1. PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data
- Author
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Anastasia Razdaibiedina, Alexander Brechalov, Helena Friesen, Mojca Mattiazzi Usaj, Myra Paz David Masinas, Harsha Garadi Suresh, Kyle Wang, Charles Boone, Jimmy Ba, and Brenda Andrews
- Subjects
Self-supervised ,Machine Learning ,Single-cell ,Imaging ,Protein ,Biology (General) ,QH301-705.5 ,Medicine (General) ,R5-920 - Abstract
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.
- Published
- 2024
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