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

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.