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使用数学模型从CFSE数据估计淋巴细胞分裂参数及其影响。

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

作者信息

Mazzocco Pauline, Bernard Samuel, Pujo-Menjouet Laurent

机构信息

Université de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Villeurbanne, France.

Université de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France.

出版信息

PLoS One. 2017 Jun 16;12(6):e0179768. doi: 10.1371/journal.pone.0179768. eCollection 2017.

Abstract

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.

摘要

羧基荧光素二乙酸琥珀酰亚胺酯(CFSE)标记已被广泛用于追踪和研究细胞增殖。在此,我们使用数学建模来描述用CFSE追踪的体外多克隆刺激后免疫细胞增殖的动力学。这种方法使我们能够估计一组关键参数,包括与细胞死亡和增殖相关的参数。我们开发了一个三相模型,该模型区分了潜伏期(解释未分裂细胞的行为)、静止期和分裂过程的活跃期。参数估计值来自模型结果,然后将数值模拟与体外实验的动力学进行比较,并测试不同的生物学假设。我们的模型使我们能够比较CD4 +和CD8 +细胞的动力学,并突出它们的动力学差异。最后,我们进行敏感性分析以量化每个参数对增殖动力学的影响。有趣的是,我们发现参数敏感性随时间和细胞代数而变化。我们的方法可以帮助生物学家理解细胞增殖机制并识别潜在的病理性分裂过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ced/5473582/a72bee952109/pone.0179768.g001.jpg

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