Optimal 2D-SIM reconstruction by two filtering steps with Richardson-Lucy deconvolution

A large open access dataset of transillumination imaging the toward realization of optical computed tomography

Transillumination imaging is commonly used in medicine and biometrics to provide non-invasive insights into internal structures. However, the prevalent image blurring resulting from scattering effects poses a significant challenge to the effective application of transillumination. Artificial intelligence algorithms have gained prominence for enhancing transillumination images and addressing challenges such as scattering suppression, depth estimation, and three-dimensional structure reconstruction. These advances require high-quality input images to optimize model performance. Acquiring a large-scale transillumination image dataset for practical AI applications is difficult due to subjective imaging conditions. This study aimed to overcome this obstacle by introducing a comprehensive dataset of transillumination images. Methods and algorithms for generating depth-dependent point-spread function and transillumination images were presented. The dataset comprised 12,000 pairs of images of clear and scattered media, each associated with the corresponding depth information. This study is valuable for advancing AI-based solutions in transillumination imaging and provides a foundation for further research on image deblurring, depth perception, and 3D reconstruction.

Stochastically structured illumination microscopy scan less super resolution imaging

In super-resolution, a varying illumination image stack is required. This enriched dataset typically necessitates precise mechanical control and micron-scale optical alignment and repeatability. Here, we introduce a novel methodology for super-resolution microscopy called stochastically structured illumination microscopy (S2IM), which bypasses the need for illumination control exploiting instead the random, uncontrolled movement of the target object. We tested our methodology within the clinically relevant ophthalmoscopic setting, harnessing the inherent saccadic motion of the eye to induce stochastic displacement of the illumination pattern on the retina. We opted to avoid human subjects by utilizing a phantom eye model featuring a retina composed of human induced pluripotent stem cells (iPSC) retinal neurons and replicating the ocular saccadic movements by custom actuators. Our findings demonstrate that S2IM unlocks scan-less super-resolution with a resolution enhancement of 1.91, with promising prospects also beyond ophthalmoscopy applications such as active matter or atmospheric/astronomical observation.

Surpassing light inhomogeneities in structured-illumination microscopy with FlexSIM

Super-resolution structured-illumination microscopy (SIM) is a powerful technique that allows one to surpass the diffraction limit by up to a factor two. Yet, its practical use is hampered by its sensitivity to imaging conditions which makes it prone to reconstruction artefacts. In this work, we present FlexSIM, a flexible SIM reconstruction method capable to handle highly challenging data. Specifically, we demonstrate the ability of FlexSIM to deal with the distortion of patterns, the high level of noise encountered in live imaging, as well as out-of-focus fluorescence. Moreover, we show that FlexSIM achieves state-of-the-art performance over a variety of open SIM datasets.

MTFP1 controls mitochondrial fusion to regulate inner membrane quality control and maintain mtDNA levels

Mitochondrial dynamics play a critical role in cell fate decisions and in controlling mtDNA levels and distribution. However, the molecular mechanisms linking mitochondrial membrane remodeling and quality control to mtDNA copy number (CN) regulation remain elusive. Here, we demonstrate that the inner mitochondrial membrane (IMM) protein mitochondrial fission process 1 (MTFP1) negatively regulates IMM fusion. Moreover, manipulation of mitochondrial fusion through the regulation of MTFP1 levels results in mtDNA CN modulation. Mechanistically, we found that MTFP1 inhibits mitochondrial fusion to isolate and exclude damaged IMM subdomains from the rest of the network. Subsequently, peripheral fission ensures their segregation into small MTFP1-enriched mitochondria (SMEM) that are targeted for degradation in an autophagic-dependent manner. Remarkably, MTFP1-dependent IMM quality control is essential for basal nucleoid recycling and therefore to maintain adequate mtDNA levels within the cell.

Superresolution structured illumination microscopy reconstruction algorithms: a review

Structured illumination microscopy (SIM) has become the standard for next-generation wide-field microscopy, offering ultrahigh imaging speed, superresolution, a large field-of-view, and long-term imaging. Over the past decade, SIM hardware and software have flourished, leading to successful applications in various biological questions. However, unlocking the full potential of SIM system hardware requires the development of advanced reconstruction algorithms. Here, we introduce the basic theory of two SIM algorithms, namely, optical sectioning SIM (OS-SIM) and superresolution SIM (SR-SIM), and summarize their implementation modalities. We then provide a brief overview of existing OS-SIM processing algorithms and review the development of SR-SIM reconstruction algorithms, focusing primarily on 2D-SIM, 3D-SIM, and blind-SIM. To showcase the state-of-the-art development of SIM systems and assist users in selecting a commercial SIM system for a specific application, we compare the features of representative off-the-shelf SIM systems. Finally, we provide perspectives on the potential future developments of SIM.

