Parameter-free image resolution estimation based on decorrelation analysis

Co-linear Hexa-Mirror-Based Multi-Periodic Structured Illumination Microscopy

Structured illumination microscopy (SIM) is a robust wide-field optical nanoscopy technique. Several approaches are implemented to improve SIM's resolution capability (∼2-fold). However, achieving a high resolution with a large field of view (FOV) is still challenging. We present tilt-mirror-based multi-periodic SIM for large-FOV super-resolution microscopy. The sample is illuminated by a multi-periodic structured pattern generated by six-beam interference using a custom-designed mirror mount. We achieve 3.16-fold resolution improvement while using a 20×/0.40 numerical-aperture objective that supports a large FOV (0.53 mm × 0.34 mm). This overcomes the high-space-bandwidth product challenge, achieving 9.98-fold improvement. mMP-SIM decouples illumination and collection paths, enabling scalable super-resolution over a large FOV. By using a 28×/0.80 numerical-aperture objective lens, an optical resolution of 170 nm over a 0.40 mm × 0.25 mm imaging area is demonstrated. The proof-of-principle experimental demonstration is performed for both fluorescent beads and a biosample like U2OS (human bone osteosarcoma) cells.

Fast reconstruction and optical-sectioning three-dimensional structured illumination microscopy

Three-dimensional structured illumination microscopy (3DSIM) is a popular method for observing subcellular/cellular structures or animal/plant tissues with gentle phototoxicity and 3D super-resolution. However, its time-consuming reconstruction process poses challenges for high-throughput imaging and real-time observation. Moreover, traditional 3DSIM typically requires more than six z layers for successful reconstruction and is susceptible to defocused backgrounds. This poses a great gap between single-layer 2DSIM and 6-layer 3DSIM, and limits the observation of thicker samples. To address these limitations, we developed FO-3DSIM, a novel method that integrates spatial-domain reconstruction with optical-sectioning SIM. FO-3DSIM enhances reconstruction speed by up to 855.7 times with superior performance with limited z layers and under high defocused backgrounds. It retains the high-fidelity, low-photon reconstruction capabilities of our previously proposed Open-3DSIM. Utilizing fast reconstruction and optical sectioning, we achieved large field-of-view (FOV) 3D super-resolution imaging of mouse kidney actin, covering a region of 0.453 mm × 0.453 mm × 2.75 μm within 23 min of acquisition and 13 min of reconstruction. Near real-time performance was demonstrated in live actin imaging with FO-3DSIM. Our approach reduces photodamage through limited z layer reconstruction, allowing the observation of ER tubes with just three layers. We anticipate that FO-3DSIM will pave the way for near real-time, large FOV 6D imaging, encompassing xyz super-resolution, multi-color, long-term, and polarization imaging with less photodamage, removed defocused backgrounds, and reduced reconstruction time.

Efficiently accelerated bioimage analysis with NanoPyx, a Liquid Engine-powered Python framework

The expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to reactive microscopy and computing fields requiring efficiency.

In Operando Locally-Resolved Photophysics in Perovskite Solar Cells by Correlation Clustering Imaging

The instability of metal halide perovskites limits the commercialization of solar cells despite their impressive efficiencies. This instability, driven by photo-induced ion migration, leads to material restructuring, defect formation, degradation, and defect healing. However, these same "unwanted" properties enable to propose Correlation Clustering Imaging (CLIM), a technique that detects local photoluminescence (PL) fluctuations through wide-field fluorescence microscopy. It is shown that such fluctuations are present in high-quality perovskites and their corresponding solar cells. CLIM successfully visualizes the polycrystalline grain structure in perovskite films, closely matching electron microscopy images. The analysis of fluctuations reveals a dominant metastable defect responsible for the fluctuations. In solar cells in short-circuit conditions, these fluctuations are significantly larger, and corresponding correlated regions extend up to 10 micrometers, compared to 2 micrometers in films. It is proposed that the regions resolved by CLIM in solar cells possess a common pool of charge extraction channels, which fluctuate and cause PL to vary. Since PL fluctuations reflect non-radiative recombination processes, CLIM provides valuable insights into the structural and functional dynamics of carrier transport, ion migration, defect behavior, and recombination losses. CLIM offers a non-invasive approach to understanding luminescent materials and devices in operando, utilizing contrasts based on previously untapped properties.

