通过表达数据去卷积实现细胞群体的计算机同步
In Silico Synchronization of Cellular Populations Through Expression Data Deconvolution
摘要：
细胞群体通常是在其各自细胞周期中的不同点处的细胞的异质集合，每个细胞具有个体与个体不同的细胞周期时间。因此，真正的单细胞行为，特别是细胞周期依赖性的单细胞行为，在群体水平（平均）测量中常常被掩盖。我们已经开发了一个简单的反卷积方法，可以用来消除人口水平的时间序列数据的异步变异的影响。在本文中，我们总结了我们的方法的发展和应用的一些最新进展，并提供了技术更新，导致增加生物保真度。我们还探讨了几个初步的验证结果，并讨论了几个正在进行的应用程序，突出了该方法的有用性，估计参数的微分方程模型的单细胞基因调控。
英文摘要：
Cellular populations are typically heterogenous collections of cells at different points in their respective cell cycles, each with a cell cycle time that varies from individual to individual. As a result, true single-cell behavior, particularly that which is cell-cycle--dependent, is often obscured in population-level (averaged) measurements. We have developed a simple deconvolution method that can be used to remove the effects of asynchronous variability from population-level time-series data. In this paper, we summarize some recent progress in the development and application of our approach, and provide technical updates that result in increased biological fidelity. We also explore several preliminary validation results and discuss several ongoing applications that highlight the method's usefulness for estimating parameters in differential equation models of single-cell gene regulation.
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