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Advancing Spectrally-Resolved Single Molecule Localization Microscopy with Deep Learning.

Hanna Manko1, Yves Mély2, Julien Godet3,4

  • 1Laboratoire de BioImagerie et Pathologies, UMR CNRS 7021, ITI InnoVec, Université de Strasbourg, Illkirch, 67401, France.

Small (Weinheim an Der Bergstrasse, Germany)
|April 24, 2023
PubMed
Summary
This summary is machine-generated.

Spectrally-resolved single molecule localization microscopy (srSMLM) performance is enhanced by srUnet, a deep learning tool. This method improves spatial and spectral resolution, especially for low-emitting molecules in multicolor imaging.

Keywords:
Unetsdeep learningspectrally resolved single molecule localization microscopy

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Area of Science:

  • Biophysics
  • Optical Microscopy
  • Computational Biology

Background:

  • Single molecule localization microscopy (SMLM) provides nanoscale resolution imaging.
  • Spectrally-resolved SMLM (srSMLM) adds spectral information but faces resolution trade-offs due to photon budget limitations.

Purpose of the Study:

  • To develop a deep learning approach to enhance both spatial and spectral resolution in srSMLM.
  • To overcome the inherent resolution loss in srSMLM caused by simultaneous spectral data acquisition.

Main Methods:

  • Implementation of srUnet, a deep learning Unet-based image processing routine.
  • Training srUnet to increase spectral and spatial signals for srSMLM data.
  • Post-processing analysis of srSMLM data using srUnet.

Main Results:

  • srUnet significantly improves localization and spectral precision, particularly for low-emitting species.
  • The method enhances the proportion of localizations with reliable spatial and spectral characterization.
  • Spectral shifts and light dispersion linearity are preserved, simplifying multicolor experiments.

Conclusions:

  • srUnet effectively compensates for resolution loss in srSMLM.
  • This deep learning tool boosts srSMLM performance, approaching conventional SMLM capabilities.
  • srUnet has the potential to establish srSMLM as a standard for multicolor single molecule imaging.