Rapid, artifact-reduced, image reconstruction for super-resolution structured illumination microscopy

Super-resolution structured illumination microscopy (SR-SIM) is finding increasing application in biomedical research due to its superior ability to visualize subcellular dynamics in living cells. However, during image reconstruction artifacts can be introduced and when coupled with time-consuming postprocessing procedures, limits this technique from becoming a routine imaging tool for biologists. To address these issues, an accelerated, artifact-reduced reconstruction algorithm termed joint space frequency reconstruction-based artifact reduction algorithm (JSFR-AR-SIM) was developed by integrating a high-speed reconstruction framework with a high-fidelity optimization approach designed to suppress the sidelobe artifact. Consequently, JSFR-AR-SIM produces high-quality, super-resolution images with minimal artifacts, and the reconstruction speed is increased. We anticipate this algorithm to facilitate SR-SIM becoming a routine tool in biomedical laboratories.

Electron-beam patterned calibration structures for structured illumination microscopy

Super-resolution fluorescence microscopy can be achieved by image reconstruction after spatially patterned illumination or sequential photo-switching and read-out. Reconstruction algorithms and microscope performance are typically tested using simulated image data, due to a lack of strategies to pattern complex fluorescent patterns with nanoscale dimension control. Here, we report direct electron-beam patterning of fluorescence nanopatterns as calibration standards for super-resolution fluorescence. Patterned regions are identified with both electron microscopy and fluorescence labelling of choice, allowing precise correlation of predefined pattern dimensions, a posteriori obtained electron images, and reconstructed super-resolution images.

Resolution doubling in light-sheet microscopy via oblique plane structured illumination

Structured illumination microscopy (SIM) doubles the spatial resolution of a fluorescence microscope without requiring high laser powers or specialized fluorophores. However, the excitation of out-of-focus fluorescence can accelerate photobleaching and phototoxicity. In contrast, light-sheet fluorescence microscopy (LSFM) largely avoids exciting out-of-focus fluorescence, thereby enabling volumetric imaging with low photobleaching and intrinsic optical sectioning. Combining SIM with LSFM would enable gentle three-dimensional (3D) imaging at doubled resolution. However, multiple orientations of the illumination pattern, which are needed for isotropic resolution doubling in SIM, are challenging to implement in a light-sheet format. Here we show that multidirectional structured illumination can be implemented in oblique plane microscopy, an LSFM technique that uses a single objective for excitation and detection, in a straightforward manner. We demonstrate isotropic lateral resolution below 150 nm, combined with lower phototoxicity compared to traditional SIM systems and volumetric acquisition speed exceeding 1 Hz.

Real-time reconstruction using electro-optics modulator-based structured illumination microscopy

Structured illumination microscopy (SIM), a super-resolution technology, has a wide range of applications in life sciences. In this study, we present an electro-optic high-speed phase-shift super-resolution microscopy imaging system including 2D SIM, total internal reflection fluorescence-SIM, and 3D SIM modes. This system uses galvanometers and an electro-optic modulator to flexibly and quickly control the phase and direction of structured illumination patterns. Moreover, its design consists of precise timing for improved acquisition speed and software architecture for real-time reconstruction. The highest acquisition rate achieved was 151 frames/s, while the highest real-time super-resolution reconstruction frame rate achieved was over 25 frames/s.

Fluorescence Microscopy-An Outline of Hardware, Biological Handling, and Fluorophore Considerations

Fluorescence microscopy has become a critical tool for researchers to understand biological processes at the cellular level. Micrographs from fixed and live-cell imaging procedures feature in a plethora of scientific articles for the field of cell biology, but the complexities of fluorescence microscopy as an imaging tool can sometimes be overlooked or misunderstood. This review seeks to cover the three fundamental considerations when designing fluorescence microscopy experiments: (1) hardware availability; (2) amenability of biological models to fluorescence microscopy; and (3) suitability of imaging agents for intended applications. This review will help equip the reader to make judicious decisions when designing fluorescence microscopy experiments that deliver high-resolution and informative images for cell biology.

