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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: Oct 4, 2025

High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues

High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues

Published on: September 15, 2021

Deep imaging flow cytometry.

Kangrui Huang¹, Hiroki Matsumura¹, Yaqi Zhao¹

¹Department of Chemistry, The University of Tokyo, Tokyo 113-0033, Japan. a_isozaki@chem.s.u-tokyo.ac.jp.

|February 10, 2022

Summary

This summary is machine-generated.

Deep-learning-enhanced imaging flow cytometry (dIFC) overcomes the throughput-resolution trade-off. This novel method uses AI to generate high-resolution cell images at high speeds, improving accuracy in cell analysis.

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Last Updated: Oct 4, 2025

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Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

Qualitative and Quantitative Analysis of the Immune Synapse in the Human System Using Imaging Flow Cytometry

Published on: January 7, 2019

Area of Science:

Biomedical imaging
Cell biology
Machine learning

Background:

Imaging flow cytometry (IFC) enables high-throughput single-cell analysis.
A key limitation of IFC is the trade-off between throughput, sensitivity, and spatial resolution.
Achieving high throughput typically requires lower magnification, reducing image resolution.

Purpose of the Study:

To develop a deep-learning-enhanced imaging flow cytometry (dIFC) method.
To overcome the inherent trade-off between throughput, sensitivity, and spatial resolution in IFC.
To enable high-throughput, high-resolution cell imaging and analysis.

Main Methods:

Implemented a deep learning-based image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) platform.
Developed a high-resolution (HR) image generator using a two-generative adversarial network (GAN) architecture.
Synthesized virtual HR images from low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA) at high flow speeds (2 m s^-1).

Main Results:

dIFC successfully generated high-resolution images comparable to those from high-magnification lenses (40×/0.95-NA).
Characterized dIFC using various cell types, including *Chlamydomonas reinhardtii*, Jurkat cells, and *Saccharomyces cerevisiae*.
Demonstrated enhanced accuracy in FISH-spot counting and budding yeast neck-width measurement using dIFC.

Conclusions:

dIFC effectively circumvents the throughput-sensitivity-resolution trade-off in IFC.
The developed method enables high-throughput acquisition of high-resolution cell images.
dIFC holds significant potential for advanced statistical analysis of cells with high-dimensional spatial information.