PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data.
Mol Syst Biol
; 20(5): 521-548, 2024 May.
Article
em En
| MEDLINE
| ID: mdl-38472305
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.
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MEDLINE
Assunto principal:
Saccharomyces cerevisiae
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Análise de Célula Única
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Microscopia de Fluorescência
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article