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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: Mar 27, 2026

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Published on: August 16, 2015

Label-free cell cycle analysis for high-throughput imaging flow cytometry.

Thomas Blasi^1,2,3, Holger Hennig¹, Huw D Summers⁴

¹Imaging Platform at the Broad Institute of Harvard and MIT, 415 Main St, Cambridge, Massachusetts 02142, USA.

Nature Communications

|January 8, 2016

Summary

This summary is machine-generated.

This study introduces a label-free method using imaging flow cytometry and machine learning to predict cell DNA content and cell cycle phases. This non-destructive approach enhances cell analysis without fluorescent stains.

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Last Updated: Mar 27, 2026

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Temporal Tracking of Cell Cycle Progression Using Flow Cytometry without the Need for Synchronization

Published on: August 16, 2015

Measuring Cell Cycle Progression Kinetics with Metabolic Labeling and Flow Cytometry

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Analysis of Cell Cycle Position in Mammalian Cells

Analysis of Cell Cycle Position in Mammalian Cells

Published on: January 21, 2012

Area of Science:

Cell Biology
Biotechnology
Machine Learning

Background:

Conventional flow cytometry offers high throughput but lacks single-cell imaging.
Imaging flow cytometry integrates high-throughput analysis with single-cell imaging capabilities.
Fluorescent stains can introduce confounding effects and consume valuable imaging channels.

Purpose of the Study:

To develop a label-free method for predicting DNA content and cell cycle phases using imaging flow cytometry.
To leverage supervised machine learning on morphological features from brightfield and darkfield images.
To enable non-destructive cell monitoring and maximize fluorescence channel availability.

Main Methods:

Applied supervised machine learning to morphological features from brightfield and darkfield images.
Utilized imaging flow cytometry for high-throughput single-cell analysis.
Validated the method on mammalian cells (fixed and live) and fission yeast.

Main Results:

Successfully predicted DNA content and quantified mitotic cell cycle phases without fluorescent labels.
Demonstrated accurate cell cycle analysis in mammalian cells, including assessment of mitotic arrest agents.
Showcased effectiveness in fission yeast, indicating broad applicability.

Conclusions:

Label-free morphological analysis via machine learning in imaging flow cytometry is a powerful tool for cell cycle studies.
This non-destructive method avoids limitations associated with fluorescent stains.
The approach shows promise for diverse cell types, advancing cell cycle research.