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DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae
Ying Zhang1, Yubin Xie2, Wenzhong Liu2
1Department of Bioinformatics and Systems Biology, Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China.
Autophagy
|June 18, 2019
Summary
We developed DeepPhagy, a deep learning tool that accurately quantifies yeast autophagy by analyzing GFP-Atg8 fluorescence. This method automates cell labeling, enabling large-scale functional screens for autophagy research.
Area of Science:
- Cell Biology
- Molecular Biology
- Bioinformatics
Background:
- Monitoring yeast macroautophagy/autophagy via GFP-Atg8 vacuolar delivery is crucial but labor-intensive due to manual cell labeling.
- Existing methods struggle with the scale required for functional screens, hindering comprehensive autophagy research.
Purpose of the Study:
- To develop a deep learning tool, DeepPhagy, for accurate and automated quantification of yeast autophagy.
- To enable large-scale analysis of autophagic phenotypes in Saccharomyces cerevisiae, particularly in AuTophaGy-related (ATG) gene knockout mutants.
Main Methods:
- Time-course analysis of nitrogen starvation-induced autophagy in 35 ATG gene knockout mutants of Saccharomyces cerevisiae.
- Development and validation of DeepPhagy, a deep learning tool, using a benchmark dataset of manually labeled autophagic and non-autophagic cells.
- Application of DeepPhagy for automated analysis of 1,944 confocal images (>200,000 cells) and classification of autophagic phenotypes.
Main Results:
- DeepPhagy achieved high accuracy (AUC = 0.9710) in recognizing autophagic cells, outperforming existing methods.
- Automated analysis classified autophagic phenotypes of 35 atg knockout mutants into 3 classes with high consistency.
- DeepPhagy successfully analyzed additional autophagic phenotypes, including Atg1-GFP targeting, GFP-Atg19 delivery, and GFP-Atg8-indicated autophagic body disintegration.
Conclusions:
- DeepPhagy transforms the GFP-Atg8 fluorescence assay into a quantitative measurement for yeast autophagic phenotypes.
- Deep learning-based methods offer a powerful and scalable approach for analyzing various types of autophagy.
- This study significantly advances the potential for high-throughput functional screens in autophagy research.
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