Super-resolution Optical Fluctuation Imaging Imaging Research Articles

Live-Cell SOFI Correlation with SMLM and AFM Imaging.

Standard optical imaging is diffraction-limited and lacks the resolving power to visualize many of the organelles and proteins found within the cell. The advent of super-resolution techniques overcame this barrier, enabling observation of subcellular structures down to tens of nanometers in size; however these techniques require or are typically applied to fixed samples. This raises the question of how well a fixed-cell image represents the system prior to fixation. Here we present the addition of live-cell Super-Resolution Optical Fluctuation Imaging (SOFI) to a previously reported correlative process using Single Molecule Localization Microscopy (SMLM) and Atomic Force Microscopy (AFM). SOFI was used with fluorescent proteins and low laser power to observe cellular ultrastructure in live COS-7 cells. SOFI-SMLM-AFM of microtubules showed minimal changes to the microtubule network in the 20 min between live-cell SOFI and fixation. Microtubule diameters were also analyzed through all microscopies; SOFI found diameters of 249 ± 68 nm and SMLM was 71 ± 33 nm. AFM height measurements found microtubules to protrude 26 ± 13 nm above the surrounding cellular material. The correlation of SMLM and AFM was extended to two-color SMLM to image both microtubules and actin. Two target SOFI was performed with various fluorescent protein combinations. rsGreen1-rsKAME, rsGreen1-Dronpa, and ffDronpaF-rsKAME fluorescent protein combinations were determined to be suitable for two target SOFI imaging. This correlative application of super-resolution live-cell and fixed-cell imaging revealed minimal artifacts created for the imaged target structures through the sample preparation procedure and emphasizes the power of correlative microscopy.

Correlative 3D microscopy of single cells using super-resolution and scanning ion-conductance microscopy

High-resolution live-cell imaging is necessary to study complex biological phenomena. Modern fluorescence microscopy methods are increasingly combined with complementary, label-free techniques to put the fluorescence information into the cellular context. The most common high-resolution imaging approaches used in combination with fluorescence imaging are electron microscopy and atomic-force microscopy (AFM), originally developed for solid-state material characterization. AFM routinely resolves atomic steps, however on soft biological samples, the forces between the tip and the sample deform the fragile membrane, thereby distorting the otherwise high axial resolution of the technique. Here we present scanning ion-conductance microscopy (SICM) as an alternative approach for topographical imaging of soft biological samples, preserving high axial resolution on cells. SICM is complemented with live-cell compatible super-resolution optical fluctuation imaging (SOFI). To demonstrate the capabilities of our method we show correlative 3D cellular maps with SOFI implementation in both 2D and 3D with self-blinking dyes for two-color high-order SOFI imaging. Finally, we employ correlative SICM/SOFI microscopy for visualizing actin dynamics in live COS-7 cells with subdiffraction-resolution.

Smoothness correction for better SOFI imaging

Sub-diffraction or super-resolution fluorescence imaging allows the visualization of the cellular morphology and interactions at the nanoscale. Statistical analysis methods such as super-resolution optical fluctuation imaging (SOFI) obtain an improved spatial resolution by analyzing fluorophore blinking but can be perturbed by the presence of non-stationary processes such as photodestruction or fluctuations in the illumination. In this work, we propose to use Whittaker smoothing to remove these smooth signal trends and retain only the information associated to independent blinking of the emitters, thus enhancing the SOFI signals. We find that our method works well to correct photodestruction, especially when it occurs quickly. The resulting images show a much higher contrast, strongly suppressed background and a more detailed visualization of cellular structures. Our method is parameter-free and computationally efficient, and can be readily applied on both two-dimensional and three-dimensional data.

Design of experiments for the optimization of SOFI super-resolution microscopy imaging.

