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For the full sample preparation protocol please refer to [https://doi.org/10.1101/2023.08.23.554438].  The images were taken using custom modified Total-Internal Reflection excitation wide-field fluorescence microscope. Fluorescence is excited with a 638 nm continuous-wave laser (PhoxX+ 638-150 Omicron) coupled into a single-mode fiber (P1-460B-FC-2, Thorlabs). After passing the fiber, light is collimated and expanded by a factor of ∼3.6× using a telescope. The beam was then focused with a lens (f = 200 mm, AC508-200-A-ML, Thorlabs) onto the back focal plane of a 100×/1.4 NA oil immersion objective (UAPON100xO, Olympus). Fluorescence is collected using the same objective (epi-fluorescence setup) and spectrally separated from the excitation light by sending it through a quad-line beam splitter (Di03 R405/488/532/635, Semrock). A tube lens (AC254-200-A-ML, Thorlabs) focuses the light through an adjustable slit beam stop (SP60, OWIS), which limits the field of view. Two additional lenses (f = 100 mm AC254-100-A, Thorlabs, and f = 150 mm AC508-150-A, Thorlabs), allow for adjusting the final image magnification on the camera. A notch filter (ZET635NF, Chroma) is used to additionally suppress any back-reflected/back-scattered excitation light. The light then imaged on an electron-multiplying charge-coupled device (EM-CCD) camera (Andor iXON Ultra 897) after passing through two additional emission longpass filters (ET647lp, Semrock). The final pixel size of the image is 80 nm.   Each measurement required adaptation of certain parameters that were written in the name of the file according to the following scheme:p - power before fiber in mWe - exposure time in msi - interval between frames in msg - electronic multiplier gainf - number of frames"],"publicationDate":"2025-12-15","publisher":"Zenodo","embargoEndDate":"2025-12-15","sources":null,"formats":null,"contributors":null,"coverages":null,"bestAccessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/"},"container":null,"documentationUrls":null,"codeRepositoryUrl":null,"programmingLanguage":null,"contactPeople":null,"contactGroups":null,"tools":null,"size":null,"version":null,"geoLocations":null,"id":"doi_dedup___::4ed72484bd478d12e11c22eec0ddafab","originalIds":["50|datacite____::4ed72484bd478d12e11c22eec0ddafab","10.5281/zenodo.17909379","50|od______2659::4ed72484bd478d12e11c22eec0ddafab","oai:zenodo.org:17909379","50|datacite____::86995a759e3d8b10a9710b3b709e405a","10.5281/zenodo.17909378"],"pids":[{"scheme":"doi","value":"10.5281/zenodo.17909379"},{"scheme":"doi","value":"10.5281/zenodo.17909378"}],"dateOfCollection":null,"lastUpdateTimeStamp":null,"indicators":{"citationImpact":{"citationCount":0.0,"influence":2.2251732E-9,"popularity":2.342466E-9,"impulse":0.0,"citationClass":"C5","influenceClass":"C5","impulseClass":"C5","popularityClass":"C5"}},"projects":[{"id":"corda_____he::68d692c07dc1e53ed7c4a186e87651e6","code":"101119924","acronym":"BE-LIGHT","title":"BE-LIGHT Improving BiomEdical diagnosis through LIGHT-based technologies and machine learning","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/101119924"}]}],"organizations":[{"legalName":"University of Göttingen","acronym":"UGOE","id":"openorgs____::eadc8da90a546e98c03f896661a2e4d4","pids":[{"scheme":"fundref","value":"501100003385"},{"scheme":"PIC","value":"999845640"},{"scheme":"OrgRef","value":"180763"},{"scheme":"GRID","value":"grid.7450.6"},{"scheme":"Wikidata","value":"Q152838"},{"scheme":"fundref","value":"501100003383"},{"scheme":"FundRef","value":"501100003383"},{"scheme":"mag_id","value":"74656192"},{"scheme":"fundref","value":"501100004939"},{"scheme":"OrgRef","value":"47002223"},{"scheme":"ISNI","value":"0000000123644210"},{"scheme":"wikidata","value":"Q152838"},{"scheme":"FundRef","value":"501100003385"},{"scheme":"ROR","value":"https://ror.org/01y9bpm73"},{"scheme":"OrgReg","value":"DE0060"},{"scheme":"FundRef","value":"501100004939"}]}],"communities":[{"code":"eosc","label":"EOSC","provenance":null}],"collectedFrom":[{"key":"openaire____::9e3be59865b2c1c335d32dae2fe7b254","value":"Datacite"},{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"}],"instances":[{"pids":[{"scheme":"doi","value":"10.5281/zenodo.17909379"}],"license":"CC BY","type":"Dataset","urls":["https://dx.doi.org/10.5281/zenodo.17909379"],"publicationDate":"2025-12-15","refereed":"nonPeerReviewed","hostedBy":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"},"collectedFrom":{"key":"openaire____::9e3be59865b2c1c335d32dae2fe7b254","value":"Datacite"}},{"pids":[{"scheme":"doi","value":"10.5281/zenodo.17909379"}],"alternateIdentifiers":[{"scheme":"oai","value":"oai:zenodo.org:17909379"}],"license":"CC BY","accessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/","openAccessRoute":null},"type":"Dataset","urls":["https://zenodo.org/records/17909379","http://dx.doi.org/10.5281/zenodo.17909379"],"publicationDate":"2025-12-15","refereed":"nonPeerReviewed","hostedBy":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"},"collectedFrom":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"}},{"pids":[{"scheme":"doi","value":"10.5281/zenodo.17909378"}],"license":"CC BY","type":"Dataset","urls":["https://dx.doi.org/10.5281/zenodo.17909378"],"publicationDate":"2025-12-15","refereed":"nonPeerReviewed","hostedBy":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"},"collectedFrom":{"key":"openaire____::9e3be59865b2c1c335d32dae2fe7b254","value":"Datacite"}}],"links":[{"header":{"relationType":"resultProject","relationClass":"isProducedBy","relatedIdentifier":"corda_____he::68d692c07dc1e53ed7c4a186e87651e6","relatedRecordType":"project","relationProvenance":"sysimport:crosswalk:repository","trust":"0.9"},"collectedfrom":[{"dsId":"openaire____::3f264f93cf3b0cfc4ede188a6300455c","dsName":"CORDA - 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