1. PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data.
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Razdaibiedina, Anastasia, Brechalov, Alexander, Friesen, Helena, Mattiazzi Usaj, Mojca, Masinas, Myra Paz David, Garadi Suresh, Harsha, Wang, Kyle, Boone, Charles, Ba, Jimmy, and Andrews, Brenda
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SUPERVISED learning , *TASK analysis , *PROTEIN-protein interactions , *FLUORESCENCE microscopy , *PROTEINS , *PROTEIN analysis - Abstract
Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website (https://thecellvision.org/pifia/), PIFiA is a resource for the quantitative analysis of protein organization within the cell. Synopsis: PIFiA is a self-supervised deep-learning approach for protein functional annotation from single-cell images. It generates feature profiles from images of the yeast ORF-GFP collection that can be used in downstream analyses. PIFiA features identify new functional groups of proteins within organelles and proteins with heterogeneous localizations. PIFiA features successfully predict protein–protein interactions and members of protein complexes. PIFiA outperforms previous methods on four different standards of protein function. Images and analysis are available at thecellvision.org/pifia. PIFiA is a self-supervised deep-learning approach for protein functional annotation from single-cell images. It generates feature profiles from images of the yeast ORF-GFP collection that can be used in downstream analyses. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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