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Protocol for automated multivariate quantitative-image-based cytometry analysis by fluorescence microscopy of

Laetitia Besse1, Typhaine Rumiac2, Anne Reynaud-Angelin3

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This study introduces a protocol for quantitative-image-based cytometry (QIBC) using fluorescence microscopy. The method employs AI-driven image analysis for efficient cell segmentation and multiparametric data visualization.

Keywords:
Cell-based AssaysMicroscopySignal Transduction

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Area of Science:

  • Cell Biology
  • Biotechnology
  • Microscopy

Background:

  • Quantitative-image-based cytometry (QIBC) is a powerful technique for analyzing cellular characteristics.
  • Accurate and efficient image analysis is crucial for robust QIBC results.

Purpose of the Study:

  • To present a detailed protocol for multivariate QIBC analysis of asynchronous adherent cells.
  • To introduce an open-source Fiji script for automated image analysis and data visualization in QIBC.

Main Methods:

  • Cell preparation, treatment, fixation, staining, and fluorescence microscopy imaging.
  • Development and application of an open-source Fiji script integrating deep learning for automated nuclei segmentation.
  • Multiparametric data visualization of QIBC results.

Main Results:

  • A comprehensive protocol for QIBC analysis is described.
  • The Fiji script enables robust, automated nuclei segmentation with minimal user input.
  • The protocol facilitates efficient multiparametric data visualization.

Conclusions:

  • This protocol and associated Fiji script provide an efficient and powerful tool for multivariate QIBC analysis.
  • The integration of AI, specifically deep learning, enhances the automation and accuracy of QIBC image analysis.
  • This approach supports detailed cellular characterization through fluorescence microscopy.