Advancing Spectrally-Resolved Single Molecule Localization Microscopy with Deep Learning.
Fiche publication
Date publication
avril 2023
Journal
Small (Weinheim an der Bergstrasse, Germany)
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MELY Yves
Tous les auteurs :
Manko H, Mély Y, Godet J
Lien Pubmed
Résumé
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.
Mots clés
Unets, deep learning, spectrally resolved single molecule localization microscopy
Référence
Small. 2023 04 24;:e2300728