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Visualization of biomolecular interactions and sensing in living systems with super-resolution fluorescence microscopy.

Fluorescence microscopy is a powerful method to study living systems with high spatial and temporal resolution. However, the resolution of a conventional microscope is limited by diffraction, which precludes the direct visualization of many biological processes occurring at their small scale. During the last decade, several super-resolution fluorescence microscopy methods have been developed that break this diffraction limit and offer a new revolutionary view on structures with a size in the range of 100 nm and smaller. One of these techniques is pcSOFI, which distills sub-diffraction information out of a statistical analysis of blinking fluorophores.

The necessary blinking for pcSOFI is most easily generated by using reversibly switchable fluorescent proteins (RSFPs), a class of photochromic derivatives of the green fluorescent protein (GFP), discovered in 1962. The special feature of these genetically encoded fluorophores is their capacity to reversibly switch between a fluorescent on-state and a non-fluorescent off-state, depending on the light with which they are irradiated. Continuous switching between the two states results in fluorescence blinking, suitable for pcSOFI analysis.

Because of the key-role “photophysically smart labels”, such as RSFPs, play in super-resolution imaging, optimal performance of the methods is largely dependent on the quality of the used fluorophores. The development of optimized variants is thus a crucial step towards achieving the full potential of sub-diffraction microscopy.

Within this dissertation, I outline the basics of how to create and characterize improved fluorescent proteins, and describe my efforts in developing RSFPs with beneficial properties for advanced imaging. In Chapter 2, I describe a series of mutants based on Dronpa and Dronpa2, which are a slow and a fast photoswitcher. By creating structural variation and optimizing the expression, I paved the way for the creation of refSOFI, a complementation approach that allows the visualization of protein-protein interactions with super-resolution. Other variants of Dronpa and Dronpa2 were shown to exhibit more fluorescence when expressed at 37°C and maintained efficient photoswitching characteristics.

In Chapter 3, I introduce the rsGreens, which were developed using a strategy especially suited for optimizing “smart” fluorescent proteins. I provide an in depth characterization of a range of mutants, in terms of spectroscopic, photochromic and biological properties. The work on rsGreens is continued in Chapter 4, where I describe the structural analysis of rsGreen0.7 and the lessons learned about biological performance and photoswitching. This information is subsequently used for the structure-guided development of new rsGreen variants with significantly altered photoswitching characteristics.

The final results chapter, Chapter 5, provides an extensive description of pcSOFI and includes an analysis of the method’s performance under different imaging conditions. I also present the first results obtained with multi-tau (mt) pcSOFI, which is a pcSOFI approach that can separate multiple spectrally similar fluorophores based on their blinking behavior.

The development of a large number of new RSFPs, with beneficial biological and photochromic properties significantly increases the number of available “smart probes”. The characterization of the created RSFPs also contributes to a better understanding of the mechanisms behind all the different processes, which may benefit further developments. By aiding the development of refSOFI, by showing the potential of mt-pcSOFI and by performing the quality assessment of regular pcSOFI, I hope to have broadened the application area of pcSOFI and super-resolution microscopy in general.

Date:1 Oct 2011 →  31 Dec 2016
Keywords:Genetically Encoded Biosensors, Fluorescent Proteins, Super-resolution, Fluorescence Microscopy, Protein Design, pcSOFI
Disciplines:Biochemistry and metabolism, Medical biochemistry and metabolism, Sustainable chemistry, Multimedia processing, Biological system engineering, Signal processing
Project type:PhD project