Direct stochastic optical reconstruction microscopy with standard fluorescent probes

Sebastian van de Linde, Anna Löschberger, Teresa Klein, Meike Heidbreder, Steve Wolter, Mike Heilemann, Markus Sauer

Research output: Contribution to journalArticle

541 Citations (Scopus)

Abstract

Direct stochastic optical reconstruction microscopy (dSTORM) uses conventional fluorescent probes such as labeled antibodies or chemical tags for subdiffraction resolution fluorescence imaging with a lateral resolution of ∼20 nm. In contrast to photoactivated localization microscopy (PALM) with photoactivatable fluorescent proteins, dSTORM experiments start with bright fluorescent samples in which the fluorophores have to be transferred to a stable and reversible OFF state. The OFF state has a lifetime in the range of 100 milliseconds to several seconds after irradiation with light intensities low enough to ensure minimal photodestruction. Either spontaneously or photoinduced on irradiation with a second laser wavelength, a sparse subset of fluorophores is reactivated and their positions are precisely determined. Repetitive activation, localization and deactivation allow a temporal separation of spatially unresolved structures in a reconstructed image. Here we present a step-by-step protocol for dSTORM imaging in fixed and living cells on a wide-field fluorescence microscope, with standard fluorescent probes focusing especially on the photoinduced fine adjustment of the ratio of fluorophores residing in the ON and OFF states. Furthermore, we discuss labeling strategies, acquisition parameters, and temporal and spatial resolution. The ultimate step of data acquisition and data processing can be performed in seconds to minutes.

Original languageEnglish
Pages (from-to)991-1009
Number of pages19
JournalNature Protocols
Volume6
Issue number7
DOIs
Publication statusPublished - 16 Jun 2011

Keywords

  • automatic data processing
  • fluorescent dyes
  • microscopy, fluorescence/methods
  • photons
  • software
  • staining and labeling
  • stochastic processes

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