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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: Apr 19, 2026

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

flowCL: ontology-based cell population labelling in flow cytometry.

Mélanie Courtot¹, Justin Meskas¹, Alexander D Diehl¹

¹Molecular Biology and Biochemistry Department, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY 14203, USA, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA, Center for Human Immunology, Autoimmunity and Inflammation, National Institutes of Health, Bethesda, MD 20892, USA, School of Dental Medicine, University at Buffalo, NY 14214-8006, USA, J. Craig Venter Institute, La Jolla, CA 92037, USA, Department of Pathology, University of California, San Diego, CA 92093, USA.

Bioinformatics (Oxford, England)

|December 7, 2014

Summary

This summary is machine-generated.

flowCL software provides automated semantic labeling of cell populations in flow cytometry data using surface markers. This enables standardized and reproducible identification of cell types, crucial for disease analysis.

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Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Accurate identification of cell populations is crucial for flow cytometry data analysis, particularly for disease-specific cell identification.
Current methods for cell population labeling lack standardization, hindering the integration of algorithmic outputs with external knowledge bases.

Purpose of the Study:

To develop a software package for automated semantic labeling of cell populations in flow cytometry data.
To enable unambiguous and reproducible identification of standardized cell types based on immunophenotype.

Main Methods:

Development of the flowCL software package.
Application of flowCL for labeling cell populations using surface marker data.
Utilized the Federation of Clinical Immunology Societies Human Immunology Project Consortium lyoplate populations as a use case.

Main Results:

flowCL performs semantic labeling of cell populations based on their surface markers.
Successful application of flowCL to a complex dataset, demonstrating its utility.

Conclusions:

Automated labeling of cell populations by flowCL facilitates standardized and reproducible cell type identification.
The software enhances the integration of flow cytometry data analysis with external biological knowledge.