Approaching maximum resolution in structured illumination microscopy via accurate noise modeling

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification

Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network. DPA-TISR adaptively enhances the cross-frame alignment in the phase domain and outperforms existing state-of-the-art SISR and TISR models. We also develop Bayesian DPA-TISR and design an expected calibration error minimization framework that reliably infers inference confidence. We demonstrate multicolor live-cell SR imaging for more than 10,000 time points of various biological specimens with high fidelity, temporal consistency and accurate confidence quantification.

Super-Resolution Goes Viral: T4 Virus Particles as Versatile 3D-Bio-NanoRulers

In the burgeoning field of super-resolution fluorescence microscopy, significant efforts are being dedicated to expanding its applications into the 3D domain. Various methodologies have been developed that enable isotropic resolution at the nanometer scale, facilitating the visualization of 3D subcellular structures with unprecedented clarity. Central to this progress is the need for reliable 3D structures that are biologically compatible for validating resolution capabilities. Choosing the optimal standard poses a considerable challenge, necessitating, among other attributes, precisely defined geometry and the capability for specific labeling at sub-diffraction-limit distances. In this context, the use of the non-human-infecting virus, bacteriophage T4 is introduced as an effective and straightforward bio-ruler for 3D super-resolution imaging. Employing DNA point accumulation for imaging in nanoscale topography (DNA-PAINT) along with the technique of astigmatic imaging, the icosahedral capsid of the bacteriophage T4, measuring 120 nm in length and 86 nm in width, and its hollow viral tail is uncovered. This level of detail in light microscopy represents a significant advancement in T4 imaging. A simple protocol for the production and preparation of samples is further outlined. Moreover, the extensive potential of bacteriophage T4 as a multifaceted 3D bio-ruler, proposing its application as a novel benchmark for 3D super-resolution imaging in biological studies is explored.

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.

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

Tm3+-Based Downshifting Nanoprobes with Enhanced Luminescence at 1680 nm for In Vivo Vascular Growth Monitoring

Optical imaging in the 1500-1700 nm region, known as near-infrared IIb (NIR-IIb), shows potential for noninvasive in vivo detection owing to its ultrahigh tissue penetration depth and spatiotemporal resolution. Rare earth-doped nanoparticles have emerged as widely used NIR-IIb probes because of their excellent optical properties. However, their downshifting emissions rarely exhibit sufficient brightness beyond 1600 nm. This study presents tetragonal-phase thulium-doped nanoparticles (Tm3+-NPs) with core-shell-shell structures (CSS, LiYbF4:3%Tm@LiYbF4@LiYF4) that exhibit bright downshifting luminescence at 1680 nm. Enhanced luminescence is attributed to (1) the promoted nonradiative relaxation between the doping ions and (2) the maximized sensitization process. Additionally, this strategy was validated for NIR-IIb luminescence enhancement of erbium (Er3+)-doped NPs. After surface modification with PEGylated liposomes, tetragonal-phase Tm3+-NPs exhibited a prolonged blood cycle time, high colloidal stability, and good biocompatibility. Owing to the advantages of Tm3+-based probes in NIR-IIb imaging, in vivo thrombus detection and monitoring of angiogenesis and arteriogenesis were successfully performed in a mouse model of ischemic hind limbs.

Super-Resolved Protein Imaging Using Bifunctional Light-Up Aptamers

Efficient labeling methods for protein visualization with minimal tag size and appropriate photophysical properties are required for single-molecule localization microscopy (SMLM), providing insights into the organization and interactions of biomolecules in cells at the molecular level. Among the fluorescent light-up aptamers (FLAPs) originally developed for RNA imaging, RhoBAST stands out due to its remarkable brightness, photostability, fluorogenicity, and rapid exchange kinetics, enabling super-resolved imaging with high localization precision. Here, we expand the applicability of RhoBAST to protein imaging by fusing it to protein-binding aptamers. The versatility of such bifunctional aptamers is demonstrated by employing a variety of protein-binding aptamers and different FLAPs. Moreover, fusing RhoBAST with the GFP-binding aptamer AP3 facilitates high- and super-resolution imaging of GFP-tagged proteins, which is particularly valuable in view of the widespread availability of plasmids and stable cell lines expressing proteins fused to GFP. The bifunctional aptamers compare favorably with standard antibody-based immunofluorescence protocols, as they are 7-fold smaller than antibody conjugates and exhibit higher bleaching-resistance. We demonstrate the effectiveness of our approach in super-resolution microscopy in secondary mammalian cell lines and primary neurons by RhoBAST-PAINT, an SMLM protein imaging technique that leverages the transient binding of the fluorogenic rhodamine dye SpyRho to RhoBAST.