Super-resolution optical fluctuation imaging (SOFI) is a well-known super-resolution technique appreciated for its versatility and broad applicability. However, even though an extended theoretical description is available, it is still not fully understood how the interplay between different experimental parameters influences the quality of a SOFI image. We investigated the relationship between five experimental parameters (measurement time, on-time t on, off-time t off, probe brightness, and out of focus background) and the quality of the super-resolved images they yielded, expressed as Signal to Noise Ratio (SNR). Empirical relationships were modeled for second- and third-order SOFI using data simulated according to a D-Optimal design of experiments, which is an ad-hoc design built to reduce the experimental load when the total number of trials to be conducted becomes too high for practical applications. This approach proves to be more reliable and efficient for parameter optimization compared to the more classical parameter by parameter approach. Our results indicate that the best image quality is achieved for the fastest emitter blinking (lowest t on and t off), lowest background level, and the highest measurement duration, while the brightness variation does not affect the quality in a statistically significant way within the investigated range. However, when the ranges spanned by the parameters are constrained, a different set of optimal conditions may arise. For example, for second-order SOFI, we identified situations in which the increase of t off can be beneficial to SNR, such as when the measurement duration is long enough. In general, optimal values of t on and t off have been found to be highly dependent from each other and from the measurement duration.

Model-free pixelation correction in SOFI imaging

Super-resolution optical fluctuation imaging (SOFI) delivers an enhanced spatial resolution in fluorescence imaging by analyzing spontaneous fluctuations in the fluorophore emission. SOFI images are usually obtained by calculating cross-cumulants between adjacent pixels on the detector, which provides the increased pixel densities required to sample the improved spatial resolution, but can result in pixelation artifacts. In this contribution, we describe a simple, model-free, and computationally efficient algorithm to correct such artifacts by matching the means and variances of the different cross-cumulants. We show that this strategy not only results in pixels that are essentially free of artifacts, but can also correct for detector imperfections such as the spurious correlations present in electron-multiplied CCD cameras.

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.

Cusp-artifacts in high order superresolution optical fluctuation imaging.

Superresolution optical fluctuation imaging (SOFI) is a simple and affordable super-resolution imaging technique, and attracted a growing community over the past decade. However, the theoretical resolution enhancement of high order SOFI is still not fulfilled. In this study, we identify "cusp artifacts" in high order SOFI images, and show that the high-order cumulants, odd-order moments and balanced-cumulants (bSOFI) are highly vulnerable to cusp artifacts. Our study provides guidelines for developing and screening for fluorescence probes, and improving data acquisition for SOFI. The new insight is important to inspire positive utilization of the cusp artifacts.

An extended quantitative model for super-resolution optical fluctuation imaging (SOFI).

Super-resolution optical fluctuation imaging (SOFI) provides super-resolution (SR) fluorescence imaging by analyzing fluctuations in the fluorophore emission. The technique has been used both to acquire quantitative SR images and to provide SR biosensing by monitoring changes in fluorophore blinking dynamics. Proper analysis of such data relies on a fully quantitative model of the imaging. However, previous SOFI imaging models made several assumptions that can not be realized in practice. In this work we address these limitations by developing and verifying a fully quantitative model that better approximates real-world imaging conditions. Our model shows that (i) SOFI images are free of bias, or can be made so, if the signal is stationary and fluorophores blink independently, (ii) allows a fully quantitative description of the link between SOFI imaging and probe dynamics, and (iii) paves the way for more advanced SOFI image reconstruction by offering a computationally fast way to calculate SOFI images for arbitrary probe, sample and instrumental properties.

Cusp Artifacts in High Order Superresolution Optical Fluctuation Imaging (SOFI)

Superresolution optical fluctuation imaging (SOFI) is a simple and affordable super-resolution imaging technique, and attracted a growing community over the past decade. However, the theoretical resolution enhancement of high order SOFI is still not fulfilled. In this study, we identify “cusp artifacts” in high order SOFI images, and show that the high-order cumulants, odd-order moments and balanced-cumulants (bSOFI) are highly vulnerable to cusp artifacts. Our study provides guidelines for developing and screening for fluorescence probes, and improving data acquisition for SOFI. The new insight is important to inspire positive utilization of the cusp artifacts.

