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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: May 4, 2026

Characterization of Aquatic Biofilms with Flow Cytometry

Characterization of Aquatic Biofilms with Flow Cytometry

Published on: June 6, 2018

Flow cytometry bioinformatics.

Kieran O'Neill¹, Nima Aghaeepour¹, Josef Spidlen²

¹Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada ; Bioinformatics Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada.

Plos Computational Biology

|December 24, 2013

Summary

This summary is machine-generated.

Flow cytometry bioinformatics integrates computational statistics and machine learning to analyze complex single-cell data. This field develops tools for data preprocessing, cell population identification, and biological discovery, enhancing high-throughput analysis.

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Last Updated: May 4, 2026

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Area of Science:

Bioinformatics
Computational Biology
Biotechnology

Background:

Flow cytometry enables high-throughput quantification of multiple biomarkers on single cells.
The increasing dimensionality and throughput of flow cytometry data necessitate advanced computational analysis.
Bioinformatics applied to flow cytometry leverages computational statistics and machine learning.

Purpose of the Study:

To outline the scope and methodologies of flow cytometry bioinformatics.
To highlight the computational tools and standards crucial for analyzing flow cytometry data.
To emphasize the role of open data and software in advancing the field.

Main Methods:

Data preprocessing techniques including spectral compensation, data transformation, quality assessment, and normalization.
Cell population identification methods, from manual gating to automated approaches using dimensionality reduction and machine learning.
Advanced data characterization techniques like probability binning and combinatorial gating.

Main Results:

Development of diverse computational methods for flow cytometry data analysis, including preprocessing, population identification, and diagnosis.
Establishment of data standards (e.g., Flow Cytometry Standard) and public repositories (e.g., FlowRepository) for data sharing.
Availability of open-source software, such as Bioconductor packages and GenePattern, facilitating widespread adoption.

Conclusions:

Flow cytometry bioinformatics is essential for managing and analyzing large-scale, multidimensional single-cell data.
Computational methods and open standards are critical for reproducible and scalable flow cytometry research.
The field is continuously evolving with advancements in machine learning and data sharing initiatives.