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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: Nov 18, 2025

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM

Published on: June 28, 2017

Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection.

Shaobo Luo^1,2, Kim Truc Nguyen^2,3, Binh T T Nguyen³

¹ESIEE, Universite Paris-Est, Noisy-le-Grand Cedex, France.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|February 7, 2021

Summary

A new deep learning system using imaging flow cytometry accurately detects Cryptosporidium and Giardia in water. This high-throughput method offers rapid and precise bioparticle analysis for environmental monitoring.

Keywords:

cell classification convolutional neural network deep learning imaging flow cytometry

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A Modified EPA Method 1623 that Uses Tangential Flow Hollow-fiber Ultrafiltration and Heat Dissociation Steps to Detect Waterborne Cryptosporidium and Giardia spp.

A Modified EPA Method 1623 that Uses Tangential Flow Hollow-fiber Ultrafiltration and Heat Dissociation Steps to Detect Waterborne Cryptosporidium and Giardia spp.

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High-throughput Measurement of Dictyostelium discoideum Macropinocytosis by Flow Cytometry

High-throughput Measurement of Dictyostelium discoideum Macropinocytosis by Flow Cytometry

Published on: September 10, 2018

Area of Science:

Environmental Science
Biotechnology
Microbiology

Background:

Imaging flow cytometry generates vast data, challenging analysis of similar microorganisms.
Accurate detection of waterborne pathogens like Cryptosporidium and Giardia is crucial for public health.

Purpose of the Study:

To develop a high-throughput system for rapid and accurate detection of Cryptosporidium and Giardia in drinking water.
To leverage deep learning for enhanced bioparticle image analysis.

Main Methods:

Integration of imaging flow cytometry with an efficient artificial neural network (MCellNet).
Development of a deep learning model for high-speed classification of microorganisms.
Validation of the system's performance in detecting specific waterborne parasites.

Main Results:

Achieved a classification accuracy exceeding 99.6% for Cryptosporidium and Giardia.
Demonstrated high sensitivity (97.37%) and specificity (99.95%) in detection.
Reached analysis speeds of 346 frames per second, surpassing existing deep learning algorithms.

Conclusions:

The MCellNet-based system provides a rapid, accurate, and high-throughput solution for bioparticle detection.
This technology has significant potential for environmental monitoring and biosensing applications.
The system overcomes data analysis challenges posed by imaging flow cytometry for similar morphologies.