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

High-order super-resolution optical fluctuation imaging with ultrasmall polymer dots

Super-resolution optical fluctuation imaging (SOFI) rapidly generates super-resolution images by analyzing fluorescence intensity fluctuations. However, fluorophores for high-order SOFI applications are very rare. Here, we report ultrasmall semiconducting polymer dots (Pdots) to achieve high-order SOFI at single-particle and cellular levels. The ultrasmall Pdots exhibit an average diameter of ∼7 nm and superior photoblinking characteristics. The Pdot bioconjugates specifically labeled subcellular structures and demonstrated super-resolution imaging with a spatial resolution of ∼64 nm, which is ∼6 times enhancement compared to wide-field images. This study demonstrates the potential of the small-sized semiconductor Pdots for high-order SOFI in biomedical applications.

Simultaneous multicolor fluorescence imaging using PSF splitting

We present a way to encode more information in fluorescence imaging by splitting the original point spread function (PSF), which offers broadband operation and compatibility with other PSF engineering modalities and existing analysis tools. We demonstrate the approach using the 'Circulator', an add-on that encodes the fluorophore emission band into the PSF, enabling simultaneous multicolor super-resolution and single-molecule microscopy using essentially the full field of view.

Cross-Correlation Increases Sampling in Diffusion-Based Super-Resolution Optical Fluctuation Imaging

Correlation signal processing of optical three-dimensional (x, y, t) data can produce super-resolution images. The second-order cross-correlation function XC 2 has been documented to produce super-resolution imaging with static and blinking emitters but not for diffusing emitters. Here, we both analytically and numerically demonstrate cross-correlation analysis for diffusing particles. We then expand our fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) analysis to use cross-correlation as a postprocessing computational technique to extract both dynamic and structural information on particle diffusion in nanoscale structures simultaneously. Cross-correlation maintains the same super-resolution as auto-correlation while also increasing the sampling rates to reduce aliasing for spatial information in both simulated and experimental data. Our work demonstrates how fcsSOFI with cross-correlation can be a powerful signal-processing tool to resolve the nanoscale dynamics and structure in samples relevant to biological and soft materials.

Cross-correlation increases sampling in diffusion-based super-resolution optical fluctuation imaging

Correlation signal processing of optical three-dimensional (x, y, t) data can produce super-resolution images. The second order cross-correlation function XC 2 has been documented to produce super-resolution imaging with static and blinking emitters but not for diffusing emitters. Here, we both analytically and numerically demonstrate cross-correlation analysis for diffusing particles. We then expand our fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) analysis to use cross-correlation as a post-processing computational technique to extract both dynamic and structural information of particle diffusion in nanoscale structures simultaneously. We further show how this method increases sampling rates and reduces aliasing for spatial information in both simulated and experimental data. Our work demonstrates how fcsSOFI with cross-correlation can be a powerful signal-processing tool to resolve the nanoscale dynamics and structure in samples relevant to biological and soft materials.

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Fluorescence fluctuation-based super-resolution microscopy: Basic concepts for an easy start

Due to the wave nature of light, optical microscopy has a lower-bound lateral resolution limit of approximately half of the wavelength of visible light, that is, within the range of 200 to 350 nm. Fluorescence fluctuation-based super-resolution microscopy (FF-SRM) is a term used to encompass a collection of image analysis techniques that rely on the statistical processing of temporal variations of the fluorescence signal. FF-SRM aims to reduce the uncertainty of the location of fluorophores within an image, often improving spatial resolution by several tens of nanometers. FF-SRM is suitable for live-cell imaging due to its compatibility with most fluorescent probes and relatively simple instrumental and experimental requirements, which are mostly camera-based epifluorescence instruments. Each FF-SRM approach has strengths and weaknesses, which depend directly on the underlying statistical principles through which enhanced spatial resolution is achieved. In this review, the basic concepts and principles behind a range of FF-SRM methods published to date are described. Their operational parameters are explained and guidance for their selection is provided.

Characterizing Dark State Kinetics and Single Molecule Fluorescence of FusionRed and FusionRed-MQ at Low Irradiances

The presence of dark states causes fluorescence intermittency of single molecules due to transitions between “on” and “off” states. Genetically encodable markers such as fluorescent proteins (FPs) exhibit dark states...

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.

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.

Orthogonal fluorescent chemogenetic reporters for multicolor imaging

Spectrally separated fluorophores allow the observation of multiple targets simultaneously inside living cells, leading to a deeper understanding of the molecular interplay that regulates cell function and fate. Chemogenetic systems combining a tag and a synthetic fluorophore provide certain advantages over fluorescent proteins since there is no requirement for chromophore maturation. Here, we present the engineering of a set of spectrally orthogonal fluorogen activating tags based on the Fluorescence Activating and absorption Shifting Tag (FAST), that are compatible with two-color, live cell imaging. The resulting tags, greenFAST and redFAST, demonstrate orthogonality not only in their fluorogen recognition capabilities, but also in their one- and two-photon absorption profiles. This pair of orthogonal tags allowed the creation of a two-color cell cycle sensor capable of detecting very short, early cell cycles in zebrafish development, and the development of split complementation systems capable of detecting multiple protein-protein interactions by live cell fluorescence microscopy.

Self-Blinking Dyes Unlock High-Order and Multiplane Super-Resolution Optical Fluctuation Imaging

Most diffraction-unlimited super-resolution imaging critically depends on the switching of fluorophores between at least two states, often induced using intense laser light and specialized buffers or UV radiation. Recently, so-called self-blinking dyes that switch spontaneously between an open, fluorescent "on" state and a closed, colorless "off" state were introduced. Here, we exploit the synergy between super-resolution optical fluctuation imaging (SOFI) and spontaneously switching fluorophores for 2D and 3D imaging. SOFI analyzes higher order statistics of fluctuations in the fluorophore emission instead of localizing individual molecules. It thereby tolerates a broad range of labeling densities, switching behavior, and probe brightness. Thus, even dyes that exhibit spontaneous blinking characteristics that are not suitable or suboptimal for single molecule localization microscopy can be used successfully for SOFI-based super-resolution imaging. We demonstrate 2D imaging of fixed cells with almost uniform resolution up to 50-60 nm in 6th order SOFI and characterize changing experimental conditions. Next, we investigate volumetric imaging using biplane and eight-plane data acquisition. We extend 3D cross-cumulant analysis to 4th order, achieving super-resolution in 3D with up to 29 depth planes. Finally, the low laser excitation intensities needed for single and biplane self-blinking SOFI are well suited for live-cell imaging. We show the perspective for time-resolved imaging by observing slow membrane movements in cells. Self-blinking SOFI thus provides a more robust alternative route for easy-to-use 2D and 3D high-resolution imaging.

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.