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Related Concept Videos

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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Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
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RS-FISH: precise, interactive, fast, and scalable FISH spot detection.

Ella Bahry1, Laura Breimann1,2, Marwan Zouinkhi1,3

  • 1Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany.

Nature Methods
|November 17, 2022
PubMed
Summary
This summary is machine-generated.

We developed Radial Symmetry-FISH (RS-FISH), a user-friendly software for accurate fluorescent spot detection in biological images. This tool enhances spatial genomics and transcriptomics analysis by efficiently processing large datasets.

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Area of Science:

  • Genomics and Epigenetics
  • Bioimaging and Computational Biology

Background:

  • Fluorescent in-situ hybridization (FISH) methods are crucial for extracting spatial genetic and epigenetic information.
  • Accurate detection of fluorescent spots in microscopy images is a significant challenge in FISH-based analyses.

Purpose of the Study:

  • To introduce Radial Symmetry-FISH (RS-FISH), a novel software for accurate, fast, and user-friendly fluorescent spot detection.
  • To provide a scalable solution for analyzing large 2D and 3D image datasets from cleared or expanded biological samples.

Main Methods:

  • Development of the RS-FISH software utilizing radial symmetry principles for spot detection.
  • Implementation of interactive parameter tuning for user-defined adjustments.
  • Integration of distributed processing capabilities for scalability across workstations, clusters, and cloud environments.

Main Results:

  • RS-FISH demonstrates high detection accuracy and low localization error across varying signal-to-noise ratios.
  • The software efficiently handles large datasets and complex 3D image volumes.
  • RS-FISH is suitable for demanding applications like single-molecule FISH, spatial transcriptomics, and spatial genomics.

Conclusions:

  • RS-FISH offers a robust and efficient solution for fluorescent spot detection in bioimaging.
  • The software significantly improves the analysis of spatial omics data.
  • RS-FISH facilitates advanced genetic and epigenetic research through enhanced image analysis capabilities.