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Updated: Jun 30, 2025

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
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Cell segmentation in images without structural fluorescent labels.

Daniel Zyss1,2,3,4, Susana A Ribeiro4, Mary J C Ludlam4

  • 1Centre for Computational Biology (CBIO), Mines Paris, PSL University, Paris, France.

Biological Imaging
|March 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new segmentation workflow for high-content screening (HCS) assays. It enhances biological insights by reducing reliance on structural fluorescent labels (FLs), freeing up imaging channels.

Keywords:
cell biologycell segmentationfluorescence microscopyhigh-content screeningimage-based cellular assays

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Area of Science:

  • Cellular imaging
  • High-content screening (HCS)
  • Fluorescence microscopy

Background:

  • High-content screening (HCS) is vital for understanding drug mechanisms of action.
  • Successful HCS relies on careful selection of fluorescent labels (FLs).
  • Current HCS assays use biological and structural FLs, but limited imaging channels restrict multiplexing.

Purpose of the Study:

  • To develop a novel segmentation workflow for HCS assays.
  • To overcome the dependency on structural FLs for image segmentation.
  • To maximize the biological information content in HCS assays.

Main Methods:

  • Fine-tuning pre-trained generalist cell segmentation models.
  • Extracting structural information from biological FLs.
  • Aggregating segmentation results from multiple FLs.

Main Results:

  • The proposed workflow successfully extracts structural information from biological readouts.
  • Performance and robustness improvements were confirmed across various segmentation strategies, acquisition methods, cell lines, and FLs.
  • The method frees up two fluorescence microscopy channels for biologically relevant FLs.

Conclusions:

  • The developed segmentation workflow enhances HCS assay capabilities by freeing imaging channels.
  • This approach maximizes biological information content without compromising computational single-cell profiling accuracy.
  • It offers a robust and accurate method for advanced cellular analysis in drug discovery.