Structural basis for the influence of a single mutation K145N on the oligomerization and photoswitching rate of Dronpa

The crystal structure of the on-state of PDM1-4, a single-mutation variant of the photochromic fluorescent protein Dronpa, is reported at 1.95 angstrom resolution. PDM1-4 is a Dronpa variant that possesses a slower off-switching rate than Dronpa and thus can effectively increase the image resolution in subdiffraction optical microscopy, although the precise molecular basis for this change has not been elucidated. This work shows that the Lys145Asn mutation in PDM1-4 stabilizes the interface available for dimerization, facilitating oligomerization of the protein. No significant changes were observed in the chromophore environment of PDM1-4 compared with Dronpa, and the ensemble absorption and emission properties of PDM1-4 were highly similar to those of Dronpa. It is proposed that the slower off-switching rate in PDM1-4 is caused by a decrease in the potential flexibility of certain beta-strands caused by oligomerization along the AC interface

Model-free uncertainty estimation in stochastical optical fluctuation imaging (SOFI) leads to a doubled temporal resolution

Stochastic optical fluctuation imaging (SOFI) is a super-resolution fluorescence imaging technique that makes use of stochastic fluctuations in the emission of the fluorophores. During a SOFI measurement multiple fluorescence images are acquired from the sample, followed by the calculation of the spatiotemporal cumulants of the intensities observed at each position. Compared to other techniques, SOFI works well under conditions of low signal-to-noise, high background, or high emitter densities. However, it can be difficult to unambiguously determine the reliability of images produced by any superresolution imaging technique. In this work we present a strategy that enables the estimation of the variance or uncertainty associated with each pixel in the SOFI image. In addition to estimating the image quality or reliability, we show that this can be used to optimize the signal-to-noise ratio (SNR) of SOFI images by including multiple pixel combinations in the cumulant calculation. We present an algorithm to perform this optimization, which automatically takes all relevant instrumental, sample, and probe parameters into account. Depending on the optical magnification of the system, this strategy can be used to improve the SNR of a SOFI image by 40% to 90%. This gain in information is entirely free, in the sense that it does not require additional efforts or complications. Alternatively our approach can be applied to reduce the number of fluorescence images to meet a particular quality level by about 30% to 50%, strongly improving the temporal resolution of SOFI imaging

Fluorescent protein design for superresolution microscopy. Exploring the power of protein engineering

A profound insight into life can only be obtained by studying living systems with high spatiotemporal resolution. Until now, the most powerful method for doing this is light microscopy. Light microscopy allows us to study living systems, be it cells or complete organisms, with a submicrometer spatial and subsecond temporal resolution. To study specific molecules or reactions amidst the multitude of processes going on, one typically labels one specific molecule or process with a fluorescent marker, and images the system with fluorescence microscopy. Traditionally, this is done using small organic fluorophores or fluorescent proteins. Fluorescent proteins (FPs) are proteins that contain a fluorophore that is autocatalytically formed and absorbs and emits in the visible wavelength region. Being genetically encoded, they are ubiquitously used as reporter genes and as highly specific markers for fluorescence imaging. After the initial discovery of green fluorescent proteins, many variants with modified and improved properties were made. For instance, while the first FP was a green FP, the first variants had altered excitation and emission spectra. Nowadays, FPs spanning almost the entire visible range are available. One interesting subtype of fluorescent proteins are what we call “photophysically smart labels”, the photophysical behavior of which is dependent on the light with which they have been irradiated. These labels’ emissive properties depend on the light they have encountered before and are of crucial importance in diffraction-unlimited fluorescence microscopy. We call this class of FPs the phototransformable FPs. Examples of phototransformable fluorescent proteins are reversibly photoswitchable FPs and irreversibly photoconvertible FPs. In this dissertation, I introduce some basic concepts and techniques regarding the work that follows. Then, I describe in two publications my contribution to the repertoire of phototransformable FPs: in Chapter 2, I describe how I could rationally design a FP that is both reversibly photoswitchable from a bright to a dark state as well as irreversibly photoconvertible from a green to a red state. I did this by introducing photochromic behavior into Dendra2, a photoconvertible FP. In Chapter 3, I went the other way around. Using rational and random mutagenesis, I could introduce green-to-red photoconversion behavior into the green photochromic FP Dronpa. These studies have led to two new FPs, namely NijiFP (based on Dendra2) and pcDronpa2 (based on Dronpa). I showed that these labels can be used in advanced microscopy applications, including diffraction-unlimited fluorescence microscopy. In the last two chapters, the focus is on the microscopy, more specifically photochromic stochastic optical fluctuation imaging (pcSOFI). pcSOFI is a technique that allows an improvement of spatial resolution by making use of the intrinsic flickering of fluorophores. Chapter 4 is a reprint of a book chapter in which I first describe reversibly photoswitchable FPs and their applications in diffraction-unlimited fluorescence microscopy. In a second part, I describe how the reversibly photoswitchable FP Dronpa can be used to do pcSOFI. In Chapter 5 then, I tested a number of FPs as to their performance in pcSOFI microscopy. From this study, it was found that EGFP, the most widely used FP, typically seen as a “non-smart fluorophore”, is an ideal label for pcSOFI. The results that I obtained and describe in this dissertation have contributed to a broader understanding of FPs at an atomic level. Concretely, they have shown how particular residues influence particular photophysical properties. Next to these fundamental insights, I also made several new FPs, the most important of which are NijiFP, ffDronpa and pcDronpa2. In the second, microscopy-oriented part of this dissertation, I focused on pcSOFI microscopy. Via the step-by-step guide and the testing and scoring of different labels, I hope to have broadened the application area of pcSOFI and hope to have brought this simple and robust technique to a non-specialized public.status: publishe

A comprehensive dataset of image sequences covering 20 fluorescent protein labels and 12 imaging conditions for use in super-resolution imaging.

