{"doi":"10.1364/oe.501100","title":"Three-dimensional multifocal scanning microscopy for super-resolution cell and tissue imaging","abstract":"Recent advancements in image-scanning microscopy have significantly enriched super-resolution biological research, providing deeper insights into cellular structures and processes. However, current image-scanning techniques often require complex instrumentation and alignment, constraining their broader applicability in cell biological discovery and convenient, cost-effective integration into commonly used frameworks like epi-fluorescence microscopes. Here, we introduce three-dimensional multifocal scanning microscopy (3D-MSM) for super-resolution imaging of cells and tissue with substantially reduced instrumental complexity. This method harnesses the inherent 3D movement of specimens to achieve stationary, multi-focal excitation and super-resolution microscopy through a standard epi-fluorescence platform. We validated the system using a range of phantom, single-cell, and tissue specimens. The combined strengths of structured illumination, confocal detection, and epi-fluorescence setup result in two-fold resolution improvement in all three dimensions, effective optical sectioning, scalable volume acquisition, and compatibility with general imaging and sample protocols. We anticipate that 3D-MSM will pave a promising path for future super-resolution investigations in cell and tissue biology.","journal":"Optics Express","year":2023,"id":351865,"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":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9597,"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":253517,"name":"Biagio Mandracchia","orcid":"0000-0002-4583-4733","position":1,"is_corresponding":false},{"id":1071522,"name":"Kyungduck Yoon","orcid":"0000-0002-3106-9735","position":2,"is_corresponding":false},{"id":640786,"name":"Keyi Han","orcid":"0000-0002-5141-1673","position":3,"is_corresponding":false},{"id":253522,"name":"Shu Jia","orcid":"0000-0001-6854-7537","position":4,"is_corresponding":false},{"id":1072198,"name":"Kidan Tadesse","orcid":null,"position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":null,"created_at":"2026-07-19T01:12:45.897709Z","pmid":"38017958","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":[]}