Information enhanced fluorescence imaging based on smart labels

Biology is complex. While fluorescence microscopy offers a beneficial approach to gain insights into life science questions, the biological complexity requires continuous improvement of existing techniques. Common needs are the enhancement of the image quality and reduction of imaging artifacts, quantification of studied analytes and molecular properties, as well as the visualization of multiple biological structures simultaneously. In this thesis, I am addressing the aforementioned challenges and propose four different solution approaches that can be used with or are enabled by smart labels - so-called reversibly switchable fluorescent proteins. First, I am reducing imaging artifacts in super-resolution optical fluctuation imaging, a post-processing microscopy technique, that benefits from the robust stochastic fluctuations of these photoswitchers. The quantification of analytes is enabled through the novel 'photochromism-enabled absolute quantification' method. This approach combines a measurement and data analysis concept and is facilitated through photochromic biosensors. Furthermore, I present the simulation tool AniSim that allows to emulate, design and theoretically study fluorescence anisotropy techniques, followed by a discussion of its application in developing and understanding experiments that leverage the precise control of the states of reversibly switchable fluorescent proteins. This facilitates the quantified study of large, highly asymmetric or bound molecules. Lastly, a multiplexing technique is introduced allowing to unmix four spectrally and spatially overlapping fluorophores with only four acquired images. This method enables the distinction of various labelled structures and profits from the different light-induced dynamics of photoswitchers.