{"doi":"10.1038/s44320-024-00029-6","title":"PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data","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.","journal":"Molecular Systems Biology","year":2024,"id":432845,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":14,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9265,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1166929,"name":"Alexander Brechalov","orcid":"0000-0003-0540-4517","position":1,"is_corresponding":false},{"id":344022,"name":"Helena Friesen","orcid":"0000-0001-8925-7440","position":2,"is_corresponding":false},{"id":268499,"name":"Mojca Mattiazzi Ušaj","orcid":"0000-0002-7389-8478","position":3,"is_corresponding":false},{"id":346189,"name":"Myra Paz David Masinas","orcid":null,"position":4,"is_corresponding":false},{"id":1166930,"name":"Harsha Garadi Suresh","orcid":"0000-0002-8286-8991","position":5,"is_corresponding":false},{"id":1166931,"name":"Kyle Wang","orcid":"0000-0003-3699-9285","position":6,"is_corresponding":false},{"id":94094,"name":"Charles Boone","orcid":"0000-0002-3542-6760","position":7,"is_corresponding":false},{"id":1166932,"name":"Jimmy Ba","orcid":"0009-0000-9062-4180","position":8,"is_corresponding":false},{"id":94104,"name":"Brenda Andrews","orcid":"0000-0001-6427-6493","position":9,"is_corresponding":false},{"id":1166928,"name":"Anastasia Razdaibiedina","orcid":"0000-0002-0572-1136","position":0,"is_corresponding":true}],"reference_count":57,"raw_metadata":null,"created_at":"2026-07-19T01:59:39.937122Z","pmid":"38472305","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}