Frequency-spatial domain joint optimization for improving super-resolution images of nonlinear structured illumination microscopy

Introducing nonlinear fluorophore excitation into structured illumination microscopy (SIM) can further extend its spatial resolution without theoretical limitation. However, it is a great challenge to recover the weak higher-order harmonic signal and reconstruct high-fidelity super-resolution (SR) images. Here, we proposed a joint optimization strategy in both the frequency and spatial domains to reconstruct high-quality nonlinear SIM (NL-SIM) images. We demonstrate that our method can reconstruct SR images with fewer artifacts and higher fidelity on the BioSR dataset with patterned-activation NL-SIM. This method could robustly overcome one of the long-lived obstacles on NL-SIM imaging, thereby promoting its wide application in biology.

Volumetric interferometric lattice light-sheet imaging

Live-cell imaging with high spatiotemporal resolution and high detection sensitivity facilitates the study of the dynamics of cellular structure and function. However, extracting high-resolution 4D (3D space plus time) information from live cells remains challenging, because current methods are slow, require high peak excitation intensities or suffer from high out-of-focus background. Here we present 3D interferometric lattice light-sheet (3D-iLLS) imaging, a technique that requires low excitation light levels and provides high background suppression and substantially improved volumetric resolution by combining 4Pi interferometry with selective plane illumination. We demonstrate that 3D-iLLS has an axial resolution and single-particle localization precision of 100 nm (FWHM) and <10 nm (1σ), respectively. We illustrate the performance of 3D-iLLS in a range of systems: single messenger RNA molecules, nanoscale assemblies of transcription regulators in the nucleus, the microtubule cytoskeleton, and mitochondria organelles. The enhanced 4D resolution and increased signal-to-noise ratio (SNR) of 3D-iLLS will facilitate the analysis of biological processes at the sub-cellular level.

Structured illumination microscopy with noise-controlled image reconstructions

Super-resolution structured illumination microscopy (SIM) has become a widely used method for biological imaging. Standard reconstruction algorithms, however, are prone to generate noise-specific artifacts that limit their applicability for lower signal-to-noise data. Here we present a physically realistic noise model that explains the structured noise artifact, which we then use to motivate new complementary reconstruction approaches. True-Wiener-filtered SIM optimizes contrast given the available signal-to-noise ratio, and flat-noise SIM fully overcomes the structured noise artifact while maintaining resolving power. Both methods eliminate ad hoc user-adjustable reconstruction parameters in favor of physical parameters, enhancing objectivity. The new reconstructions point to a trade-off between contrast and a natural noise appearance. This trade-off can be partly overcome by further notch filtering but at the expense of a decrease in signal-to-noise ratio. The benefits of the proposed approaches are demonstrated on focal adhesion and tubulin samples in two and three dimensions, and on nanofabricated fluorescent test patterns.

Structured illumination microscopy artefacts caused by illumination scattering

Despite its wide application in live-cell super-resolution (SR) imaging, structured illumination microscopy (SIM) suffers from aberrations caused by various sources. Although artefacts generated from inaccurate reconstruction parameter estimation and noise amplification can be minimized, aberrations due to the scattering of excitation light on samples have rarely been investigated. In this paper, by simulating multiple subcellular structure with the distinct refractive index from water, we study how different thicknesses of this subcellular structure scatter incident light on its optical path of SIM excitation. Because aberrant interference light aggravates with the increase in sample thickness, the reconstruction of the 2D-SIM SR image degraded with the change of focus along the axial axis. Therefore, this work may guide the future development of algorithms to suppress SIM artefacts caused by scattering in thick samples. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

GPU-accelerated real-time reconstruction in Python of three-dimensional datasets from structured illumination microscopy with hexagonal patterns

We present a structured illumination microscopy system that projects a hexagonal pattern by the interference among three coherent beams, suitable for implementation in a light-sheet geometry. Seven images acquired as the illumination pattern is shifted laterally can be processed to produce a super-resolved image that surpasses the diffraction-limited resolution by a factor of over 2 in an exemplar light-sheet arrangement. Three methods of processing data are discussed depending on whether the raw images are available in groups of seven, individually in a stream or as a larger batch representing a three-dimensional stack. We show that imaging axially moving samples can introduce artefacts, visible as fine structures in the processed images. However, these artefacts are easily removed by a filtering operation carried out as part of the batch processing algorithm for three-dimensional stacks. The reconstruction algorithms implemented in Python include specific optimizations for calculation on a graphics processing unit and we demonstrate its operation on experimental data of static objects and on simulated data of moving objects. We show that the software can process over 239 input raw frames per second at 512 × 512 pixels, generating over 34 super-resolved frames per second at 1024 × 1024 pixels. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Super-resolution fluorescence microscopy by line-scanning with an unmodified two-photon microscope

