DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae
DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae
复制标题
DeepPhagy：用于定量测量酿酒酵母自噬活性的深度学习框架
DOI：
10.1080/15548627.2019.1632622
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发表时间：
2019-06-22
期刊：
影响因子：
13.3
通讯作者：
Xue, Yu
中科院分区：
文献类型：
--
作者：
Zhang, Ying;Xie, Yubin;Xue, Yu
眼见为实。在共聚焦显微镜下直接观察GFP-Atg 8空泡递送是用于监测酵母大自噬/自噬的最有用的终点测量之一。然而，从大规模图像集中手动标记单个细胞是耗时且劳动密集型的，这极大地阻碍了其在功能筛选中的广泛使用。在本文中，我们对酿酒酵母中35个AuTophaGy相关（ATG）基因的野生型和敲除突变体中氮饥饿诱导的自噬进行了时程分析，并获得了1，944张共聚焦图像，其中包含> 200，000个细胞。我们手动标记了8，078个自噬细胞和18，493个非自噬细胞作为基准数据集，并开发了一种新的自噬深度学习工具（DeepPhagy），与其他现有方法相比，该工具在识别自噬细胞方面表现出上级的准确性，10倍交叉验证的曲线下面积（AUC）值为0.9710。我们进一步使用DeepPhagy自动分析所有图像，并将35个atg敲除突变体的自噬表型定量分为3类。我们的计算和生化结果的高度一致性表明了DeepPhagy测量自噬活性的可靠性。此外，我们使用DeepPhagy分析了另外3种类型的自噬表型，包括Atg 1-GFP靶向空泡，GFP-Atg 19的空泡递送，以及GFP-Atg 8指示的自噬体的解体，所有这些都具有令人满意的准确性。综上所述，我们的研究不仅使GFP-Atg 8荧光检测成为分析S.但也证明了基于深度学习的方法可能适用于不同类型的自噬。
Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (ATG) genes in Saccharomyces cerevisiae and obtained 1,944 confocal images containing > 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 atg knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 fluorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in S. cerevisiae but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy.