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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Main Authors: Pauline Mazzocco, Samuel Bernard, Laurent Pujo-Menjouet
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5473582?pdf=render
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spelling doaj-cf8aeb38b7f7425d94db0d57e65b92542020-11-25T02:36:42ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01126e017976810.1371/journal.pone.0179768Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling.Pauline MazzoccoSamuel BernardLaurent Pujo-MenjouetCarboxyfluorescein 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.http://europepmc.org/articles/PMC5473582?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Pauline Mazzocco
Samuel Bernard
Laurent Pujo-Menjouet
spellingShingle Pauline Mazzocco
Samuel Bernard
Laurent Pujo-Menjouet
Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling.
PLoS ONE
author_facet Pauline Mazzocco
Samuel Bernard
Laurent Pujo-Menjouet
author_sort Pauline Mazzocco
title Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling.
title_short Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling.
title_full Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling.
title_fullStr Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling.
title_full_unstemmed Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling.
title_sort estimates and impact of lymphocyte division parameters from cfse data using mathematical modelling.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2017-01-01
description 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.
url http://europepmc.org/articles/PMC5473582?pdf=render
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AT samuelbernard estimatesandimpactoflymphocytedivisionparametersfromcfsedatausingmathematicalmodelling
AT laurentpujomenjouet estimatesandimpactoflymphocytedivisionparametersfromcfsedatausingmathematicalmodelling
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