Fluorescence-based microscopy as one of the standard tools in biomedical research benefits more and more from super-resolution methods, which offer enhanced spatial resolution allowing insights into new biological processes. A typical drawback of using these methods is the need for new, complex optical set-ups. This becomes even more significant when using two-photon fluorescence excitation, which offers deep tissue imaging and excellent z-sectioning. We show that the generation of striped-illumination patterns in two-photon laser scanning microscopy can readily be exploited for achieving optical super-resolution and contrast enhancement using open-source image reconstruction software. The special appeal of this approach is that even in the case of a commercial two-photon laser scanning microscope no optomechanical modifications are required to achieve this modality. Modifying the scanning software with a custom-written macro to address the scanning mirrors in combination with rapid intensity switching by an electro-optic modulator is sufficient to accomplish the acquisition of two-photon striped-illumination patterns on an sCMOS camera. We demonstrate and analyse the resulting resolution improvement by applying different recently published image resolution evaluation procedures to the reconstructed filtered widefield and super-resolved images. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

High-fidelity structured illumination microscopy by point-spread-function engineering

Structured illumination microscopy (SIM) has become a widely used tool for insight into biomedical challenges due to its rapid, long-term, and super-resolution (SR) imaging. However, artifacts that often appear in SIM images have long brought into question its fidelity, and might cause misinterpretation of biological structures. We present HiFi-SIM, a high-fidelity SIM reconstruction algorithm, by engineering the effective point spread function (PSF) into an ideal form. HiFi-SIM can effectively reduce commonly seen artifacts without loss of fine structures and improve the axial sectioning for samples with strong background. In particular, HiFi-SIM is not sensitive to the commonly used PSF and reconstruction parameters; hence, it lowers the requirements for dedicated PSF calibration and complicated parameter adjustment, thus promoting SIM as a daily imaging tool.

Method for assessing the spatiotemporal resolution of structured illumination microscopy (SIM)

A method is proposed for assessing the temporal resolution of structured illumination microscopy (SIM), by tracking the amplitude of different spatial frequency components over time, and comparing them to a temporally-oscillating ground-truth. This method is used to gain insight into the performance limits of SIM, along with alternative reconstruction techniques (termed 'rolling SIM') that claim to improve temporal resolution. Results show that the temporal resolution of SIM varies considerably between low and high spatial frequencies, and that, despite being used in several high profile papers and commercial microscope software, rolling SIM provides no increase in temporal resolution over conventional SIM.

Virtual single-pixel imaging-based deconvolution method for spatial resolution improvement in wide-field fluorescence microscopy

Deconvolution technique has been widely used in fluorescence microscopy to restore fine structures of biological samples. However, conventional deconvolution methods usually achieve little contrast enhancement in dense structures that have the intervals close to the Rayleigh criterion. Herein, we developed a novel deconvolution method, termed virtual single-pixel imaging (v-SPI). Differing from existing deconvolution methods, v-SPI aims to retrieve the less blurred image directly, not the sample distribution which cannot be actually obtained. And the result can be retrieved simply by solving a linear matrix in spatial domain. In addition, the proposed method has no requirement of calibrating parameters of microscope system. Simulation and experimental results demonstrated that the proposed v-SPI method can enhance the contrast of dense structures significantly and acquire a 24% increase in resolution.

Second-order optimized regularized structured illumination microscopy (sorSIM) for high-quality and rapid super resolution image reconstruction with low signal level

Structured illumination microscopy (SIM) is a widely used super resolution imaging technique that can down-modulate a sample's high-frequency information into objective recordable frequencies to enhance the resolution below the diffraction limit. However, classical SIM image reconstruction methods often generate poor results under low illumination conditions, which are required for reducing photobleaching and phototoxicity in cell imaging experiments. Although denoising methods or auxiliary items improved SIM image reconstruction in low signal level situations, they still suffer from decreased reconstruction quality and significant background artifacts, inevitably limiting their practical applications. In order to improve the reconstruction quality, second-order optimized regularized SIM (sorSIM) is designed specifically for image reconstruction in low signal level situations. In sorSIM, a second-order regularization term is introduced to suppress noise effect, and the penalty factor in this term is selected to optimize the resolution enhancement and noise resistance. Compared to classical SIM image reconstruction algorithms as well as to those previously used in low illumination cases, the proposed sorSIM provides images with enhanced resolution and fewer background artifacts. Therefore, sorSIM can be a potential tool for high-quality and rapid super resolution imaging, especially for low signal images.