Synergizing superresolution optical fluctuation imaging with single molecule localization microscopy

Single-molecule-localization-microscopy (SMLM) and superresolution-optical-fluctuation-imaging (SOFI) enable imaging biological samples well beyond the diffraction-limit of light. SOFI imaging is typically faster, yet has lower resolution than SMLM. Since the same (or similar) data format is acquired for both methods, their algorithms could presumably be combined synergistically for reconstruction and improvement of overall imaging performance. For that, we first defined a measure of the acquired-SNR for each method. This measure was ∼x10 to x100 higher for SOFI as compared to SMLM, indicating faster recognition and acquisition of features by SOFI. This measure also allowed fluorophore-specific optimization of SOFI reconstruction over its time-window and time-lag. We show that SOFI-assisted SMLM imaging can improve image reconstruction by rejecting common sources of background (e.g. out-of-focus emission and auto-fluorescence), especially under low signal-to-noise ratio conditions, by efficient optical sectioning and by shortening image reconstruction time. The performance and utility of our approach was evaluated by realistic simulations and by SOFI-assisted SMLM imaging of the plasma membrane of activated fixed and live T-cells (in isolation or in conjugation to antigen presenting cells). Our approach enhances SMLM performance under demanding imaging conditions and could set an example for synergizing additional imaging techniques.

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.

Statistical precision in super-resolution optical fluctuation imaging.

The super-resolution optical fluctuation imaging (SOFI) technique enhances image spatial resolution by calculating the spatiotemporal cross-cumulants of independent stochastic intensity fluctuations of emitters. Ideally, SOFI eliminates any noise that is not correlated over time, but in practice, due to limited data lengths, the statistical uncertainty of cumulants will affect the continuities and homogeneities of SOFI images. Since the variance and signal-to-noise ratio (SNR) characterize cumulant statistical uncertainty, we determined theoretical expressions for these based on a single dataset. From a simulation of temporal fluctuations of blinking fluorescent emitters, we calculated the quantitative relation between the SNR of cumulants and multiple parameters of the blinking signal, such as the on-time ratio, acquisition frame to average blinking rate ratio, sequence length, and photon amplitude, which not only provides a physical interpretation for SOFI phenomena but also theoretical guidance to achieve optimal practical outcomes.

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.

Local Dynamic Range Compression for High Order Super-Resolution Optical Fluctuation Imaging (SOFI)

The addition of Superresolution Optical Fluctuation Imaging (SOFI)[1][2] to the arsenal of superresolution methods offers a simple and affordable alternative to the more sophisticated techniques (such as STED, localization microscopies, and structured illumination). Calculations of higher order cumulants lead to a larger improvement in SOFI resolution, but also introduce a large nonlinear expansion in brightness dynamic range. Balanced SOFI (bSOFI)[3] provides a way to correct for the expanded dynamic range, but at the same time it introduces artifacts in the corrected image. In this work, we introduce a novel local dynamic range compression method, where the brightness dynamic range of a high (up to 6th-) order SOFI image is locally self-calibrated by the brightness of the corresponding second order SOFI image. The method is implemented for both auto- and cross- correlation SOFI with 36 fold extra pixels, and is combined with deconvolution. Local dynamic range compression SOFI (ldrc-SOFI) is demonstrated for simulated data and for QD625 labeled alpha-tubulin in fixed 3T3 cells with a 4-fold resolution enhancement. We show that ldrc-SOFI suffers from fewer artifacts compared to bSOFI, while exhibiting faithful dynamic range compression. A large library of simulated data of filaments networks with variable filaments density, labeling density, labeling uncertainty, noise level, background level and nonspecific binding probability was generated and examined by both bSOFI and ldrc-SOFI. When the signal to noise ratio (SNR) and other sample conditions are favorable, both algorithms perform well. In fact, under very low filaments density bSOFI performs better. However, under challenging imaging conditions (high feature density, non-specific background, high noise level), ldrc-SOFI yields better performance with less artifacts.[1] PNAS, 106(52):22287, 2009.[2] Optics express, 18(18):18875, 2010.[3] Optical Nanoscopy, 1(1):1, 2012.

