Buck Taraz E, Rao Arvind, Coelho Luis Pedro, Fuhrman Margaret H, Jarvik Jonathan W, Berget Peter B, Murphy Robert F
Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1016-9. doi: 10.1109/IEMBS.2009.5332888.
Protein subcellular location is one of the most important determinants of protein function during cellular processes. Changes in protein behavior during the cell cycle are expected to be involved in cellular reprogramming during disease and development, and there is therefore a critical need to understand cell-cycle dependent variation in protein localization which may be related to aberrant pathway activity. With this goal, it would be useful to have an automated method that can be applied on a proteomic scale to identify candidate proteins showing cell-cycle dependent variation of location. Fluorescence microscopy, and especially automated, high-throughput microscopy, can provide images for tens of thousands of fluorescently-tagged proteins for this purpose. Previous work on analysis of cell cycle variation has traditionally relied on obtaining time-series images over an entire cell cycle; these methods are not applicable to the single time point images that are much easier to obtain on a large scale. Hence a method that can infer cell cycle-dependence of proteins from asynchronous, static cell images would be preferable. In this work, we demonstrate such a method that can associate protein pattern variation in static images with cell cycle progression. We additionally show that a one-dimensional parameterization of cell cycle progression and protein feature pattern is sufficient to infer association between localization and cell cycle.
蛋白质亚细胞定位是细胞过程中蛋白质功能的最重要决定因素之一。细胞周期中蛋白质行为的变化预计与疾病和发育过程中的细胞重编程有关，因此迫切需要了解蛋白质定位中可能与异常信号通路活性相关的细胞周期依赖性变化。出于这个目标，拥有一种可以应用于蛋白质组规模以识别显示定位细胞周期依赖性变化的候选蛋白质的自动化方法将很有用。荧光显微镜，尤其是自动化的高通量显微镜，可以为此目的提供数万个荧光标记蛋白质的图像。以往关于细胞周期变化分析的工作传统上依赖于在整个细胞周期内获取时间序列图像；这些方法不适用于在大规模上更容易获得的单个时间点图像。因此，一种可以从异步、静态细胞图像推断蛋白质细胞周期依赖性的方法将更可取。在这项工作中，我们展示了一种可以将静态图像中的蛋白质模式变化与细胞周期进程相关联的方法。我们还表明，细胞周期进程和蛋白质特征模式的一维参数化足以推断定位与细胞周期之间的关联。
