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Flow Cytometry

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Related Experiment Video

Updated: Jun 4, 2026

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

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

Published on: December 16, 2014

Cellular analysis by open-source software for affordable cytometry.

Anja Mittag¹, Fernanda E Pinto, Denise C Endringer

¹Translational Centre for Regenerative Medicine, Leipzig, Germany.

|February 15, 2011

Summary

This summary is machine-generated.

Open-source CellProfiler software enables quantitative image cytometry analysis. This affordable tool is suitable for self-assembled microscopes, advancing cellular research and healthcare applications.

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Last Updated: Jun 4, 2026

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Published on: November 7, 2025

Area of Science:

Biomedical Engineering
Cell Biology
Computational Biology

Background:

Image cytometry is crucial for affordable healthcare and cellular research.
Developing personal, low-cost cytometers requires robust software for quantitative analysis.
Existing open-source image analysis tools may lack comparability to established cytometric programs.

Purpose of the Study:

To compare the performance of the open-source software CellProfiler (CP) with a commercial image cytometry program.
To validate CP for quantitative cytometric analysis using leukocytes and fluorescent beads.
To assess the suitability of CP for use with self-assembled, low-cost fluorescence microscopes.

Main Methods:

Leukocyte and fluorescent bead images were acquired using a Laser Scanning Cytometer.
Images were analyzed using both CellProfiler (CP) and conventional cytometer software.
Custom algorithms were developed within CP for accurate cell and bead analysis.
Key parameters like cell count and fluorescence intensity were compared between the two analysis methods.

Main Results:

CellProfiler (CP) produced results comparable to the commercial cytometer software.
High correlation was observed for hallmark parameters, including cell count and fluorescence intensity.
Developed algorithms enabled effective analysis of leukocytes and beads using CP.

Conclusions:

CellProfiler (CP) is a validated and appropriate open-source software for quantitative image cytometry.
CP facilitates affordable cellular analysis, making it suitable for self-assembled microscopes.
This validates the use of CP for advancing accessible cytometry in research and healthcare.