Super-resolution fluorescence microscopy techniques allow imaging fluorescently labelled structures with a resolution that surpasses the diffraction limit of light (approx. 200nm). The quality and, thus, reliability of each of these techniques is strongly dependent on (1) the quality of the optics, (2) the fitness of the specific fluorescent label for the given technique and (3) the algorithms being used. Of these, the fitness of the labels is most subjective, as fitness metrics are scarce, and generating samples with different labels and imaging them is laborious. This prevent rigorous fitness assessment of fluorescent labels. We have developed a mathematical framework for assessing the quality of SOFI data [1], [2], which we used to assess the fitness of 20 different fluorescent protein labels for SOFI imaging. Here, we report this dataset of 2240 image sequences, representing 10 fields of view each of transfected Cos7 cells expressing each of the 20 different fluorescent proteins under 4-12 imaging conditions. The labels span the visible spectrum and include non-photo-transforming and photo-transforming fluorescent proteins. The imaging conditions consist of 4 different excitation powers, each with three different powers of 405 nm light added (except for the blue labels that are excited with 405 nm light). Though this data was in essence generated to assess which labels are best suited for SOFI imaging, it can be used as a benchmark for further development of the SOFI algorithm, or for the development of other super-resolution imaging modalities that benefit from similar input data.status: Published onlin

pcSOFI as a Smart Label-Based Superresolution Microscopy Technique

Stochastic optical fluctuation imaging (SOFI) is a superresolution imaging technique that uses the flickering of fluorescent labels to generate a microscopic image with a resolution better than what the diffraction limit allows. Its adaptation towards fluorescent protein-labeled samples (called photoconversion SOFI or pcSOFI) allows for a straightforward and easily accessible way of generating superresolution images. In this protocol, we will discuss how so-called "smart labels," and specifically the reversibly switchable fluorescent proteins, have opened doors towards superresolution imaging in general and we provide a protocol on how to perform pcSOFI on HeLa cells expressing human β-actin labeled with the reversibly photoswitchable fluorescent protein Dronpa.status: publishe

Genetically encoded biosensors based on innovative scaffolds

Genetically encoded biosensors are indispensable tools for visualizing the spatiotemporal dynamics of analytes or processes in living cells in vitro and in vivo. Their widespread adaptation has gone hand in hand with the development of sensors for new analytes or processes and improved functionality and robustness. In this review, we highlight some of the recent advances in genetically encoded biosensor development, with a special focus on novel and innovative scaffolds that will lead to new possibilities in the future.status: publishe

Diffraction-unlimited fluorescence microscopy of living biological samples using pcSOFI

The complex microscopic nature of many live biological processes is often obscured by the diffraction limit of light, requiring diffraction-unlimited fluorescence microscopy to resolve them. Because of the vast range of different processes that can be studied, sub-diffraction imaging should work efficiently under many different conditions. Photochromic stochastic optical fluctuation imaging (pcSOFI) is a recent addition to the field of diffraction-unlimited fluorescence microscopy. This robust and versatile method employs a statistical analysis of random fluctuations in the emission of single labels, in this case reversibly switchable fluorescent proteins (RSFPs), to retrieve super-resolution information. Added to the resolution enhancement, pcSOFI also offers contrast enhancement and background reduction in a practical and convenient way. Here, we describe the necessary steps to obtain diffraction-unlimited images, including multicolor and three-dimensional imaging, and highlight the advantages of pcSOFI together with the circumstances under which pcSOFI can be favorably applied.status: publishe

SOFIevaluator: a strategy for the quantitative quality assessment of SOFI data

Super-resolution fluorescence imaging techniques allow optical imaging of specimens beyond the diffraction limit of light. Super-resolution optical fluctuation imaging (SOFI) relies on computational analysis of stochastic blinking events to obtain a super-resolved image. As with some other super-resolution methods, this strong dependency on computational analysis can make it difficult to gauge how well the resulting images reflect the underlying sample structure. We herein report SOFIevaluator, an unbiased and parameter-free algorithm for calculating a set of metrics that describes the quality of super-resolution fluorescence imaging data for SOFI. We additionally demonstrate how SOFIevaluator can be used to identify fluorescent proteins that perform well for SOFI imaging under different imaging conditions.shorttitle: SOFIevaluator urldate: 2020-01-14 file: Submitted Version:/Users/peter/Zotero/storage/LTTQBTXF/Moeyaert et al. - 2020 - SOFIevaluator a strategy for the quantitative qua.pdf:application/pdfstatus: publishe