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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: May 6, 2026

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Published on: August 15, 2013

Analysis of cellular objects through diffraction images acquired by flow cytometry.

Jun Zhang, Yuanming Feng, Marina S Moran

|October 24, 2013

Summary

This summary is machine-generated.

This study introduces an automated method to analyze cell diffraction images, classifying cells by their 3D morphology. The technique uses global and local pattern variations for rapid, label-free cell identification.

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Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

Published on: May 5, 2017

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Last Updated: May 6, 2026

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

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Published on: August 15, 2013

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Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

Published on: May 5, 2017

Area of Science:

Biophysics
Cell Biology
Image Analysis

Background:

Diffraction imaging reveals pattern variations at global and local scales in cell samples.
These variations are linked to object size and morphological heterogeneity.
Automated analysis is crucial for efficient cell classification.

Purpose of the Study:

To develop an automated image processing method for analyzing cell diffraction images.
To classify cells based on their 3D morphology using label-free techniques.
To correlate global diffraction pattern variations with cell size and morphology.

Main Methods:

Acquisition of diffraction images along side scattering directions.
Development of an automated method to categorize global diffraction patterns into three types.
Integration with existing methods for local texture pattern quantification.
Fully automated analysis pipeline for diffraction image data.

Main Results:

Global pattern variations in diffraction images correlate with categorical size and morphological heterogeneity.
The automated method successfully separates diffraction images into three global pattern types.
Combined analysis enables rapid and label-free classification of cells by 3D morphology.

Conclusions:

The developed automated method provides a robust approach for label-free cell classification.
This technique allows for rapid analysis of cell morphology directly from diffraction images.
The findings advance the field of biophysical characterization of cells.