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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: Mar 31, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Pattern recognition cytometry for label-free cell classification by 2D light scattering measurements.

Xuantao Su, Shanshan Liu, Xu Qiao

|October 20, 2015

Summary

This study introduces a label-free cell classification method using pattern recognition cytometry. The technique accurately distinguishes between different yeast cell aggregates and classifies normal cervical cells from HeLa cells.

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Last Updated: Mar 31, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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

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Sample Preparation for Mass Cytometry Analysis

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Published on: April 29, 2017

Area of Science:

Biotechnology
Cell Biology
Machine Learning

Background:

Label-free cell classification is crucial for biological research and diagnostics.
Existing methods often require cell staining, which can alter cell properties.
Developing non-invasive techniques for cell analysis is an ongoing challenge.

Purpose of the Study:

To develop and validate a novel pattern recognition cytometric technique for label-free cell classification.
To assess the performance of the technique in differentiating between yeast cell aggregates.
To demonstrate the capability of the method for classifying different types of human cells.

Main Methods:

Utilized a static cytometer to acquire two-dimensional (2D) light scattering patterns from single cells and cell aggregates.
Employed adaptive boosting (AdaBoost), a machine learning algorithm, for analyzing the 2D light scattering patterns.
Validated the cytometric setup by comparing experimental yeast cell results with theoretical simulations.

Main Results:

The pattern recognition cytometric technique successfully differentiated between aggregates of three and four yeast cells.
The method achieved high accuracy in the label-free classification of normal cervical cells and HeLa cells.
The performance of the cytometric setup was confirmed through comparison with theoretical simulations.

Conclusions:

Pattern recognition cytometry offers a powerful tool for label-free cell classification.
The AdaBoost algorithm effectively analyzes light scattering patterns for accurate cell differentiation.
This technique holds significant potential for applications in cell biology and medical diagnostics.