Deep learning enables structured illumination microscopy with low light levels and enhanced speed

Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction in the number of raw images required for super-resolution SIM, and generate images under extreme low light conditions (at least 100× fewer photons). We validate the performance of deep neural networks on different cellular structures and achieve multi-color, live-cell super-resolution imaging with greatly reduced photobleaching.

Super-resolution microscopy typically requires high laser powers which can induce photobleaching and degrade image quality. Here the authors augment structured illumination microscopy (SIM) with deep learning to reduce the number of raw images required and boost its performance under low light conditions.

Deep learning enables structured illumination microscopy with low light levels and enhanced speed

Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction in the number of raw images required for super-resolution SIM, and generate images under extreme low light conditions (at least 100× fewer photons). We validate the performance of deep neural networks on different cellular structures and achieve multi-color, live-cell super-resolution imaging with greatly reduced photobleaching.

Video-rate multi-color structured illumination microscopy with simultaneous real-time reconstruction

Super-resolved structured illumination microscopy (SR-SIM) is among the fastest fluorescence microscopy techniques capable of surpassing the optical diffraction limit. Current custom-build instruments are able to deliver two-fold resolution enhancement with high acquisition speed. SR-SIM is usually a two-step process, with raw-data acquisition and subsequent, time-consuming post-processing for image reconstruction. In contrast, wide-field and (multi-spot) confocal techniques produce high-resolution images instantly. Such immediacy is also possible with SR-SIM, by tight integration of a video-rate capable SIM with fast reconstruction software. Here we present instant SR-SIM by VIGOR (Video-rate Immediate GPU-accelerated Open-Source Reconstruction). We demonstrate multi-color SR-SIM at video frame-rates, with less than 250 ms delay between measurement and reconstructed image display. This is achieved by modifying and extending high-speed SR-SIM image acquisition with a new, GPU-enhanced, network-enabled image-reconstruction software. We demonstrate high-speed surveying of biological samples in multiple colors and live imaging of moving mitochondria as an example of intracellular dynamics.

Sequential acquisition and image reconstruction in super-resolved structured illumination microscopy (SR-SIM) is time-consuming. Here the authors optimise both acquisition and reconstruction software to achieve multicolour SR-SIM at video frame-rates with reconstructed images displaying with only milliseconds delay during the experiment.

Implementation of a speckle-correlation-based optical lever with extended dynamic range

A speckle-correlation-based optical lever (SC-OptLev) is constructed for the measurement of small changes in the orientation angle of a surface. The dynamic range of SC-OptLev is found to be twice that of a conventional OptLev for the same experimental configurations. Different filtering mechanisms are implemented, and the correlation results are compared. Two types of computer-automated SC-OptLevs, an open-source-based computing system with a low-cost image sensor and a commercial computing system, are presented with assistive computational modules.

Improved vector-extrapolation-based Richardson-Lucy algorithm used for wavefront coded imaging

The Richardson-Lucy (RL) algorithm is a well-known nonlinear restoration method and has been widely applied in the fields of astronomical image restoration, microscopic image restoration, and so on because of its capability of generating high-quality restoration results and potential in realizing super-resolution. However, when being applied to restore the wavefront coded blurry images, the classical RL algorithm converges very slowly and has to be iterated many times before obtaining a satisfactory result, which severely prohibits its real-time application. Vector-extrapolation-based RL algorithm was invented to solve this problem, but the noise amplification increases fast, and additional post-processing is needed to further improve the signal-to-noise ratio. Therefore, in this paper, an improved RL algorithm is proposed by introducing an exponential modified correction term into the framework of the original vector-extrapolation-based RL algorithm. It not only results in a bigger iteration step, which ensures a faster convergence can be obtained, but also the noise amplification is effectively prohibited. Besides that, we design a structure-similarity-index-metric-based stopping criterion, based on which the optimum number of iterations for each color channel is obtained. Experimental results reveal that the total iterations decreases approximately 78.9%, and the restored images demonstrate a superior visual quality without denoising additionally.

