Super-resolution microscopy live cell imaging and image analysis

Novel fundamental research results provided new techniques going beyond the diffraction limit. These recent advances known as super-resolution microscopy have been awarded by the Nobel Prize as they promise new discoveries in biology and live sciences. All these techniques rely on complex signal and image processing. The applicability in biology, and particularly for live cell imaging, remains challenging and needs further investigation. Focusing on image processing and analysis, the thesis is devoted to a significant enhancement of structured illumination microscopy (SIM) and super-resolution optical fluctuation imaging (SOFI)methods towards fast live cell and quantitative imaging. The thesis presents a novel image reconstruction method for both 2D and 3D SIM data, compatible with weak signals, and robust towards unwanted image artifacts. This image reconstruction is efficient under low light conditions, reduces phototoxicity and facilitates live cell observations. We demonstrate the performance of our new method by imaging long super-resolution video sequences of live U2-OS cells and improving cell particle tracking. We develop an adapted 3D deconvolution algorithm for SOFI, which suppresses noise and makes 3D SOFI live cell imaging feasible due to reduction of the number of required input images. We introduce a novel linearization procedure for SOFI maximizing the resolution gain and show that SOFI and PALM can both be applied on the same dataset revealing more insights about the sample. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of the sample through the estimation of molecular parameters. For quantifying the outcome of our super-resolutionmethods, the thesis presents a novel methodology for objective image quality assessment measuring spatial resolution and signal to noise ratio in real samples. We demonstrate our enhanced SOFI framework by high throughput 3D imaging of live HeLa cells acquiring the whole super-resolution 3D image in 0.95 s, by investigating focal adhesions in live MEF cells, by fast optical readout of fluorescently labelled DNA strands and by unraveling the nanoscale organization of CD4 proteins on a plasma membrane of T-cells. Within the thesis, unique open-source software packages SIMToolbox and SOFI simulation tool were developed to facilitate implementation of super-resolution microscopy methods

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 challenging task for super-resolution microscopy. We have addressed this important issue by combining photo-activated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed cell focal adhesion images, we investigated the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework was used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualized the dynamics of focal adhesions, and revealed local mean velocities around 190 nm per minute. The complementarity of PALM and SOFI was assessed in detail with a methodology that integrates a quantitative 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 the fluorophore density and the photo-activation and photo-switching rates

Spectral Cross-Cumulants for Multicolor Super-resolved SOFI Imaging

Super-resolution optical fluctuation imaging (SOFI) provides a resolution beyond the diffraction limit by analysing stochastic fluorescence fluctuations with higher-order statistics. Using nth order spatio-temporal cross-cumulants the spatial resolution as well as the sampling can be increased up to n-fold in all three spatial dimensions. In this study, we extend the cumulant analysis into the spectral domain and propose a novel multicolor super-resolution scheme. The simultaneous acquisition of two spectral channels followed by spectral cross-cumulant analysis and unmixing increase the spectral sampling. The number of discriminable fluorophore species is thus not limited to the number of physical detection channels. Using two color channels, we demonstrate spectral unmixing of three fluorophore species in simulations and multiple experiments with different cellular structures, fluorophores and filter sets. Based on an eigenvalue/ vector analysis we propose a scheme for an optimized spectral filter choice. Overall, our methodology provides a novel route for easy-to-implement multicolor sub-diffraction imaging using standard microscopes while conserving the spatial super-resolution property. This makes simultaneous multiplexed super-resolution fluorescence imaging widely accessible to the life science community interested to probe colocalization between two or more molecular species.Comment: main: 21 pages & 4 figures, supplementary 20 pages & 16 figure

Combining PALM and SOFI for quantitative imaging of focal adhesions in living cells

Focal adhesions are complicated assemblies of hundreds of proteins that allow cells to sense their extracellular matrix and adhere to it. Although most focal adhesion proteins have been identified, their spatial organization in living cells remains challenging to observe. Photo-activated localization microscopy (PALM) is an interesting technique for this purpose, especially since it allows estimation of molecular parameters such as the number of fluorophores. However, focal adhesions are dynamic entities, requiring a temporal resolution below one minute, which is difficult to achieve with PALM. In order to address this problem, we merged PALM with super-resolution optical fluctuation imaging (SOFI) by applying both techniques to the same data. Since SOFI tolerates an overlap of single molecule images, it can improve the temporal resolution compared to PALM. Moreover, an adaptation called balanced SOFI (bSOFI) allows estimation of molecular parameters, such as the fluorophore density. We therefore performed simulations in order to assess PALM and SOFI for quantitative imaging of dynamic structures. We demonstrated the potential of our PALM–SOFI concept as a quantitative imaging framework by investigating moving focal adhesions in living cells

High-speed multiplane structured illumination microscopy of living cells using an image-splitting prism

Descloux A, Mueller M, Navikas V, et al. High-speed multiplane structured illumination microscopy of living cells using an image-splitting prism. Nanophotonics. 2020;9(1):143-148.Super-resolution structured illumination microscopy (SR-SIM) can be conducted at video-rate acquisition speeds when combined with high-speed spatial light modulators and sCMOS cameras, rendering it particularly suitable for live-cell imaging. If, however, three-dimensional (3D) information is desired, the sequential acquisition of vertical image stacks employed by current setups significantly slows down the acquisition process. In this work, we present a multiplane approach to SR-SIM that overcomes this slowdown via the simultaneous acquisition of multiple object planes, employing a recently introduced multiplane image splitting prism combined with highspeed SIM illumination. This strategy requires only the introduction of a single optical element and the addition of a second camera to acquire a laterally highly resolved 3D image stack. We demonstrate the performance of multiplane SIM by applying this instrument to imaging the dynamics of mitochondria in living COS-7 cells

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

Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software

With the widespread uptake of two-dimensional (2D) and three-dimensional (3D) single-molecule localization microscopy (SMLM), a large set of different data analysis packages have been developed to generate super-resolution images. In a large community effort, we designed a competition to extensively characterize and rank the performance of 2D and 3D SMLM software packages. We generated realistic simulated datasets for popular imaging modalities—2D, astigmatic 3D, biplane 3D and double-helix 3D—and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D SMLM software and provides a holistic view of how the latest 2D and 3D SMLM packages perform in realistic conditions. This resource allows researchers to identify optimal analytical software for their experiments, allows 3D SMLM software developers to benchmark new software against the current state of the art, and provides insight into the current limits of the field