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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: Jul 15, 2025

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Published on: January 12, 2018

High-throughput adjustable deformability cytometry utilizing elasto-inertial focusing and virtual fluidic channel.

Zheng Zhou¹, Chen Ni¹, Zhixian Zhu¹

¹School of Mechanical Engineering, and, Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, 211189, China. nan.xiang@seu.edu.cn.

|September 28, 2023

Summary

This study introduces adjustable deformability cytometry for high-throughput mechanical phenotyping of diverse cell types. The novel system accurately classifies cells based on size and deformation, offering a flexible tool for biological sample analysis.

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Last Updated: Jul 15, 2025

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

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A Microfluidic Technique to Probe Cell Deformability

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Area of Science:

Biophysics
Cell Biology
Microfluidics

Background:

Cell mechanical properties are crucial biomarkers for cell states and diseases.
Existing microfluidic deformability cytometry lacks universal applicability for multiple cell sizes.
A single device for characterizing diverse cell sizes remains a significant challenge.

Purpose of the Study:

To develop a high-throughput, adjustable deformability cytometry system.
To enable characterization of multiple cell sizes using a single microfluidic device.
To improve cell classification accuracy using deep learning.

Main Methods:

Integration of 3D elasto-inertial focusing and a virtual fluidic channel.
Adjustable flow ratios to generate shear forces and induce cell deformation.
Development of a mini-bilateral segmentation network (mini-BiSeNet) for rapid cell identification and feature extraction.

Main Results:

Achieved high-throughput (up to 3000 cells/sec) and adjustable detection of multiple cell sizes.
Demonstrated homogeneous cell deformation through combined elasto-inertial focusing and virtual channel.
Attained ~90% accuracy in classifying different cell populations (A549, MCF-7, MDA-MB-231, WBCs) using deep learning.

Conclusions:

The proposed deformability cytometry system offers precise and rapid mechanical phenotyping.
The system demonstrates flexibility for characterizing various biological samples, including pleural effusions.
This technology holds promise for advancing label-free cell analysis and disease detection.