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Related Experiment Video

Updated: Feb 22, 2026

Highly Multiplexed, Super-resolution Imaging of T Cells Using madSTORM
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Correlative SIM-STORM Microscopy.

O Burri1, T Laroche1, R Guiet1

  • 1BioImaging and Optics Platform (BIOP), Ecole Polytechnique Fédérale de Lausanne (EPFL), Faculty of Life Sciences (SV), Station 15, 1015, Lausanne, Switzerland.

Methods in Molecular Biology (Clifton, N.J.)
|September 20, 2017
PubMed
Summary
This summary is machine-generated.

Super-resolution microscopy techniques like structured illumination microscopy (SIM) and direct stochastic optical reconstruction microscopy (dSTORM) overcome light diffraction limits. Combining SIM and dSTORM allows high-resolution imaging within the cellular context.

Keywords:
Correlative microscopyNanoscopyStochastic optical reconstruction microscopyStructured illumination microscopy

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Test Samples for Optimizing STORM Super-Resolution Microscopy
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Test Samples for Optimizing STORM Super-Resolution Microscopy
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Area of Science:

  • Cellular and Molecular Imaging
  • Biophysics
  • Optical Microscopy

Background:

  • Fluorescence light microscopy is crucial for live-cell imaging but limited by diffraction.
  • Conventional microscopy resolution is ~200 nm laterally and 600-800 nm axially.
  • Super-resolution microscopy techniques have revolutionized biological imaging.

Purpose of the Study:

  • To present an imaging workflow combining structured illumination microscopy (SIM) and direct stochastic optical reconstruction microscopy (dSTORM).
  • To enable high-resolution dSTORM data visualization within its cellular context.

Main Methods:

  • Structured Illumination Microscopy (SIM)
  • Direct Stochastic Optical Reconstruction Microscopy (dSTORM)
  • Integrated imaging workflow development.

Main Results:

  • Demonstrated a workflow for combining SIM and dSTORM.
  • Enabled visualization of subcellular structures with super-resolution.
  • Provided cellular context for high-resolution dSTORM data.

Conclusions:

  • The combined SIM and dSTORM workflow enhances biological imaging capabilities.
  • This approach is invaluable for placing super-resolved data into a cellular context.
  • Facilitates deeper understanding of subcellular structures and dynamics.