期刊
NATURE METHODS
卷 19, 期 8, 页码 995-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41592-022-01541-z
关键词
-
资金
- Japan Society for the Promotion of Science
- Chan Zuckerberg Biohub
Cytoself is a self-supervised deep learning-based approach that accurately predicts and clusters protein localization from fluorescence images, providing insights into the diversity and complexity of cellular architecture.
Cytoself is a self-supervised deep learning-based approach for profiling and clustering protein localization from fluorescence images. Cytoself outperforms established approaches and can accurately predict protein subcellular localization. Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself's ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches. Moreover, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret them in the context of the fluorescence images, and analyze the performance contributions of each component of our approach.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据