{"doi":"10.1101/2021.01.08.425961","title":"Super-Resolution Label-free Volumetric Vibrational Imaging","abstract":"Abstract Innovations in high-resolution optical imaging have allowed visualization of nanoscale biological structures and connections. However, super-resolution fluorescence techniques, including both optics-oriented and sample-expansion based, are limited in quantification and throughput especially in tissues from photobleaching or quenching of the fluorophores, and low-efficiency or non-uniform delivery of the probes. Here, we report a general sample-expansion vibrational imaging strategy, termed VISTA, for scalable label-free high-resolution interrogations of protein-rich biological structures with resolution down to 82 nm. VISTA achieves decent three-dimensional image quality through optimal retention of endogenous proteins, isotropic sample expansion, and deprivation of scattering lipids. Free from probe-labeling associated issues, VISTA offers unbiased and high-throughput tissue investigations. With correlative VISTA and immunofluorescence, we further validated the imaging specificity of VISTA and trained an image-segmentation model for label-free multi-component and volumetric prediction of nucleus, blood vessels, neuronal cells and dendrites in complex mouse brain tissues. VISTA could hence open new avenues for versatile biomedical studies.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":215308,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9543,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":252379,"name":"Kun Miao","orcid":"0000-0001-6567-3650","position":1,"is_corresponding":false},{"id":651299,"name":"Li‐En Lin","orcid":"0000-0003-3086-6991","position":2,"is_corresponding":false},{"id":245902,"name":"Xinhong Chen","orcid":"0000-0003-0408-0813","position":3,"is_corresponding":false},{"id":252377,"name":"Jiajun Du","orcid":"0000-0003-2693-834X","position":4,"is_corresponding":false},{"id":252383,"name":"Lu Wei","orcid":"0000-0001-9170-2283","position":5,"is_corresponding":false},{"id":252378,"name":"Chenxi Qian","orcid":"0000-0003-4815-5565","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:52:55.971300Z","pmid":null,"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":[]}