{"doi":"10.1101/2023.11.09.566477","title":"Proximity Labeling Expansion Microscopy (PL-ExM) resolves structure of the interactome","abstract":"Elucidating the spatial relationships within the protein interactome is pivotal to understanding the organization and regulation of protein-protein interactions. However, capturing the 3D architecture of the interactome presents a dual challenge: precise interactome labeling and super-resolution imaging. To bridge this gap, we present the Proximity Labeling Expansion Microscopy (PL-ExM). This innovation combines proximity labeling (PL) to spatially biotinylate interacting proteins with expansion microscopy (ExM) to increase imaging resolution by physically enlarging cells. PL-ExM unveils intricate details of the 3D interactome's spatial layout in cells using standard microscopes, including confocal and Airyscan. Multiplexing PL-ExM imaging was achieved by pairing the PL with immunofluorescence staining. These multicolor images directly visualize how interactome structures position specific proteins in the protein-protein interaction network. Furthermore, PL-ExM stands out as an assessment method to gauge the labeling radius and efficiency of different PL techniques. The accuracy of PL-ExM is validated by our proteomic results from PL mass spectrometry. Thus, PL-ExM is an accessible solution for 3D mapping of the interactome structure and an accurate tool to access PL quality.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":401553,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9475,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":584314,"name":"Xiaorong Wang","orcid":"0000-0002-9332-9571","position":1,"is_corresponding":false},{"id":568291,"name":"Xiangpeng Li","orcid":"0000-0002-4230-5676","position":2,"is_corresponding":false},{"id":1178519,"name":"Xiao Jun Huang","orcid":"0000-0003-1906-5819","position":3,"is_corresponding":false},{"id":1178986,"name":"Katie C. Fong","orcid":null,"position":4,"is_corresponding":false},{"id":289624,"name":"Clinton Yu","orcid":"0000-0002-2931-5474","position":5,"is_corresponding":false},{"id":273173,"name":"Arthur A. Tran","orcid":null,"position":6,"is_corresponding":false},{"id":631921,"name":"Lorenzo Scipioni","orcid":"0000-0003-2980-1912","position":7,"is_corresponding":false},{"id":564555,"name":"Zhipeng Dai","orcid":"0000-0003-4716-3096","position":8,"is_corresponding":false},{"id":289627,"name":"Lan Huang","orcid":"0000-0002-3140-4687","position":9,"is_corresponding":false},{"id":564556,"name":"Xiaoyu Shi","orcid":"0000-0002-9634-2659","position":10,"is_corresponding":false},{"id":1178518,"name":"Sohyeon Park","orcid":"0000-0003-0986-2128","position":0,"is_corresponding":true}],"reference_count":55,"raw_metadata":null,"created_at":"2026-07-19T01:20:12.316631Z","pmid":"38014020","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":[]}