Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min−1. The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics.

Smart fluorophores", such as reversibly switchable fluorescent proteins (RSFPs), are crucial for advanced fluorescence imaging. However, only a limited number of such labels is available and many display reduced biological performance compared to more classical variants.We present the development of robustly photoswitchable variants of EGFP, named rsGreens, that display up to 30-fold higher fluorescence in E. coli colonies grown at 37°C and more than 4-fold higher fluorescence when expressed in HEK293T cells compared to their ancestor protein rsEGFP. This enhancement is not due to an intrinsic increase in the fluorescence brightness of the probes, but rather due to enhanced expression levels that allow many more probe molecules to be functional at any given time. We developed rsGreens displaying a range of photoswitching kinetics and show how these can be used for multi-modal diffraction-unlimited fluorescence imaging such as pcSOFI and RESOLFT, achieving a spatial resolution of ~70 nm. By determining the first ever crystal structures of a negative reversibly switchable FP derived from Aequorea victoria in both the "on"-and "off"-conformation we were able to confirm the presence of a cis-trans isomerization and provide further insights into the mechanisms underlying the photochromism. Our work demonstrates that genetically encoded "smart fluorophores" can be readily optimized for biological performance, and provides a practical strategy for developing maturation-and stability-enhanced photochromic fluorescent proteins.KEYWORDS: fluorescent proteins, reversible photoswitching, super-resolution fluorescence microscopy, SOFI, RESOLFT, crystal structure determination, rsEGFP, superfolder Fluorescent proteins (FPs) enable the minimally-invasive labeling of intracellular structures in live systems. 1 The discovery and development of "smart photoactive FPs", 2,3 with features such as irreversible photoactivation and photoconversion, or reversible photoswitching, allowed the development of diffraction-unlimited imaging techniques such as (f)PALM 4,5 ((fluorescence) photoactivated localization microscopy), RESOLFT 6 (reversible saturable optical fluorescence transitions) and (pc)SOFI 7,8 ((photochromic) stochastic optical fluctuation imaging). These techniques strongly rely on the performance of the fluorophores and considerable efforts have therefore been dedicated to create optimized "smart labels". 9 This is exemplified by the continuous optimization and diversification of the EosFP family, 10-15 or the development of Dronpa 16 mutants with different or added photophysical properties. [17][18][19][20][21][22] Probes that combine multiple "smart" behaviors have also been engineered. [23][24][25] On the whole, however, the general acceptance of the FP-based "smart labels" has not quite risen up to the high expectations set by the many applications they enable. In some cases this is due to concerns surrounding the biological compatibility of the labels, meaning that the label may interfere with the functioning of the syst...

Diffraction-unlimited fluorescence imaging allows the visualization of intact, strongly heterogeneous systems at unprecedented levels of detail. Beyond the acquisition of detailed pictures, increasing efforts are now being focused on deriving quantitative insights from these techniques. In this work, we review the recent developments on sub-diffraction quantization that have arisen for the various techniques currently in use. We pay particular attention to the information that can be obtained but also the practical problems that can be faced, and provide suggestions for solutions or workarounds. We also show that these quantitative metrics not only provide a way to turn raw data into hard statistics but also help to understand the features and pitfalls associated with sub-diffraction imaging. Ultimately, these developments will lead to a highly standardized and easily applicable toolbox of techniques, which will find widespread application in the scientific community.

Super-resolution optical fluctuation imaging (SOFI) allows one to perform sub-diffraction fluorescence microscopy of living cells. By analyzing the acquired image sequence with an advanced correlation method, i.e. a high-order cross-cumulant analysis, super-resolution in all three spatial dimensions can be achieved. Here we introduce a software tool for a simple qualitative comparison of SOFI images under simulated conditions considering parameters of the microscope setup and essential properties of the biological sample. This tool incorporates SOFI and STORM algorithms, displays and describes the SOFI image processing steps in a tutorial-like fashion. Fast testing of various parameters simplifies the parameter optimization prior to experimental work. The performance of the simulation tool is demonstrated by comparing simulated results with experimentally acquired data.

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.

Super-resolution optical fluctuation imaging overcomes the diffraction limit by analyzing fluctuations in the fluorophore emission. A key assumption of the imaging is that the fluorophores are independent, though this is invalidated in the presence of photodestruction. In this work, we evaluate the effect of photodestruction on SOFI imaging using theoretical considerations and computer simulations. We find that photodestruction gives rise to an additional signal that does not present an easily interpretable view of the sample structure. This additional signal is strong and the resulting images typically exhibit less noise. Accordingly, these images may be mis-interpreted as being more visually pleasing or more informative. To address this uncertainty, we develop a procedure that can robustly estimate to what extent any particular experiment is affected by photodestruction. We also develop a detailed assessment methodology and use it to evaluate the performance of several correction algorithms. We identify two approaches that can correct for the presence of even strong photodestruction, one of which can be implemented directly in the SOFI calculation software.

We expand photochromic super-resolution optical fluctuation imaging (pcSOFI) to monochromatic dual-channel sub-diffraction microscopy. Multi-tau (mt-)pcSOFI unmixes spectrally identical reversibly switchable fluorescent proteins (RSFPs) based on their blinking kinetics. We show that mt-pcSOFI can be used to simultaneously image two structures in living cells with existing RSFPs and the newly developed ffDronpa-F.

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