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Extending resolution within a single imaging frame.

Esley Torres-GarcíaRaúl Pinto-CámaraAlejandro LinaresDamián MartínezVíctor AbonzaEduardo Brito-AlarcónCarlos Calcines-CruzGustavo Valdés-GalindoDavid TorresMartina JabloñskiHéctor H Torres-MartínezJosé L MartínezHaydee O HernándezJosé P Ocelotl-OviedoYasel GarcésMarco BarchiRocco D'AntuonoAna BoškovićJoseph G DubrovskyAlberto DarszonMariano G BuffoneRoberto Rodríguez MoralesJuan Manuel Rendon-ManchaChristopher D WoodArmando Hernández-GarcíaDiego KrapfÁlvaro H CrevennaAdán Guerrero
Published in: Nature communications (2022)
The resolution of fluorescence microscopy images is limited by the physical properties of light. In the last decade, numerous super-resolution microscopy (SRM) approaches have been proposed to deal with such hindrance. Here we present Mean-Shift Super Resolution (MSSR), a new SRM algorithm based on the Mean Shift theory, which extends spatial resolution of single fluorescence images beyond the diffraction limit of light. MSSR works on low and high fluorophore densities, is not limited by the architecture of the optical setup and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image stacks, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other SRM approaches. Along with its wide accessibility, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.
Keyphrases
  • single molecule
  • high resolution
  • deep learning
  • convolutional neural network
  • optical coherence tomography
  • high speed
  • machine learning
  • mental health
  • mass spectrometry
  • high throughput
  • energy transfer
  • single cell