Obtaining super-resolved images at the mesoscale through super-resolution radial fluctuations

Significance

Current super-resolution imaging techniques allow for a greater understanding of cellular structures; however, they are often complex or only have the ability to image a few cells at once. This small field of view (FOV) may not represent the behavior across the entire sample, and manual selection of regions of interest (ROIs) may introduce bias. It is possible to stitch and tile many small ROIs; however, this can result in artifacts across an image.

Aim

The aim is to achieve accurate super-resolved images across a large FOV ( 4.4 × 3.0    mm ).

Approach

We have applied super-resolution radial fluctuations processing in conjunction with the Mesolens, which has the unusual combination of a low-magnification and high numerical aperture, to obtain super-resolved images.

Results

We demonstrate it is possible to achieve images with a resolution of 446.3 ± 10.9    nm , providing a ∼ 1.6 -fold improvement in spatial resolution, over an FOV of 4.4 × 3.0    mm , with minimal error, and consistent structural agreement.

Conclusions

We provide a simple method for obtaining accurate super-resolution images over a large FOV, allowing for a simultaneous understanding of both subcellular structures and their large-scale interactions.

High-throughput expansion microscopy enables scalable super-resolution imaging

Expansion microscopy (ExM) enables nanoscale imaging using a standard confocal microscope through the physical, isotropic expansion of fixed immunolabeled specimens. ExM is widely employed to image proteins, nucleic acids, and lipid membranes in single cells; however, current methods limit the number of samples that can be processed simultaneously. We developed High-throughput Expansion Microscopy (HiExM), a robust platform that enables expansion microscopy of cells cultured in a standard 96-well plate. Our method enables ~4.2 x expansion of cells within individual wells, across multiple wells, and between plates. We also demonstrate that HiExM can be combined with high-throughput confocal imaging platforms to greatly improve the ease and scalability of image acquisition. As an example, we analyzed the effects of doxorubicin, a known cardiotoxic agent, on human cardiomyocytes (CMs) as measured by the Hoechst signal across the nucleus. We show a dose-dependent effect on nuclear DNA that is not observed in unexpanded CMs, suggesting that HiExM improves the detection of cellular phenotypes in response to drug treatment. Our method broadens the application of ExM as a tool for scalable super-resolution imaging in biological research applications.

Deep learning enables the use of ultra-high-density array in DNBSEQ

DNBSEQ employs a patterned array to facilitate massively parallel sequencing of DNA nanoballs (DNBs), leading to a considerable boost in throughput. By employing the ultra-high-density (UHD) array with an increased density of DNB binding sites, the throughput of DNBSEQ can be further expanded. However, the typical imaging system of the DNBSEQ sequencer is unable to resolve adjacent DNBs spaced smaller than the resolution limit, resulting in poor base-calling performance of the UHD array and hindering its practical application. In this study, we propose a deep-learning-based DNB image super-resolution network named DNBSRN to address this problem. DNBSRN has a specifically designed structure for DNB images and employs a histogram-matching-based preprocessing approach. For the eight DNB image datasets generated from the DNBSEQ sequencer using UHD arrays with 360 nm pitch, the base-calling performances are significantly improved after super-resolution reconstruction by DNBSRN and reached a comparable level to those of the regular density array. In terms of reconstruction speed, DNBSRN takes only 7.61 ms for an input image with 500 × 500 pixels, which minimizes its influence on throughput. Furthermore, compared with state-of-the-art super-resolution networks, DNBSRN demonstrates superior performance in terms of both the quality and speed of DNB image reconstruction. DNBSRN successfully addresses the DNB image super-resolution task. Integrating DNBSRN into the image analysis workflow of DNBSEQ will allow for the application of UHD array, hence enabling a considerable improvement in throughput as well as tremendous savings in unit reagent cost.

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning.

Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis

Intravital 2P-microscopy enables the longitudinal study of brain tumor biology in superficial mouse cortex layers. Intravital microscopy of the white matter, an important route of glioblastoma invasion and recurrence, has not been feasible, due to low signal-to-noise ratios and insufficient spatiotemporal resolution. Here, we present an intravital microscopy and artificial intelligence-based analysis workflow (Deep3P) that enables longitudinal deep imaging of glioblastoma up to a depth of 1.2 mm. We find that perivascular invasion is the preferred invasion route into the corpus callosum and uncover two vascular mechanisms of glioblastoma migration in the white matter. Furthermore, we observe morphological changes after white matter infiltration, a potential basis of an imaging biomarker during early glioblastoma colonization. Taken together, Deep3P allows for a non-invasive intravital investigation of brain tumor biology and its tumor microenvironment at subcortical depths explored, opening up opportunities for studying the neuroscience of brain tumors and other model systems.

Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration

Fluorescence microscopy-based image restoration has received widespread attention in the life sciences and has led to significant progress, benefiting from deep learning technology. However, most current task-specific methods have limited generalizability to different fluorescence microscopy-based image restoration problems. Here, we seek to improve generalizability and explore the potential of applying a pretrained foundation model to fluorescence microscopy-based image restoration. We provide a universal fluorescence microscopy-based image restoration (UniFMIR) model to address different restoration problems, and show that UniFMIR offers higher image restoration precision, better generalization and increased versatility. Demonstrations on five tasks and 14 datasets covering a wide range of microscopy imaging modalities and biological samples demonstrate that the pretrained UniFMIR can effectively transfer knowledge to a specific situation via fine-tuning, uncover clear nanoscale biomolecular structures and facilitate high-quality imaging. This work has the potential to inspire and trigger new research highlights for fluorescence microscopy-based image restoration.

Adaptive optics in an oblique plane microscope

Adaptive optics (AO) can restore diffraction-limited performance when imaging beyond superficial cell layers in vivo and in vitro, and as such, is of interest for advanced 3D microscopy methods such as light-sheet fluorescence microscopy (LSFM). In a typical LSFM system, the illumination and detection paths are separate and subject to different optical aberrations. To achieve optimal microscope performance, it is necessary to sense and correct these aberrations in both light paths, resulting in a complex microscope system. Here, we show that in an oblique plane microscope (OPM), a type of LSFM with a single primary objective lens, the same deformable mirror can correct both illumination and fluorescence detection. Besides reducing the complexity, we show that AO in OPM also restores the relative alignment of the light-sheet and focal plane, and that a projection imaging mode can stabilize and improve the wavefront correction in a sensorless AO format. We demonstrate OPM with AO on fluorescent nanospheres and by imaging the vasculature and cancer cells in zebrafish embryos embedded in a glass capillary, restoring diffraction limited resolution and improving the signal strength twofold.

Fast spectroscopic imaging using extreme ultraviolet interferometry

Extreme ultraviolet pulses as generated by high harmonic generation (HHG) are a powerful tool for both time-resolved spectroscopy and coherent diffractive imaging. However, the integration of spectroscopy and microscopy to harness the unique broadband spectra provided by HHG is hardly explored due to the challenge to decouple spectroscopic and microscopic information. Here, we present an interferometric approach to this problem that combines Fourier transform spectroscopy (FTS) with Fourier transform holography (FTH). This is made possible by the generation of phase-locked pulses using a pair of HHG sources. Crucially, in our geometry the number of interferometric measurements required is at most equal to the number of high-harmonics in the illumination, and can be further reduced by incorporating prior knowledge about the structure of the FTH sample. Compared to conventional FTS, this approach achieves over an order of magnitude increase in acquisition speed for full spectro-microscopic data, and furthermore allows high-resolution computational imaging.

On-line correlative imaging of cryo-PALM and soft X-ray tomography for identification of subcellular structures

Correlative imaging of fluorescence microscopy and soft X-ray microscopy plays a crucial role in exploring the relationship between structure and function in cellular biology. However, the current correlative imaging methods are limited either to off-line or low-resolution fluorescence imaging. In this study, we developed an integrated on-line cryogenic photoactivated localization microscopy (cryo-PALM) system at a soft X-ray microscopy station. This design eliminates some critical issues such as sample damage and complex post-correlation arising from transferring samples between different cryostages. Furthermore, we successfully achieved correlative imaging of cryopreserved near-native cells, with a resolution of about 50 nm of cryo-PALM. Therefore, the developed on-line correlation imaging platform provides a powerful tool for investigating the intricate relationship between structure and function in biological and molecular interactions, as well as in other life science disciplines.

