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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: Mar 15, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

Holger Hennig¹, Paul Rees², Thomas Blasi³

¹Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, MA 02142, USA; Dept. of Systems Biology & Bioinformatics, University of Rostock, 18051 Rostock, Germany; College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK.

Methods (San Diego, Calif.)

|September 6, 2016

Summary

This summary is machine-generated.

This study introduces an open-source pipeline for analyzing imaging flow cytometry (IFC) data. It uses machine learning to unlock the full potential of cellular imaging, improving reproducibility and revealing hidden cell populations.

Keywords:

Feature selection High-throughput Imaging flow cytometry Machine learning Open-source software Profiling

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Last Updated: Mar 15, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

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Published on: January 7, 2019

Flow Cytometry Protocols for Surface and Intracellular Antigen Analyses of Neural Cell Types

Flow Cytometry Protocols for Surface and Intracellular Antigen Analyses of Neural Cell Types

Published on: December 18, 2014

Area of Science:

Biotechnology
Computational Biology
Cell Biology

Background:

Imaging flow cytometry (IFC) generates high-content image data from single cells, offering rich morphological and spatial information.
Current IFC data analysis relies on manual, subjective methods, limiting the use of available image-based features and hindering reproducibility.
Existing approaches fail to scale with the hundreds of features per cell, thus not fully utilizing the spatial and morphometric data.

Purpose of the Study:

To present an open-source software pipeline for comprehensive analysis of imaging flow cytometry data.
To enable researchers to leverage the full analytical power of IFC by utilizing machine learning algorithms on high-dimensional image data.
To overcome the limitations of manual analysis and improve the quality, reproducibility, and rigor of IFC-based research.

Main Methods:

Utilized open-source software CellProfiler to process raw imaging flow cytometry data (.rif files).
Developed an image processing pipeline within CellProfiler to identify cells and subcellular compartments for feature measurement.
Applied machine learning and clustering algorithms via CellProfiler Analyst for high-dimensional data analysis and automated cell classification.

Main Results:

The pipeline successfully extracts hundreds of morphological features from cellular images.
Machine learning algorithms enable automated classification of cell types, cell cycle phases, and experimental conditions.
The workflow facilitates the discovery of previously unappreciated cell populations based on subtle image features.

Conclusions:

This open-source workflow empowers the scientific community to fully exploit IFC data.
It enhances the ability to detect subtle cellular differences, including those in label-free channels like bright-field and dark-field.
The approach improves the scalability, reproducibility, and rigor of imaging flow cytometry data analysis.