Joint modeling of cell and nuclear shape variation.
Joint modeling of cell and nuclear shape variation.
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DOI：
10.1091/mbc.e15-06-0370
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发表时间：
2015-11-05
影响因子：
3.3
通讯作者：
Murphy RF
中科院分区：
文献类型：
--
作者：
Johnson GR;Buck TE;Sullivan DP;Rohde GK;Murphy RF
首次表明，可以根据三种不同细胞系的核形状准确预测细胞形状（反之亦然）。通过改变蛋白质 C1QBP 或各种药物可以降低这种相关性。此外，还给出了形状变化动力学的生成模型。该软件可在开源 CellOrganizer 系统中使用。细胞形状变化建模对于我们理解细胞生物学至关重要。先前的工作已经证明了非刚性图像配准方法在构建非参数核形状模型中的实用性，其中测量所有形状之间的成对变形距离并将其嵌入到低维形状空间中。使用这些方法，我们探索细胞形状和核形状之间的关系。我们发现它们经常相互依赖，并以此作为开发组合细胞和核形状空间模型的动机，将非参数细胞表示扩展到多组件三维细胞形状并识别关节形状变化的模式。我们学习一阶动力学模型来预测细胞和核形状，给定前一个时间点的形状。我们用它来确定内源蛋白标签或药物对细胞系形状动力学的影响，并表明标记的 C1QBP 降低了细胞和核形状之间的相关性。为了减少学习这些模型的计算成本，我们展示了使用计算的成对距离的一小部分来重建形状空间的能力。这些开源工具为未来研究细胞组织的分子基础提供了强大的基础。
It is shown for the first time that cell shape can be accurately predicted from nuclear shape (and vice versa) for three different cell lines. This correlation is reduced by altering protein C1QBP or various drugs. In addition, a generative model is given for the kinetics of shape change. The software is available in the open-source CellOrganizer system. Modeling cell shape variation is critical to our understanding of cell biology. Previous work has demonstrated the utility of nonrigid image registration methods for the construction of nonparametric nuclear shape models in which pairwise deformation distances are measured between all shapes and are embedded into a low-dimensional shape space. Using these methods, we explore the relationship between cell shape and nuclear shape. We find that these are frequently dependent on each other and use this as the motivation for the development of combined cell and nuclear shape space models, extending nonparametric cell representations to multiple-component three-dimensional cellular shapes and identifying modes of joint shape variation. We learn a first-order dynamics model to predict cell and nuclear shapes, given shapes at a previous time point. We use this to determine the effects of endogenous protein tags or drugs on the shape dynamics of cell lines and show that tagged C1QBP reduces the correlation between cell and nuclear shape. To reduce the computational cost of learning these models, we demonstrate the ability to reconstruct shape spaces using a fraction of computed pairwise distances. The open-source tools provide a powerful basis for future studies of the molecular basis of cell organization.