Wim Vandenberg

Diffraction-unlimited imaging: from pretty pictures to hard numbers.

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.

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions.

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.

SOFI Simulation Tool: A Software Package for Simulating and Testing Super-Resolution Optical Fluctuation Imaging

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.

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.

Effect of probe diffusion on the SOFI imaging accuracy

Live-cell super-resolution fluorescence imaging is becoming commonplace for exploring biological systems, though sample dynamics can affect the imaging quality. In this work we evaluate the effect of probe diffusion on super-resolution optical fluctuation imaging (SOFI), using a theoretical model and numerical simulations based on the imaging of live cells labelled with photochromic fluorescent proteins. We find that, over a range of physiological conditions, fluorophore diffusion results in a change in the amplitude of the SOFI signal. The magnitude of this change is approximately proportional to the on-time ratio of the fluorophores. However, for photochromic fluorescent proteins this effect is unlikely to present a significant distortion in practical experiments in biological systems. Due to this lack of distortions, probe diffusion strongly enhances the SOFI imaging by avoiding spatial undersampling caused by the limited labeling density.

A cost-effective approach to Super-resolution Optical Fluctuation (SOFI) microscopy using an industry-grade CMOS camera

Abstract Super-Resolution (SR) fluorescence microscopy is typically carried out on high-end research microscopes. Super-resolution Optical Fluctuation Imaging (SOFI) is a fast SR technique capable of live-cell imaging, that is compatible with many wide-field microscope systems. However, especially when employing fluorescent proteins, a key part of the imaging system is a very sensitive and well calibrated camera sensor. The substantial costs of such systems preclude many research groups from employing super-resolution imaging techniques. Here, we examine to what extent SOFI can be performed using a range of imaging hardware comprising different technologies and costs. In particular, we quantitatively compare the performance of an industry-grade CMOS camera to both state-of-the-art emCCD and sCMOS detectors, with SOFI-specific metrics. We show that SOFI data can be obtained using a cost-efficient industry-grade sensor, both on commercial and home-built microscope systems, though our analysis also readily exposes the merits of the per-pixel corrections performed in scientific cameras.Super-resolution (SR) fluorescence microscopy, especially at high speeds, is typically carried out on high-performance research microscopes. The substantial cost of such equipment, combined with the limited distribution of such instruments in imaging facilities, have complicated access to super-resolution imaging for research groups in the biosciences. In this work we promote the accessibility of Super-resolution Optical Fluctuation Imaging (SOFI) to the scientific community by demonstrating its flexibility in terms of the minimal required performance of the imaging system. We show that SOFI data can be acquired using a very cost-efficient industrial-grade detector on both a standard research microscope and an entirely home-built wide-field system. This enables more scientists to enter the field of live-cell super-resolution imaging, as it provides access to a robust and fast SR imaging modality at comparatively modest cost.

Reduced Fluorescent Protein Switching Fatigue by Binding-Induced Emissive State Stabilization

Reversibly switchable fluorescent proteins (RSFPs) enable advanced fluorescence imaging, though the performance of this imaging crucially depends on the properties of the labels. We report on the use of an existing small binding peptide, named Enhancer, to modulate the spectroscopic properties of the recently developed rsGreen series of RSFPs. Fusion constructs of Enhancer with rsGreen1 and rsGreenF revealed an increased molecular brightness and pH stability, although expression in living E. coli or HeLa cells resulted in a decrease of the overall emission. Surprisingly, Enhancer binding also increased off-switching speed and resistance to switching fatigue. Further investigation suggested that the RSFPs can interconvert between fast- and slow-switching emissive states, with the overall protein population gradually converting to the slow-switching state through irradiation. The Enhancer modulates the spectroscopic properties of both states, but also preferentially stabilizes the fast-switching state, supporting the increased fatigue resistance. This work demonstrates how the photo-physical properties of RSFPs can be influenced by their binding to other small proteins, which opens up new horizons for applications that may require such modulation. Furthermore, we provide new insights into the photoswitching kinetics that should be of general consideration when developing new RSFPs with improved or different photochromic properties.