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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: Dec 25, 2025

Flow Cytometry Protocols for Surface and Intracellular Antigen Analyses of Neural Cell Types

Flow Cytometry Protocols for Surface and Intracellular Antigen Analyses of Neural Cell Types

Published on: December 18, 2014

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.

Denis V Voronin^1,2, Anastasiia A Kozlova¹, Roman A Verkhovskii^1,3

¹Laboratory of Biomedical Photoacoustics, Saratov State University, 410012 Saratov, Russia.

International Journal of Molecular Sciences

|April 2, 2020

Summary

Flow cytometry enables rapid diagnostics for blood diseases by detecting rare pathogenic objects like circulating tumor cells and microbes. This review covers visualization, extraction, and deep learning methods for identifying these critical diagnostic targets.

Keywords:

cell labeling cell sorting circulating tumor cells deep learning flow cytometry data analysis imaging flow cytometry liquid biopsy

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Area of Science:

Biomedical Engineering
Clinical Diagnostics
Computational Biology

Background:

Flow cytometry is crucial for diagnosing blood-related diseases.
Detecting rare pathogenic objects in blood is a major clinical challenge.
Early detection of circulating tumor cells, microorganisms, and parasites is vital.

Purpose of the Study:

To review techniques for visualizing rare objects in blood flow.
To outline methods for extracting these rare objects for further analysis.
To explore deep learning approaches for automated identification of rare diagnostic targets.

Main Methods:

Review of current visualization techniques in flow cytometry.
Analysis of established and novel extraction methodologies.
Investigation of deep learning algorithms for object recognition.

Main Results:

Identification of key visualization and extraction techniques for rare bloodborne objects.
Highlighting the potential of deep learning for accurate and rapid automated detection.
Discussion of challenges and future directions in rare object analysis.

Conclusions:

Flow cytometry offers powerful tools for early disease detection.
Advanced methods, including deep learning, are enhancing the identification of rare diagnostic targets.
Improved techniques promise faster and more reliable clinical diagnostics for critical conditions.