Correlative SIM-STORM Microscopy.

Burri O; Laroche T; Guiet R; Seitz A

doi:10.1007/978-1-4939-7265-4_8

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Journal article

Correlative SIM-STORM Microscopy.

Burri O BioImaging and Optics Platform (BIOP), Ecole Polytechnique Fédérale de Lausanne (EPFL), Faculty of Life Sciences (SV), Station 15, 1015, Lausanne, Switzerland.
Laroche T BioImaging and Optics Platform (BIOP), Ecole Polytechnique Fédérale de Lausanne (EPFL), Faculty of Life Sciences (SV), Station 15, 1015, Lausanne, Switzerland.
Guiet R BioImaging and Optics Platform (BIOP), Ecole Polytechnique Fédérale de Lausanne (EPFL), Faculty of Life Sciences (SV), Station 15, 1015, Lausanne, Switzerland.
Seitz A BioImaging and Optics Platform (BIOP), Ecole Polytechnique Fédérale de Lausanne (EPFL), Faculty of Life Sciences (SV), Station 15, 1015, Lausanne, Switzerland. arne.seitz@epfl.ch.

2017-09-20

Published in:

Methods in molecular biology (Clifton, N.J.). - 2017

Stochastic optical reconstruction microscopy

Structured illumination microscopy

English The ability to specifically label subcellular structures or even proteins of interest in combination with the ability to look at live specimens turned fluorescence light microscopy into an invaluable tool. However, conventional light microscopy is diffraction limited, which restricts the lateral resolution to around 200 nm laterally and 600-800 nm axially. In 2014, the Nobel Prize in Chemistry was awarded to Eric Betzig, Stefan W. Hell, and William E. Moerner for the development of super-resolved fluorescent microscopy techniques. Since then, it has become evident that imaging techniques that enable the visualization of structures below the diffraction limit are essential for the field of life sciences. However, each one of these approaches has inherent advantages and limitations. Here, we describe an imaging workflow suitable for combining structured illumination microscopy (SIM) with direct stochastic optical reconstruction microscopy (dSTORM) data. This is invaluable, since it allows us to put highly resolved dSTORM data into its cellular context.

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