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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: Oct 7, 2025

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Published on: December 17, 2015

Deep Learning of Morphologic Correlations To Accurately Classify CD4+ and CD8+ T Cells by Diffraction Imaging Flow

Lin Zhao^1,2,3, Liwen Tang^1,3, Marion S Greene²

¹Institute for Advanced Optics, Hunan Institute of Science and Technology, Yueyang, Hunan 414006, China.

Analytical Chemistry

|January 10, 2022

Summary

This study reveals a strong link between T cell structure and function. Diffraction imaging flow cytometry accurately distinguishes CD4+ and CD8+ T cell subtypes based on their unique physical characteristics, enabling label-free classification.

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Last Updated: Oct 7, 2025

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Area of Science:

Immunology
Cell Biology
Biophysics

Background:

CD4+ and CD8+ T cells are crucial for adaptive immunity.
Understanding their structure-function relationship at the single-cell level is vital.

Purpose of the Study:

To investigate the morphologic differences between CD4+ and CD8+ T cells.
To establish a link between T cell structure and function using advanced imaging techniques.
To develop a label-free method for T cell subtype classification.

Main Methods:

Isolation of 2483 CD4+ and 2450 CD8+ T cells from human splenocytes via immunofluorescent sorting.
Acquisition and analysis of cross-polarized diffraction image (p-DI) pairs.
Development of a deep neural network (DINet-R) for feature extraction.
Application of a novel algorithm for support cell selection based on morphologic correlation.
Analysis of depolarization ratios.

Main Results:

Significant morphologic pattern differences were observed in p-DI pairs between CD4+ and CD8+ T cell subtypes.
DINet-R achieved 97.3 ± 0.40% accuracy in classifying T cell subtypes.
Depolarization ratio analysis indicated distinct molecular polarizability between subtypes.
A strong structure-function relationship was confirmed for major T cell subtypes.

Conclusions:

Diffraction imaging flow cytometry offers accurate, label-free classification of T cell subtypes.
The study demonstrates a significant structure-function relationship in T cells.
This technique holds potential for advancing immunological research and diagnostics.