Computational analysis of CFSE proliferation assay.

Area of Science:

• Immunology
• Computational Biology
• Mathematical Modeling

Background:

• Carboxyfluorescein succinimidyl ester (CFSE) labeling and flow cytometry are vital for tracking lymphocyte proliferation.
• Interpreting complex cell division data requires advanced analytical methods like mathematical modeling.

Purpose of the Study:

• To apply a heterogeneous linear compartmental model to analyze CFSE-labeled human T lymphocyte proliferation kinetics.
• To investigate division number-dependent rates of lymphocyte proliferation and death.
• To compare methods for estimating model parameter confidence intervals.

Main Methods:

• Utilized a compartmental model based on ordinary differential equations to describe cell division and death.
• Employed a maximum likelihood approach for parameter estimation.
• Compared variance-covariance matrix, profile-likelihood, and bootstrap techniques for confidence interval analysis.

Main Results:

• Demonstrated that lymphocyte proliferation and death rates are dependent on the number of cell divisions, both in vitro and in vivo.
• Identified division number-dependent birth and death rate functions for T lymphocytes.
• Found that information-theoretic approaches provide a consistent basis for evaluating mathematical models of varying complexity.

Conclusions:

• Lymphocyte kinetics exhibit inherent heterogeneity that should be integrated into mathematical models.
• The study highlights challenges in parameter identification for CFSE data, particularly beyond the sixth division.
• This work provides novel insights into the dynamics of lymphocyte proliferation and death based on division number.