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Live cell fluorescence microscopy-an end-to-end workflow for high-throughput image and data analysis.

Jakub Zahumensky1, Jan Malinsky1

  • 1Department of Functional Organization of Biomembranes, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, Videnska 1083, 14220 Prague, Czech Republic.

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Summary

This study introduces a gentle cell immobilization method and an automated analysis workflow for fluorescence microscopy. The new approach improves efficiency and accuracy in biological sample analysis, reducing manual bias.

Keywords:
CellposeFijiImageJautomatedblindedcellsdata analysismicroscopyunbiasedyeast

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Area of Science:

  • Cell Biology
  • Microscopy Techniques
  • Bioimaging Analysis

Background:

  • Fluorescence microscopy is crucial for analyzing protein localization, intensity, and cell morphology.
  • Traditional cell immobilization uses chemicals, causing stress and potential bias.
  • Manual segmentation and measurement in microscopy are time-consuming and subjective.

Purpose of the Study:

  • To develop a gentler cell immobilization technique for microscopy.
  • To establish an automated workflow for unbiased, high-throughput image analysis.
  • To enhance the accuracy and efficiency of fluorescence microscopy research.

Main Methods:

  • Gentle cell immobilization using agarose blocks on coverslips for cell-walled organisms.
  • Cell segmentation via Cellpose software.
  • Automated quantitative analysis using custom Fiji (ImageJ) macros and R markdown scripts.

Main Results:

  • The workflow enables live imaging under near-natural conditions, allowing chemical additions during time-lapse studies.
  • Automated analysis of multiple parameters (localization, intensity, morphology) is achieved.
  • Facilitates unbiased batch analysis of large microscopy datasets.

Conclusions:

  • The developed protocol and analysis workflow significantly improve the efficiency and accuracy of fluorescence microscopy.
  • This method is suitable for cell-walled cells and applicable to various cell types.
  • Reduces manual bias and enables robust quantitative analysis of biological samples.