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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 22, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

A computational framework to emulate the human perspective in flow cytometric data analysis.

Surajit Ray¹, Saumyadipta Pyne

¹Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, United States of America. sray@math.bu.edu

|May 8, 2012

Summary

This summary is machine-generated.

flowScape enhances automated flow cytometry analysis by incorporating human-like pattern recognition for cell populations. This computational cytomics framework offers robust, flexible, and sample-specific analysis beyond traditional methods.

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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

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Published on: March 24, 2023

Area of Science:

Computational Cytomics
Bioinformatics
Data Science

Background:

Automated flow cytometry data analysis methods often neglect the crucial human perspective inherent in manual gating.
Existing automated techniques may fail with real-world samples due to arbitrary cell population shapes and inter-sample variability.
Pre-specified distribution assumptions in automated analysis are often inadequate for complex biological data.

Purpose of the Study:

To develop a novel framework, flowScape, that emulates key aspects of the human perspective in flow cytometry data analysis.
To provide a more flexible and robust computational approach for analyzing high-dimensional flow data.
To improve the identification and characterization of cell populations in complex biological samples.

Main Methods:

Created a mathematically rigorous map of high-dimensional flow data landscapes using modal clusters and hierarchical structures.
Implemented ridgeline analysis for landscape traversal and cell population isolation at various resolutions.
Extended manual gating with template construction based on relative parameters for flexible, sample-specific, batch-mode population detection.

Main Results:

flowScape successfully emulates human analytical strategies, offering a new approach to flow cytometry data interpretation.
The framework demonstrates superior performance compared to existing analytical methods in flow data analysis applications.
Relative parameter-based templates enable robust and flexible identification of target cell populations across diverse samples.

Conclusions:

The human perspective, combining intuition and experience, is vital for accurate flow cytometry analysis.
flowScape integrates human-like analytical approaches with automation and mathematical rigor, creating a powerful computational tool.
This framework advances computational cytomics by providing a flexible and robust solution for complex flow data challenges.