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ColocalizR: An open-source application for cell-based high-throughput colocalization analysis.

Allan Sauvat1, Marion Leduc1, Kevin Müller1

  • 1Université Paris Descartes, Sorbonne Paris Cité, Paris, France; Equipe 11 Labellisée Ligue Nationale Contre le Cancer, Centre de Recherche des Cordeliers, Paris, France; Institut National de la Santé et de la Recherche Médicale, U1138, Paris, France; Université Pierre et Marie Curie, Paris, France; Metabolomics and Cell Biology Platforms, Gustave Roussy Cancer Campus, Villejuif, France.

Computers in Biology and Medicine
|March 11, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces ColocalizR, an R application for automated cell segmentation and colocalization analysis. It enables cell-by-cell quantification of fluorescent signal overlap, facilitating statistical analysis of cellular populations.

Keywords:
Cellular imagingCo-distributionCo-occurrenceFluorescence microscopySystems biology

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Area of Science:

  • Cell biology
  • Biophysics
  • Bioinformatics

Background:

  • Microscopic assessment of fluorescent signal colocalization is crucial in cell biology.
  • Current quantification methods (e.g., Pearson, Manders coefficients) lack advancements despite imaging improvements.
  • Need for automated, quantitative colocalization analysis at the single-cell level.

Purpose of the Study:

  • To develop an R-based application, ColocalizR, for automated cell and subcellular segmentation.
  • To enable measurement of morphology and texture features alongside colocalization.
  • To facilitate cell-by-cell colocalization analysis for statistical assessment of cellular populations.

Main Methods:

  • Development of an R-based application with a graphical user interface.
  • Automated cell and subcellular segmentation algorithms.
  • Calculation of colocalization degree on a per-cell basis.
  • Parallelized task processing for large datasets.
  • Export functionality for diverse statistical analyses.

Main Results:

  • ColocalizR enables automated cell segmentation and feature extraction.
  • Quantitative colocalization analysis is performed on a cell-by-cell basis.
  • The application supports statistical analysis of cellular populations and subpopulations.
  • Designed for parallel processing, enhancing efficiency with large datasets.
  • User-friendly interface suitable for researchers without extensive image analysis expertise.

Conclusions:

  • ColocalizR provides a novel, automated approach to quantitative colocalization analysis in cell biology.
  • Enables high-throughput, cell-specific analysis of fluorescent signal overlap.
  • Facilitates advanced statistical investigations into cellular heterogeneity.
  • Freely available application promotes accessibility and reproducibility in research.