Image-based spatiotemporal causality inference for protein signaling networks.
Image-based spatiotemporal causality inference for protein signaling networks.
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DOI：
10.1093/bioinformatics/btx258
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发表时间：
2017-07-15
期刊：
影响因子：
--
通讯作者：
Murphy RF
中科院分区：
文献类型：
--
作者：
Ruan X;Wülfing C;Murphy RF
对信号传导和调控网络在细胞中如何工作进行建模的努力在很大程度上没有考虑空间组织，或者使用具有最小空间分辨率的房室模型。荧光显微镜提供了在信号事件期间监测许多分子时空分布的能力，但迄今为止还没有描述用于大规模图像分析以了解复杂蛋白质调控网络的方法。在这里，我们提出并评估方法，用于确定如何在一个细胞区域的浓度变化影响其他区域的其他蛋白质的浓度。使用3D共聚焦显微镜电影的GFP标记的T细胞进行共刺激，我们学习的模型包含假定的因果关系之间的12个蛋白质参与T细胞信号转导。这些模型既包括与现有知识一致的关系，也包括值得进一步探索的新预测。此外，当这些模型应用于仅部分刺激的T细胞电影的初始帧时，它们以统计学上显著的准确性预测了后期蛋白质的定位。该方法，包括时空对齐，自动区域识别，因果推理，预计将适用于一些生物系统。源代码和数据可在http://murphylab.cbd.cmu.edu/software/2017_TcellCausalModels/上作为可复制的研究档案获得
Efforts to model how signaling and regulatory networks work in cells have largely either not considered spatial organization or have used compartmental models with minimal spatial resolution. Fluorescence microscopy provides the ability to monitor the spatiotemporal distribution of many molecules during signaling events, but as of yet no methods have been described for large scale image analysis to learn a complex protein regulatory network. Here we present and evaluate methods for identifying how changes in concentration in one cell region influence concentration of other proteins in other regions. Using 3D confocal microscope movies of GFP-tagged T cells undergoing costimulation, we learned models containing putative causal relationships among 12 proteins involved in T cell signaling. The models included both relationships consistent with current knowledge and novel predictions deserving further exploration. Further, when these models were applied to the initial frames of movies of T cells that had been only partially stimulated, they predicted the localization of proteins at later times with statistically significant accuracy. The methods, consisting of spatiotemporal alignment, automated region identification, and causal inference, are anticipated to be applicable to a number of biological systems. The source code and data are available as a Reproducible Research Archive at http://murphylab.cbd.cmu.edu/software/2017_TcellCausalModels/
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影响因子：
4.5
作者：
Farkash-Amar S;Zimmer A;Eden E;Cohen A;Geva-Zatorsky N;Cohen L;Milo R;Sigal A;Danon T;Alon U
通讯作者：
Alon U
影响因子：
64.8
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影响因子：
48
作者：
Angermann, Bastian R.;Klauschen, Frederick;Meier-Schellersheim, Martin
通讯作者：
Meier-Schellersheim, Martin
影响因子：
5.8
作者：
Chindelevitch, Leonid;Ziemek, Daniel;Huang, Enoch S.
通讯作者：
Huang, Enoch S.
影响因子：
3
作者：
Zou C;Denby KJ;Feng J
通讯作者：
Feng J