Department of Computer Science, Aalto University, 00076 Aalto, Finland.
Department of Computer Science, Aalto University, 00076 Aalto, Finland Turku Centre for Biotechnology, University of Turku and Åbo Akademi, 20521 Turku, Finland.
Bioinformatics. 2016 Nov 1;32(21):3306-3313. doi: 10.1093/bioinformatics/btw395. Epub 2016 Jul 10.
Cell differentiation is steered by extracellular signals that activate a cell type specific transcriptional program. Molecular mechanisms that drive the differentiation can be analyzed by combining mathematical modeling with population average data. For standard mathematical models, the population average data is informative only if the measurements come from a homogeneous cell culture. In practice, however, the differentiation efficiencies are always imperfect. Consequently, cell cultures are inherently mixtures of several cell types, which have different molecular mechanisms and exhibit quantitatively different dynamics. There is an urgent need for data-driven mathematical modeling approaches that can detect possible heterogeneity and, further, recover the molecular mechanisms from heterogeneous data.
We develop a novel method that models a heterogeneous population using homogeneous subpopulations that evolve in parallel. Different subpopulations can represent different cell types and each subpopulation can have cell type specific molecular mechanisms. We present statistical methodology that can be used to quantify the effect of heterogeneity and to infer the subpopulation specific molecular interactions. After a proof of principle study with simulated data, we apply our methodology to analyze the differentiation of human Th17 cells using time-course RNA sequencing data. We construct putative molecular networks driving the T cell activation and Th17 differentiation and allow the cell populations to be split into two subpopulations in the case of heterogeneous samples. Our analysis shows that the heterogeneity indeed has a statistically significant effect on observed dynamics and, furthermore, our statistical methodology can infer both the subpopulation specific molecular mechanisms and the effect of heterogeneity.
An implementation of the method is available at http://research.ics.aalto.fi/csb/software/subpop/ CONTACT: jukka.intosalmi@aalto.fi or harri.lahdesmaki@aalto.fiSupplementary information: Supplementary data are available at Bioinformatics online.
细胞分化是由激活特定转录程序的细胞外信号驱动的。通过将数学建模与群体平均数据相结合，可以分析驱动分化的分子机制。对于标准的数学模型，只有当测量值来自同质细胞培养时，群体平均数据才是有信息的。然而，在实践中，分化效率总是不完美的。因此，细胞培养本质上是几种细胞类型的混合物，它们具有不同的分子机制，并表现出不同的定量动力学。迫切需要能够检测到可能的异质性并从异质数据中恢复分子机制的数据驱动的数学建模方法。
我们开发了一种新的方法，该方法使用平行进化的同质亚群来对异质群体进行建模。不同的亚群可以代表不同的细胞类型，每个亚群都有特定于细胞类型的分子机制。我们提出了一种统计方法，可以用来量化异质性的影响，并推断亚群特异性分子相互作用。在使用模拟数据进行原理验证研究之后，我们将我们的方法应用于使用时间过程 RNA 测序数据分析人 Th17 细胞的分化。我们构建了驱动 T 细胞激活和 Th17 分化的假定分子网络，并允许在异质样本的情况下将细胞群体分为两个亚群。我们的分析表明，异质性确实对观察到的动力学有统计学上的显著影响，此外，我们的统计方法可以推断出亚群特异性的分子机制和异质性的影响。
该方法的实现可在 http://research.ics.aalto.fi/csb/software/subpop/ 上获得。
jukka.intosalmi@aalto.fi 或 harri.lahdesmaki@aalto.fi
补充数据可在生物信息学在线获得。
