CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine...

Area of Science:

• Immunology
• Biophysics
• Microfluidics

Background:

• T lymphocytes, specifically CD4+ and CD8+ subclasses, are crucial for adaptive immunity.
• Current methods for T lymphocyte characterization rely on complex antibody-based cytometric assays.
• Distinguishing between CD4+ and CD8+ T cells is vital for clinical decision-making.

Purpose of the Study:

• To develop a label-free, biophysical method for distinguishing and counting CD4+ and CD8+ T lymphocytes at the single-cell level.
• To leverage integrated microfluidics and data mining for accurate cell analysis.
• To assess the potential of this approach for clinical applications.

Main Methods:

• Utilized a microfluidic chip for single-cell optical analysis in viscoelastic fluids.
• Measured biophysical properties including cell dimensions, nuclear refractive index, and cytosol refractive index.
• Employed machine learning algorithms to analyze measured properties and classify T lymphocyte subclasses.

Main Results:

• Achieved 79% accuracy in distinguishing and counting CD4+ and CD8+ T cells using biophysical properties and machine learning.
• Enhanced identification accuracy to 88% by stimulating cells with an anti-apoptotic protein, increasing biophysical differences.
• Validated the approach using samples reflecting physiological and pathological CD4+/CD8+ ratios.

Conclusions:

• The developed light-scattering and machine learning approach provides an effective label-free method for T lymphocyte subclass identification.
• This technique shows significant potential for integration into hematological clinical routines and disease monitoring, such as HIV progression.
• Further research can optimize this biophysical evaluation for broader diagnostic applications.