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Related Experiment Video

Updated: May 18, 2026

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry

An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry

Published on: May 13, 2019

Using Cell-ID 1.4 with R for microscope-based cytometry.

Alan Bush¹, Ariel Chernomoretz, Richard Yu

¹IFIByNE-CONICET and Department of Physiology, Molecular and Cellular Biology, FCEN, University of Buenos Aires, Buenos Aires, Argentina.

Current Protocols in Molecular Biology

|October 3, 2012

Summary

This summary is machine-generated.

This study presents an open-source method for quantifying cellular features and tracking cells over time using Cell-ID software and R statistical analysis. The technique enables detailed analysis of cell volume and fluorescence localization from microscopy images.

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Last Updated: May 18, 2026

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Published on: May 13, 2019

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Area of Science:

Cell biology
Microscopy
Image analysis

Background:

Quantifying cellular features from microscopy images is crucial for biological research.
Existing methods may lack comprehensive analysis capabilities or open-source accessibility.

Purpose of the Study:

To describe a novel, open-source method for quantifying cellular features and tracking cell behavior over time.
To integrate image processing with robust statistical analysis for detailed cell analysis.

Main Methods:

Utilizes Cell-ID software for image segmentation and cell localization using defocused transmission images.
Acquires fluorescence images via wide-field epifluorescence or confocal microscopy.
Employs the R statistical programming framework with a specialized package for analyzing Cell-ID output.

Main Results:

Enables quantification of various cellular features, including cell volume.
Allows for the analysis of total and subcellular fluorescence localization.
Facilitates tracking of individual cells over time.

Conclusions:

The described method provides a comprehensive and open-source solution for cellular feature quantification and cell tracking.
Integration of Cell-ID and R offers a powerful platform for advanced cell imaging data analysis.
This approach enhances the accessibility and depth of cellular analysis in biological research.