Yates Andrew, Chan Cliburn, Strid Jessica, Moon Simon, Callard Robin, George Andrew J T, Stark Jaroslav
Department of Biology, Emory University, Atlanta, GA 30322, USA.
BMC Bioinformatics. 2007 Jun 12;8:196. doi: 10.1186/1471-2105-8-196.
Quantifying cell division and death is central to many studies in the biological sciences. The fluorescent dye CFSE allows the tracking of cell division in vitro and in vivo and provides a rich source of information with which to test models of cell kinetics. Cell division and death have a stochastic component at the single-cell level, and the probabilities of these occurring in any given time interval may also undergo systematic variation at a population level. This gives rise to heterogeneity in proliferating cell populations. Branching processes provide a natural means of describing this behaviour.
We present a likelihood-based method for estimating the parameters of branching process models of cell kinetics using CFSE-labeling experiments, and demonstrate its validity using synthetic and experimental datasets. Performing inference and model comparison with real CFSE data presents some statistical problems and we suggest methods of dealing with them.
The approach we describe here can be used to recover the (potentially variable) division and death rates of any cell population for which division tracking information is available.
量化细胞分裂和死亡是生物科学中许多研究的核心。荧光染料羧基荧光素二乙酸琥珀酰亚胺酯（CFSE）能够在体外和体内追踪细胞分裂，并提供丰富的信息来源以测试细胞动力学模型。在单细胞水平上，细胞分裂和死亡具有随机成分，并且在任何给定时间间隔内发生这些情况的概率在群体水平上也可能会发生系统性变化。这导致增殖细胞群体中出现异质性。分支过程提供了描述这种行为的自然方式。
我们提出了一种基于似然性的方法，用于使用CFSE标记实验估计细胞动力学分支过程模型的参数，并使用合成数据集和实验数据集证明了其有效性。对真实的CFSE数据进行推断和模型比较存在一些统计问题，我们提出了处理这些问题的方法。
我们在此描述的方法可用于恢复任何具有分裂追踪信息的细胞群体的（可能可变的）分裂和死亡率。
