Proteome-scale movements and compartment-connectivity during the eukaryotic cell cycle

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ~20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control, while changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.细胞周期进程依赖于蛋白质组（proteome）的组成与亚细胞定位发生协同变化。本研究针对数百万活酵母细胞的图像，采用两种不同的卷积神经网络（convolutional neural networks）进行分析，解析了细胞周期过程中蛋白质组水平上的浓度与定位动态变化，并区分出约20种亚细胞定位类别。研究表明，四分之一的蛋白质组呈现细胞周期周期性特征，且蛋白质的调控多仅聚焦于定位或浓度单一维度，而非二者兼具。不同的蛋白质调控层级会被优先适配于细胞周期的不同环节：蛋白质浓度变化主要参与细胞周期的调控过程，而蛋白质定位变化则负责细胞周期程序的生物物理执行环节。本研究构建了可用于探索细胞周期全局蛋白质组动态变化的数据集资源，将助力从系统层面理解这一基础生物学过程。