Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis
Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis
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DOI：
10.1016/j.cell.2015.04.051
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发表时间：
2015-06-04
期刊：
影响因子：
64.5
通讯作者：
Andrews, Brenda J.
中科院分区：
文献类型：
--
作者：
Chong, Yolanda T.;Koh, Judice L. Y.;Andrews, Brenda J.
事实证明，蛋白质组学在产生大规模定量数据方面是无价的；然而，体内检测蛋白质组的系统方法的发展滞后。为了在蛋白质组尺度上评估蛋白质丰度和定位，我们利用酵母gfp融合收集，结合自动化遗传学，高通量显微镜和计算特征分析。我们开发了一个二元分类器集合，用于从单细胞测量中生成定位数据，并构建了与16个定位类相连的3000个蛋白质的图谱。为了研究不同化学和遗传刺激下的蛋白质组动力学，我们测量了蛋白质组的丰度和定位，并确定了随时间的变化。我们分析了2000万个细胞，以确定在羟基脲、雷帕霉素和rpd3 Delta背景下在多个定位中重新分配的动态蛋白质。由于我们的定位和丰度数据是定量的，它们为许多类型的比较研究、单细胞分析、建模和预测提供了机会。
Proteomics has proved invaluable in generating large-scale quantitative data; however, the development of systems approaches for examining the proteome in vivo has lagged behind. To evaluate protein abundance and localization on a proteome scale, we exploited the yeast GFP-fusion collection in a pipeline combining automated genetics, high-throughput microscopy, and computational feature analysis. We developed an ensemble of binary classifiers to generate localization data from single-cell measurements and constructed maps of similar to 3,000 proteins connected to 16 localization classes. To survey proteome dynamics in response to different chemical and genetic stimuli, we measure proteome-wide abundance and localization and identified changes over time. We analyzed >20 million cells to identify dynamic proteins that redistribute among multiple localizations in hydroxyurea, rapamycin, and in an rpd3 Delta background. Because our localization and abundance data are quantitative, they provide the opportunity for many types of comparative studies, single cell analyses, modeling, and prediction.