segmentation accuracy.

Main Results:

  • 2D segmentation methods and FISHQuant showed limitations with dense nuclei and low-intensity signals.
  • AI-driven 3D segmentation improved nuclear separation and signal localization across focal planes.
  • NucleAIzer and StarDist demonstrated superior precision, reduced variance (VP/VS ≈ 0.96), and strong gene spot correlation (r > 0.82).

Conclusions:

  • Inaccurate nuclear segmentation hinders automated FISH signal evaluation.
  • Deep learning 3D segmentation models, specifically NucleAIzer and StarDist, overcome limitations of 2D methods.
  • These AI approaches enhance nuclear detection consistency, leading to improved classification of gene aberrations in automated digital pathology.