| Autor: |
Razdaibiedina A; Department of Molecular Genetics, University of Toronto, Toronto ON, Canada.; The Donnelly Centre, University of Toronto, Toronto ON, Canada.; Vector Institute for Artificial Intelligence, Toronto ON, Canada., Brechalov A; Department of Molecular Genetics, University of Toronto, Toronto ON, Canada.; The Donnelly Centre, University of Toronto, Toronto ON, Canada., Friesen H; The Donnelly Centre, University of Toronto, Toronto ON, Canada., Usaj MM; The Donnelly Centre, University of Toronto, Toronto ON, Canada., Masinas MPD; The Donnelly Centre, University of Toronto, Toronto ON, Canada., Suresh HG; The Donnelly Centre, University of Toronto, Toronto ON, Canada., Wang K; Department of Molecular Genetics, University of Toronto, Toronto ON, Canada.; The Donnelly Centre, University of Toronto, Toronto ON, Canada., Boone C; Department of Molecular Genetics, University of Toronto, Toronto ON, Canada.; The Donnelly Centre, University of Toronto, Toronto ON, Canada.; RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama, Japan., Ba J; Department of Computer Science, University of Toronto, Toronto ON, Canada.; Vector Institute for Artificial Intelligence, Toronto ON, Canada., Andrews B; Department of Molecular Genetics, University of Toronto, Toronto ON, Canada.; The Donnelly Centre, University of Toronto, Toronto ON, Canada. |
| Abstrakt: |
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, ( P rotein I mage-based F unct i onal A nnotation), 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. |
