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Deep Learning of Morphologic Correlations To Accurately Classify CD4+ and CD8+ T Cells by Diffraction Imaging Flow
Lin Zhao1,2,3, Liwen Tang1,3, 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.
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.