Deconvolution optimization in super-resolution optical fluctuation imaging based on cumulant standard deviation

The super-resolution optical fluctuation imaging (SOFI) technique enhances image spatial resolution by evaluating the independent stochastic intensity fluctuations of emitters. In principle, it eliminates any noise uncorrelated temporally, and provides unlimited spatial resolution since the calculation of the nth-order cumulant followed by a deconvolution results in an image with n-fold resolution improvement in three dimensions. But in practice, due to limited data length, the statistical uncertainty of cumulants will affect the continuity and homogeneity of SOFI image, which results in the fact that the high order SOFI (typically over 3rd order) cannot improve spatial resolution significantly. Since the variance characterizes the statistical uncertainty of cumulant, we deduce its theoretical expression based on a single dataset. In traditional SOFI techniques, due to lack of statistical analysis of cumulant, there is no noise constraint condition of cumulant in the Lucy-Richardson deconvolution to prevent the algorithm from causing noise amplification. In this paper, based on the cumulant variance formula, we calculate the cumulant standard deviation in each pixel of SOFI image and introduce the results into the Lucy-Richardson algorithm as a DAMPAR to suppress the noise generation in such pixels. The simulation and experimental results show that under the same data length, the deconvolution optimization based on cumulant standard deviation significantly improves the uniformity and continuity of SOFI image. On the other hand, under the premise of identical image quality, this optimization technique can also greatly shorten the image frames to less than half the original, thus promoting the development of super-resolution imaging of living cells.

Super-resolution fluorescence imaging of structure property of biomimetic hydrogels

Biomimetic hydrogels have a wide variety of applications in the biomedical field. Functional biomimetic hydrogels which are based on polyisocyanopeptides have the same structure property as actin microfilaments and intermediate filaments of cytoskeleton. ALEXA647 dyes are labeled onto polymer chains of hydrogels, and total internal reflection fluorescence microscopy is used to image the hydrogels. Super-resolution fluorescence images for hydrogels are achieved by super-resolution optical fluctuation imaging (SOFI) and deconvolution. The SOFI imaging also features significant background reduction and thus contrast enhancement. Network structures of hydrogels and bundles of polymer chains are visualized by SOFI imaging. It is found that bundle dimensions are constant irrespective of polymer concentration and network structures have smaller pore sizes at higher concentration. The property of biomimetic hydrogels in aqueous solution is also observed.

Development of a reversibly switchable fluorescent protein for super-resolution optical fluctuation imaging (SOFI).

Reversibly switchable fluorescent proteins (RSFPs) can be effectively used for super-resolution optical fluctuation imaging (SOFI) based on the switching and fluctuation of single molecules. Several properties of RSFPs strongly influence the quality of SOFI images. These properties include (i) the averaged fluorescence intensity in the fluctuation state, (ii) the on/off contrast ratio, (iii) the photostability, and (iv) the oligomerization tendency. The first three properties determine the fluctuation range of the imaged pixels and the SOFI signal, which are of essential importance to the spatial resolution, and the last may lead to artificial aggregation of target proteins. The RSFPs that are currently used for SOFI are low in averaged fluorescence intensity in the fluctuation state, photostability, and on/off contrast ratio, thereby limiting the range of application of SOFI in biological super-resolution imaging. In this study, we developed a novel monomeric green RSFP termed Skylan-S, which features very high photostability, contrast ratio, and averaged fluorescence intensity in the fluctuation state. Taking advantage of the excellent optical properties of Skylan-S, a 4-fold improvement in the fluctuation range of the imaged pixels and higher SOFI resolution can be obtained compared with Dronpa. Furthermore, super-resolution imaging of the actin or tubulin structures and clathrin-coated pits (CCPs) in living U2OS cells labeled with Skylan-S was demonstrated using the SOFI technique. Overall, Skylan-S developed with outstanding photochemical properties is promising for long-time SOFI imaging with high spatial-temporal resolution.

Achieving increased resolution and more pixels with Superresolution Optical Fluctuation Imaging (SOFI)

Superresolution Optical Fluctuation Imaging (SOFI) as initially demonstrated allows for a resolution enhancement in imaging by a factor of square-root of two. Here, we demonstrate how to increase the resolution of SOFI images by re-weighting the Optical Transfer Function (OTF). Furthermore, we demonstrate how cross-cumulants can be exploited to obtain a fair approximation of the underlying Point-Spread Function. We show a two-fold increase of resolution (over the diffraction limit) of near-infrared quantum dot labeled tubulin-network of 3T3 fibroblasts