• Egger Boris August

• Acuna Guillermo Pedro
• Stefani Fernando Daniel

Fluorescence microscopy has become the preferred imaging tool for biological systems due to its capability to visualize specifically the target biomolecules under conditions compatible with life, like room temperature, liquid environment and irradiation with visible light. Fluorescence microscopy also provides very high sensitivity down to the detection of single molecules. As drawback, as it happens with any a far-field optical technique, the spatial resolution is limited by the wavelength of light to a few hundreds of nanometres (the so-called diffraction limit). Remarkably, in the mid 2000s, a series of imaging methods using fluorescence readout were developed that deliver images with resolution beyond the diffraction limit. These methods, called super-resolution fluorescence microscopy or far-field fluorescence nanoscopy and whose pioneers were awarded with the Nobel Prize in Chemistry in 2014(1–3), have revolutionized biological imaging and continued to be developed until the present day.

In these lectures, we will revise the working principles of super-resolution microscopy and its development from the first generation of methods up to the newest methods capable of achieving 1 nm resolution under ambient conditions(4–7). We will discuss the performance and technical aspects of the necessary hardware and sample preparation for each one of the different modalities of super-resolution microscopy existing today. Also, we will perform experiments hands-on to obtain super-resolved fluorescence images, including the acquisition and analysis of data.

After this course, the student will know:

• The fundamental principles of superresolution fluorescence microscopy in its different modalities: coordinate stochastic single-molecule localization microscopy (e.g., STORM, PALM, DNA-PAINT), coordinate targeted super-resolution microscopy (e.g., STED, RESOLFT), and single-molecule localization through sequential structured illumination (e.g., MINFLUX, RASTMIN).
• How to choose the best suited superresolution method for specific applications: visualizing fixed or living specimens, compromise between resolution and throughput (image acquisition speed), visualizing structures or trajectories.
• How acquire singlemolecule localization data and analyse it to obtain a super-resolved fluorescence image

Jupyter Python course for image processing and analysis. 

Teachers: Ferndando Stefani (Buenos Aires) and Guillermo Acuna (Fribourg)

Written exam (60 min.) during the semester. One mark.

Written exam MC.