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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: Feb 9, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Ghost cytometry.

Sadao Ota^1,2,3, Ryoichi Horisaki^3,4, Yoko Kawamura^5,2

¹Thinkcyte Inc., 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan. sadaota@solab.rcast.u-tokyo.ac.jp.

Science (New York, N.Y.)

|June 16, 2018

Summary

This summary is machine-generated.

Ghost cytometry uses a single-pixel detector and computational methods to image cells without traditional detectors. This novel technique enables high-throughput cell classification and sorting based on morphology alone.

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Last Updated: Feb 9, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

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Published on: January 27, 2023

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Flow Cytometry Purification of Mouse Meiotic Cells

Flow Cytometry Purification of Mouse Meiotic Cells

Published on: April 15, 2011

Area of Science:

Biophotonics
Cellular Imaging
Computational Biology

Background:

Conventional flow cytometry relies on spatially resolving detectors for cell analysis.
High-throughput cell classification and sorting based on morphology, without biomarkers, remain challenging.

Purpose of the Study:

To introduce ghost cytometry, an image-free ultrafast fluorescence imaging cytometry.
To demonstrate label-free cell morphology analysis and sorting using ghost cytometry.

Main Methods:

Utilizing ghost imaging principles with a single-pixel detector and a static random optical pattern.
Compressively converting spatial information from cell motion into sequential signals.
Reconstructing cell morphology computationally using temporal waveforms and pattern intensity.
Applying machine learning directly on compressed waveforms for image-free cytometry.

Main Results:

Successful computational reconstruction of cell morphology.
Demonstrated efficient, image-free, morphology-based cytometry using machine learning on compressed data.
Achieved accurate and high-throughput cell classification and selective sorting based on morphology.
Showcased the potential for label-free cell analysis, overcoming limitations of conventional methods.

Conclusions:

Ghost cytometry offers a compact, inexpensive, and effective alternative for cell analysis.
This technique enables high-throughput, biomarker-independent cell classification and sorting.
Image-free ghost cytometry advances ultrafast fluorescence imaging and cytometry applications.