Imaging tissues and cells beyond the diffraction limit with structured illumination microscopy and Bayesian image reconstruction

Background

Structured illumination microscopy (SIM) is a family of methods in optical fluorescence microscopy that can achieve both optical sectioning and super-resolution effects. SIM is a valuable method for high-resolution imaging of fixed cells or tissues labeled with conventional fluorophores, as well as for imaging the dynamics of live cells expressing fluorescent protein constructs. In SIM, one acquires a set of images with shifting illumination patterns. This set of images is subsequently treated with image analysis algorithms to produce an image with reduced out-of-focus light (optical sectioning) and/or with improved resolution (super-resolution).

Findings

Five complete, freely available SIM datasets are presented including raw and analyzed data. We report methods for image acquisition and analysis using open-source software along with examples of the resulting images when processed with different methods. We processed the data using established optical sectioning SIM and super-resolution SIM methods and with newer Bayesian restoration approaches that we are developing.

Conclusions

Various methods for SIM data acquisition and processing are actively being developed, but complete raw data from SIM experiments are not typically published. Publically available, high-quality raw data with examples of processed results will aid researchers when developing new methods in SIM. Biologists will also find interest in the high-resolution images of animal tissues and cells we acquired. All of the data were processed with SIMToolbox, an open-source and freely available software solution for SIM.

Inverse matrix based phase estimation algorithm for structured illumination microscopy

The fast imaging speed and low-intensity requirement of structured illumination microscopy (SIM) have made it one of the most widely used imaging tools in live cell imaging. In order to obtain a high fidelity reconstructed image, a precise estimation of the phase of the illumination pattern is required, especially in those structured illumination based techniques that rely on high-order harmonics to improve the resolution. This can be achieved in one of two fundamental ways. The first is to build a high-end control system capable of shifting a sinusoidal pattern with high precision, while the second is to apply estimation algorithms to determine how patterns shift during post-processing. The latter method is preferred in low-cost super-resolution imaging systems; however, existing algorithms are either time-consuming or fail due to noise and a low modulation depth. In this paper, we introduce additional matrixes into the phase estimation algorithm and propose an inverse matrix based phase estimation method with which analytical solutions of the phases can be determined without iteration. The proposed algorithm was validated via simulation and experiments using a home-made total internal reflection fluorescent SIM system (TIRF-SIM). When tested, the method obtained the true phase even when the modulation depth was low. The source code is now available for download by researchers and others.

Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy

To increase the temporal resolution and maximal imaging time of super-resolution (SR) microscopy, we have developed a deconvolution algorithm for structured illumination microscopy based on Hessian matrixes (Hessian-SIM). It uses the continuity of biological structures in multiple dimensions as a priori knowledge to guide image reconstruction and attains artifact-minimized SR images with less than 10% of the photon dose used by conventional SIM while substantially outperforming current algorithms at low signal intensities. Hessian-SIM enables rapid imaging of moving vesicles or loops in the endoplasmic reticulum without motion artifacts and with a spatiotemporal resolution of 88 nm and 188 Hz. Its high sensitivity allows the use of sub-millisecond excitation pulses followed by dark recovery times to reduce photobleaching of fluorescent proteins, enabling hour-long time-lapse SR imaging of actin filaments in live cells. Finally, we observed the structural dynamics of mitochondrial cristae and structures that, to our knowledge, have not been observed previously, such as enlarged fusion pores during vesicle exocytosis.

csiLSFM combines light-sheet fluorescence microscopy and coherent structured illumination for a lateral resolution below 100 nm

Light-sheet-based fluorescence microscopy (LSFM) features optical sectioning in the excitation process. It minimizes fluorophore bleaching as well as phototoxic effects and provides a true axial resolution. The detection path resembles properties of conventional fluorescence microscopy. Structured illumination microscopy (SIM) is attractive for superresolution because of its moderate excitation intensity, high acquisition speed, and compatibility with all fluorophores. We introduce SIM to LSFM because the combination pushes the lateral resolution to the physical limit of linear SIM. The instrument requires three objective lenses and relies on methods to control two counterpropagating coherent light sheets that generate excitation patterns in the focal plane of the detection lens. SIM patterns with the finest line spacing in the far field become available along multiple orientations. Flexible control of rotation, frequency, and phase shift of the perfectly modulated light sheet are demonstrated. Images of beads prove a near-isotropic lateral resolution of sub-100 nm. Images of yeast endoplasmic reticulum show that coherent structured illumination (csi) LSFM performs with physiologically relevant specimens.