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The Use of Carboxyfluorescein Diacetate Succinimidyl Ester (CFSE) to Monitor Lymphocyte Proliferation
Published on: October 12, 2010
A division-dependent compartmental model for computing cell numbers in CFSE-based lymphocyte proliferation assays
H T Banks1, W Clayton Thompson, Cristina Peligero
1Center for Research in Scientic Computation, Center for Quantitative Sciences in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212, United States. htbanks@ncsu.edu
Mathematical Biosciences and Engineering : MBE
|January 15, 2013
Summary
This study introduces a mathematical model using CFSE dye data to analyze immune cell division and death. The model accurately estimates cell counts, enabling determination of key parameters like doubling time and precursor viability.
Area of Science:
- Immunology
- Mathematical Biology
- Computational Science
Background:
- Mathematical models are crucial for understanding immune responses, particularly cell division, differentiation, and death.
- Carboxyfluorescein succinimidyl ester (CFSE) dye is a key experimental tool for analyzing cell proliferation in immune studies.
- Previous mathematical approaches have utilized CFSE data, with recent focus on structured population models.
Purpose of the Study:
- To develop and apply a structured partial differential equation model to analyze CFSE histogram data from immune cell populations.
- To accurately estimate immune cell counts based on division number.
- To determine critical biological parameters such as population doubling time and precursor cell viability.
Main Methods:
- A compartmental model was developed, organizing cells by the number of divisions undergone.
- A system of structured partial differential equations was derived for direct fitting to CFSE histogram data.
- The model was applied to a dataset, incorporating temporal and division-dependent rates for proliferation and death, and accounting for cellular autofluorescence variability.
Main Results:
- The derived compartmental model accurately fits CFSE histogram data.
- The model allows for direct computation of cell counts and determination of biological parameters like doubling time and precursor viability.
- Temporal and division-dependent rates of proliferation and death were found to be essential, alongside cellular autofluorescence variability.
Conclusions:
- Structured population models, particularly the derived compartmental model, are effective for analyzing CFSE data in immune response studies.
- The model provides a robust framework for quantifying immune cell dynamics and deriving key biological parameters.
- Accurate modeling requires consideration of factors like cell division history, proliferation/death rates, and cellular autofluorescence.
