Analysis of CFSE time-series data using division-, age- and label-structured population models

Area of Science:

• Cell biology
• Computational biology
• Immunology

Background:

• Carboxyfluorescein succinimidyl ester (CFSE) is widely used to study in vitro and in vivo cell proliferation.
• CFSE time-series data offer insights into cell population proliferation history.
• Current analysis methods for CFSE data often neglect cell age and use inefficient or unreliable optimization techniques.

Purpose of the Study:

• To develop a novel model-based analysis method for CFSE time-series data.
• To address limitations of existing tools, particularly their inability to account for cell age and computational inefficiencies.
• To provide a more reliable and efficient approach for analyzing cell proliferation dynamics.

Main Methods:

• A division-, age-, and label-structured population model is employed for flexible description of proliferating cells.
• Efficient maximum likelihood and Bayesian estimation algorithms are utilized for parameter inference and uncertainty quantification.
• Forward sensitivity equations of the underlying partial differential equation model are exploited for accurate gradient calculation and improved computational efficiency.

Main Results:

• The new method demonstrates improved computational efficiency and reliability compared to existing approaches.
• Analysis of immune cell proliferation data using the new method revealed the significance of various factors influencing proliferation rates.
• A predominant effect of cell age on division rate was identified, a finding not previously revealed by other computational methods.

Conclusions:

• The developed model-based method offers a significant advancement in analyzing CFSE time-series data.
• The method's ability to incorporate cell age provides deeper insights into proliferation dynamics.
• This approach enhances the reliability and efficiency of cell proliferation studies, particularly in immunology.