Self-supervised deep learning encodes high-resolution features of protein subcellular localization

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.

Automated summary

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.