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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: May 5, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Hyperspectral cytometry.

Gérald Grégori¹, Bartek Rajwa, Valery Patsekin

¹Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, 47907, USA.

Current Topics in Microbiology and Immunology

|November 26, 2013

Summary

This summary is machine-generated.

Hyperspectral cytometry, a novel single-cell analysis technique, uses spectral unmixing for more accurate data. This technology, combining spectroscopy and flow cytometry, offers advanced bioparticle characterization.

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Area of Science:

Biotechnology
Analytical Chemistry
Cell Biology

Background:

Hyperspectral cytometry merges ultrafast optical spectroscopy with flow cytometry for advanced single-cell analysis.
It utilizes diffraction gratings or prism-based monochromators to disperse fluorescence signals from multiple labels onto linear detector arrays.
Recent advancements in sensitive photomultiplier tube arrays have enabled the development of functional prototypes and commercial systems.

Purpose of the Study:

To provide a historical overview of spectral cytometry development at Purdue University Cytometry Laboratories.
To describe the technology behind Sony Corporation's first commercial spectral cytometry system, the Sony SP6800.
To introduce spectral data analysis, highlighting differences from traditional polychromatic cytometry.

Main Methods:

Spectral cytometry systems disperse fluorescence signals from labeled bioparticles onto detector arrays.
Data are spectrally unmixed using algorithms like constrained Poisson regression or non-negative matrix factorization, differing from traditional compensation methods.
Analysis involves characterizing each cell based on its spectral signature.

Main Results:

The development of sensitive photomultiplier tube arrays has led to the creation of functional spectral cytometry prototypes.
Commercial spectral cytometry systems, such as the Sony SP6800, are now available.
Spectral unmixing offers an alternative to traditional compensation in cytometry.

Conclusions:

Hyperspectral cytometry represents a significant advancement in single-cell analysis technology.
The commercialization of spectral cytometry systems marks a new era in bioparticle characterization.
Understanding spectral data analysis is crucial for utilizing this emerging technology effectively.