Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling

Carboxyfluorescein diacetate succinimidyl ester (CFSE) labelling has been widely used to track and study cell proliferation. Here we use mathematical modelling to describe the kinetics of immune cell proliferation after an in vitro polyclonal stimulation tracked with CFSE. This approach allows us to...

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Bibliographic Details
Published in:PloS one 2017-06, Vol.12 (6), p.e0179768-e0179768
Main Authors: Mazzocco, Pauline, Bernard, Samuel, Pujo-Menjouet, Laurent
Format: Article
Language:English
Subjects:
Biological models (mathematics)
Biologists
Biology and Life Sciences
Carboxyfluorescein diacetate
CD4 antigen
CD4-Positive T-Lymphocytes - cytology
CD4-Positive T-Lymphocytes - metabolism
CD8 antigen
CD8-Positive T-Lymphocytes - cytology
CD8-Positive T-Lymphocytes - metabolism
Cell cycle
Cell death
Cell division
Cell Division - physiology
Cell growth
Cell proliferation
Computer simulation
Division
Dynamical Systems
Estimates
Fluoresceins - chemistry
Fluorescent Dyes - chemistry
Genetic aspects
Humans
Immunology
Impact analysis
In vitro methods and tests
Kinetics
Labeling
Labelling
Latency
Lymphocytes
Mathematical models
Mathematics
Medicine and Health Sciences
Methods
Models, Biological
Mortality
Numerical simulations
Ordinary differential equations
Parameter estimation
Parameter sensitivity
People and Places
Physiological aspects
Research and Analysis Methods
Sensitivity
Sensitivity analysis
Staining and Labeling
Stimulation
Succinimides - chemistry
T cell receptors
Tracking
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Description
Summary:Carboxyfluorescein diacetate succinimidyl ester (CFSE) labelling has been widely used to track and study cell proliferation. Here we use mathematical modelling to describe the kinetics of immune cell proliferation after an in vitro polyclonal stimulation tracked with CFSE. This approach allows us to estimate a set of key parameters, including ones related to cell death and proliferation. We develop a three-phase model that distinguishes a latency phase, accounting for non-divided cell behaviour, a resting phase and the active phase of the division process. Parameter estimates are derived from model results, and numerical simulations are then compared to the dynamics of in vitro experiments, with different biological assumptions tested. Our model allows us to compare the dynamics of CD4+ and CD8+ cells, and to highlight their kinetic differences. Finally we perform a sensitivity analysis to quantify the impact of each parameter on proliferation kinetics. Interestingly, we find that parameter sensitivity varies with time and with cell generation. Our approach can help biologists to understand cell proliferation mechanisms and to identify potential pathological division processes.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0179768