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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

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

Framework for morphometric classification of cells in imaging flow cytometry.

G Gopakumar¹, Veerendra Kalyan Jagannadh², Sai Siva Gorthi²

¹Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Thiruvananthapuram, Kerala, India.

Journal of Microscopy

|October 16, 2015

Summary

This summary is machine-generated.

This study introduces a new automated framework for analyzing cell images from imaging flow cytometry. The system accurately classifies leukemia cell lines, paving the way for cost-effective disease diagnosis tools.

Keywords:

Classification imaging flow cytometry leukaemia morphology segmentation texture features

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Related Experiment Videos

Last Updated: Mar 31, 2026

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

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Published on: March 24, 2023

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Published on: September 15, 2021

Area of Science:

Biomedical Engineering
Cell Biology
Microscopy

Background:

Imaging flow cytometry integrates flow cytometry's throughput with microscopy's spatial detail.
Analyzing cell morphology in flow requires advanced image processing techniques.

Purpose of the Study:

To develop a general, automated framework for processing and classifying cells from imaging flow cytometry data.
To enable cost-effective cell analysis for disease diagnosis.

Main Methods:

A noniterative, graph-based approach for automatic cell localization and contour finding.
Feature extraction (size, circularity, complexity) for classification using Support Vector Machines (SVM).

Main Results:

Successfully classified unstained, label-free leukemia cell lines (MOLT, K562, HL60).
Demonstrated performance using video streams from a custom microfluidics-based imaging flow cytometer.

Conclusions:

The proposed framework offers a fully automated and cost-effective solution for cell image analysis.
This technology can facilitate affordable mass screening for diseases based on cellular morphology.