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

Published on: November 19, 2018

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

Jonghee Yoon1, YoungJu Jo2, Young Seo Kim3

  • 1Department of Physics, University of Cambridge.

Journal of Visualized Experiments : Jove
|December 4, 2018
PubMed

Insights

This study introduces a label-free method for identifying lymphocyte subtypes using quantitative phase imaging and machine learning. This technique accurately distinguishes B, CD4+ T, and CD8+ T cells without altering cellular functions.

Area of Science:

  • Biophysics
  • Immunology
  • Medical Diagnostics

Background:

  • Accurate identification of lymphocyte subtypes is crucial for immunology research and disease management.
  • Current methods using cell labeling carry risks of altering cellular functions.
  • Label-free approaches are needed to overcome limitations of traditional methods.

Purpose of the Study:

  • To develop and describe a protocol for label-free identification of lymphocyte subtypes.
  • To utilize quantitative phase imaging and machine learning for cell classification.
  • To provide a method that avoids potential risks associated with cell labeling.

Main Methods:

  • Lymphocyte isolation and preparation.
  • 3D quantitative phase imaging to measure refractive index (RI) tomograms.
  • Machine learning algorithms for analyzing biophysical parameters and classifying cell types.

Main Results:

  • Successfully measured 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes.
  • Achieved over 80% accuracy in identifying lymphocyte subtypes at the single-cell level.
  • Demonstrated the efficacy of label-free identification using intrinsic optical contrasts.

Conclusions:

  • The developed protocol offers a reliable, label-free method for lymphocyte subtype identification.
  • Quantitative phase imaging combined with machine learning provides quantitative morphological and phenotypic data.
  • This approach has significant potential for immunological studies and clinical diagnostics.

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