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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: Dec 27, 2025

A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

A Rapid Method for Multispectral Fluorescence Imaging of Frozen Tissue Sections

Published on: March 30, 2020

Virtual-freezing fluorescence imaging flow cytometry.

Hideharu Mikami¹, Makoto Kawaguchi², Chun-Jung Huang^2,3

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

Nature Communications

|March 7, 2020

Summary

This summary is machine-generated.

A novel optomechanical imaging method enhances imaging flow cytometry (IFC) by virtually freezing cells, enabling high-throughput analysis without compromising sensitivity or resolution for cell biology research.

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Area of Science:

Biomedical Engineering
Cell Biology
Microscopy

Background:

Imaging flow cytometry (IFC) is crucial for cell analysis in fields like cancer biology and immunology.
Current IFC performance is limited by a trade-off between throughput, sensitivity, and spatial resolution.
Existing methods struggle to balance speed with detailed cellular imaging.

Purpose of the Study:

To overcome the throughput-sensitivity-resolution limitations in IFC.
To introduce an optomechanical imaging technique for enhanced cell analysis.
To enable high-throughput, high-resolution imaging of single cells.

Main Methods:

Developed an optomechanical imaging method to virtually freeze flowing cells.
Achieved significantly longer effective exposure times for microscopy-grade images.
Enabled imaging flow cytometry at over 10,000 cells per second.

Main Results:

Successfully overcame the fundamental trade-off in IFC performance.
Acquired high-quality fluorescence images of single cells at unprecedented throughput.
Demonstrated applications in hematology and microbiology with deep learning analysis.

Conclusions:

The new method significantly advances IFC capabilities for biomedical research.
High-throughput, high-resolution cell imaging is now achievable.
Enables advanced statistical analysis and classification of cellular data.