Advancing Spectrally‐Resolved Single Molecule Localization Microscopy with Deep Learning

Abstract: Spectrally‐resolved single molecule localization microscopy (srSMLM) is a recent technique enriching single molecule localization microscopy with the simultaneous recording of spectra of the single emitters. srSMLM resolution is limited by the number of photons collected per emitters. Sharing a photon budget to record the localization and the spectroscopic information results in a loss of spatial and spectral resolution—or forces the sacrifice of one at the expense of the other. Here, srUnet—a deep‐learning Unet‐based image processing routine trained to increase the spectral and spatial signals to compensate for the resolution loss inherent in additionally recording the spectral component is reported. Both localization and spectral precision are improved by srUnet—particularly for the low‐emitting species. srUnet increases the fraction of localization whose signal can be both spatially and spectrally characterized. It preserves spectral shifts and the linearity of the dispersion of light. It strongly facilitates wavelength assignment in multicolor experiments. srUnet is a simple post‐processing add‐on boosting srSMLM performance close to conventional SMLM with the potential to turn srSMLM into the new standard for multicolor single molecule imaging.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Advancing Spectrally‐Resolved Single Molecule Localization Microscopy with Deep Learning ; day:24 ; month:04 ; year:2023 ; extent:9 ; Small ; (24.04.2023) (gesamt 9)

Beteiligte, Orts- und Zeitangaben

• Urheber: Manko, Hanna ; Mély, Yves ; Godet, Julien

Weitere Informationen

• DOI: 10.1002/smll.202300728
• URN: urn:nbn:de:101:1-2023042515420556094213
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:58 MESZ