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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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Updated: Apr 18, 2026

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Published on: January 9, 2019

Deep profiling of multitube flow cytometry data.

Kieran O'Neill¹, Nima Aghaeepour², Jeremy Parker²

¹Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada Terry Fox Laboratory, BC Cancer Agency, Bioinformatics Graduate Program, University of British Columbia, Department of Hematopathology, Vancouver General Hospital and Faculty of Medical Genetics, University of British Columbia, Vancouver, Canada.

Bioinformatics (Oxford, England)

|January 21, 2015

Summary

This summary is machine-generated.

FlowBin is a new method that combines flow cytometry data from multiple tubes without needing prior knowledge. This approach enables deeper tissue profiling and discovery of novel cell types, even in complex datasets like acute myeloid leukemia.

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Last Updated: Apr 18, 2026

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Area of Science:

Biotechnology
Immunology
Computational Biology

Background:

High-throughput flow cytometry (FCM) enables deep tissue phenotyping.
Limitations in FCM include inability to measure all markers in a single sample, necessitating data recombination.
Existing methods like nearest-neighbor imputation can create artificial cell populations.

Purpose of the Study:

To present flowBin, a parameterization-free method for combining multitube FCM data.
To enable higher-dimensional data for deep profiling and discovery.
To overcome limitations of existing data imputation techniques.

Main Methods:

FlowBin allocates cells into bins based on shared markers across multiple tubes.
It computes aggregate expression for each bin within each tube.
This creates a comprehensive expression matrix for all assayed markers.

Main Results:

FlowBin successfully reproduces cell type analysis from original data for abundant cell types (>10%).
It was used with classifiers to differentiate normal from cancerous cells.
Application to NPM1-mutated acute myeloid leukemia identified novel associated cell types.

Conclusions:

FlowBin provides a robust, parameterization-free solution for multitube FCM data integration.
It facilitates deep phenotyping and discovery in complex biological systems.
The method aids in identifying disease-associated cell populations, such as in acute myeloid leukemia.