Explicit kinetic heterogeneity: mathematical models for interpretation of deuterium labeling of heterogeneous cell populations.
Explicit kinetic heterogeneity: mathematical models for interpretation of deuterium labeling of heterogeneous cell populations.
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DOI：
10.1371/journal.pcbi.1000666
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发表时间：
2010-02-05
影响因子：
4.3
通讯作者：
De Boer RJ
中科院分区：
文献类型：
--
作者：
Ganusov VV;Borghans JA;De Boer RJ
估计淋巴细胞在不同条件下的分裂和死亡率对于定量了解免疫系统是至关重要的。重水或重水形式的重氢可以用来测量活体内淋巴细胞的增殖率和死亡率。根据标记和去标记曲线推断这些比率一直存在相当大的争议，不同的团体为此目的提出了不同的数学模型。我们发现，基于完全不同的生物学假设的三个最常见的模型实际上预测了数学上相同的标记曲线，其中一个参数用于指数上升和下降斜率，一个参数定义最大标记水平。通过扩展这些以前的模型，我们在这里提出了一种新的方法来分析来自氚标记实验的数据。我们构建了一个“动力学异质性”模型，在该模型中，细胞总种群由许多具有不同细胞周转率的子种群组成。在该模型中，对于给定的周转率分布，可以计算出标记DNA累积和丢失的预测分数。我们的模型再现了之前的几个实验观察结果，例如标记周期的长度与标记停止后标记DNA丢失的速度之间存在负相关。我们通过将新的显式动力学非均质性模型应用于人工生成的数据集来验证该模型的可靠性，并通过对实验数据的拟合来说明其有效性。与以前的模型相比，显式动力学异质性模型1)提供了一种解释标记数据的新方法；2)允许标记细胞在去标记过程中的非指数损失；3)可以用来描述可变标记长度的数据。如果不对生物体中细胞分裂和死亡的速度进行定量估计，就不可能理解细胞的过程。测量人类细胞周转率最广泛的方法是用重水或重水的形式给分裂的细胞加上重氢标记。令人惊讶的是，在不同的研究中，对标记核苷酸在细胞群体中积累和衰退所获得的细胞周转率的定量估计是不同的。我们证明，这些差异不太可能因为数据拟合中使用的不同数学模型而出现，因为以前使用的模型具有相同的数学结构。我们扩展了这些以前的模型，以允许具有不同周转率的细胞群体，并展示了这样一个新的显式动力学异质性模型如何应用于模拟和实验数据。这一新模型开启了一种解释氚标记实验数据的新方法，并可能导致对感染和/或治疗如何影响人类细胞周转的新见解。
Estimation of division and death rates of lymphocytes in different conditions is vital for quantitative understanding of the immune system. Deuterium, in the form of deuterated glucose or heavy water, can be used to measure rates of proliferation and death of lymphocytes in vivo. Inferring these rates from labeling and delabeling curves has been subject to considerable debate with different groups suggesting different mathematical models for that purpose. We show that the three most common models, which are based on quite different biological assumptions, actually predict mathematically identical labeling curves with one parameter for the exponential up and down slope, and one parameter defining the maximum labeling level. By extending these previous models, we here propose a novel approach for the analysis of data from deuterium labeling experiments. We construct a model of “kinetic heterogeneity” in which the total cell population consists of many sub-populations with different rates of cell turnover. In this model, for a given distribution of the rates of turnover, the predicted fraction of labeled DNA accumulated and lost can be calculated. Our model reproduces several previously made experimental observations, such as a negative correlation between the length of the labeling period and the rate at which labeled DNA is lost after label cessation. We demonstrate the reliability of the new explicit kinetic heterogeneity model by applying it to artificially generated datasets, and illustrate its usefulness by fitting experimental data. In contrast to previous models, the explicit kinetic heterogeneity model 1) provides a novel way of interpreting labeling data; 2) allows for a non-exponential loss of labeled cells during delabeling, and 3) can be used to describe data with variable labeling length. Understanding of cellular processes is impossible without quantitative estimates of how quickly cells in an organism divide and die. The most widely used approach to measure rates of cell turnover in humans is by labeling dividing cells with deuterium given in the form of deuterated glucose or heavy water. Surprisingly, quantitative estimates of the rates of cell turnover obtained from accumulation and decay of the labeled nucleotides in the cell population varied between different studies. We demonstrate that these differences were not likely to arise because of different mathematical models used in data fitting, since the previously used models have an identical mathematical structure. We extend these previous models to allow for cell populations with different rates of turnover and show how such a new explicit kinetic heterogeneity model can be applied to simulated and experimental data. The new model opens a new way of interpreting data from deuterium labeling experiments and will likely lead to new insights into how infections and/or treatments affect cell turnover in humans.