A non-toxic equinatoxin-II reveals the dynamics and distribution of sphingomyelin in the cytosolic leaflet of the plasma membrane

Sphingomyelin (SM) is a major sphingolipid in mammalian cells. SM is enriched in the extracellular leaflet of the plasma membrane (PM). Besides this localization, recent electron microscopic and biochemical studies suggest the presence of SM in the cytosolic leaflet of the PM. In the present study, we generated a non-toxic SM-binding variant (NT-EqtII) based on equinatoxin-II (EqtII) from the sea anemone Actinia equina, and examined the dynamics of SM in the cytosolic leaflet of living cell PMs. NT-EqtII with two point mutations (Leu26Ala and Pro81Ala) had essentially the same specificity and affinity to SM as wild-type EqtII. NT-EqtII expressed in the cytosol was recruited to the PM in various cell lines. Super-resolution microscopic observation revealed that NT-EqtII formed tiny domains that were significantly colocalized with cholesterol and N-terminal Lyn. Meanwhile, single molecule observation at high resolutions down to 1 ms revealed that all the examined lipid probes including NT-EqtII underwent apparent fast simple Brownian diffusion, exhibiting that SM and other lipids in the cytosolic leaflet rapidly moved in and out of domains. Thus, the novel SM-binding probe demonstrated the presence of the raft-like domain in the cytosolic leaflet of living cell PMs.

Self-Quenched Fluorophore-DNA Labels for Super-Resolution Fluorescence Microscopy

Protein labeling through transient and repetitive hybridization of short, fluorophore-labeled DNA oligonucleotides has become widely applied in various optical super-resolution microscopy methods. The main advantages are multitarget imaging and molecular quantification. A challenge is the high background signal originating from the presence of unbound fluorophore-DNA labels in solution. Here, we report the self-quenching of fluorophore dimers conjugated to DNA oligonucleotides as a general concept to reduce the fluorescence background. Upon hybridization, the fluorescence signals of both fluorophores are restored. We expand the toolbox of fluorophores suitable for self-quenching and report their spectra and hybridization equilibria. We apply self-quenched fluorophore-DNA labels to stimulated emission depletion microscopy and single-molecule localization microscopy and report improved imaging performances.

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

Correlative single-molecule and structured illumination microscopy of fast dynamics at the plasma membrane

Total internal reflection fluorescence (TIRF) microscopy offers powerful means to uncover the functional organization of proteins in the plasma membrane with very high spatial and temporal resolution. Traditional TIRF illumination, however, shows a Gaussian intensity profile, which is typically deteriorated by overlaying interference fringes hampering precise quantification of intensities-an important requisite for quantitative analyses in single-molecule localization microscopy (SMLM). Here, we combine flat-field illumination by using a standard πShaper with multi-angular TIR illumination by incorporating a spatial light modulator compatible with fast super-resolution structured illumination microscopy (SIM). This distinct combination enables quantitative multi-color SMLM with a highly homogenous illumination. By using a dual camera setup with optimized image splitting optics, we achieve a versatile combination of SMLM and SIM with up to three channels. We deploy this setup for establishing robust detection of receptor stoichiometries based on single-molecule intensity analysis and single-molecule Förster resonance energy transfer (smFRET). Homogeneous illumination furthermore enables long-term tracking and localization microscopy (TALM) of cell surface receptors identifying spatial heterogeneity of mobility and accessibility in the plasma membrane. By combination of TALM and SIM, spatially and molecularly heterogenous diffusion properties can be correlated with nanoscale cytoskeletal organization and dynamics.

Photoswitching Reagent for Super-Resolution Fluorescence Microscopy

Single-molecule localization microscopy (SMLM) has revolutionized optical microscopy by exceeding the diffraction limit and revealing previously unattainable nanoscale details of cellular structures and molecular dynamics. This super-resolution imaging capability relies on fluorophore photoswitching, which is crucial for optimizing the imaging conditions and accurately determining the fluorophore positions. To understand the general on and off photoswitching mechanisms of single dye molecules, various photoswitching reagents were evaluated. Systematic measurement of the single-molecule-level fluorescence on and off rates (kon and koff) in the presence of various photoswitching reagents and theoretical calculation of the structure of the photoswitching reagent-fluorophore pair indicated that the switch-off mechanism is mainly determined by the nucleophilicity of the photoswitching reagent, and the switch-on mechanism is a two-photon-induced dissociation process, which is related to the power of the illuminating laser and bond dissociation energy of this pair. This study contributes to a broader understanding of the molecular photoswitching mechanism in SMLM imaging and provides a basis for designing improved photoswitching reagents with potential applications extending to materials science and chemistry.

Highly Efficient Fluorescent Probe to Visualize Protein Interactions at the Superresolution

Superresolution microscopy (SR microscopy) of protein-protein interactions (PPIs) occurring in subcellular structures is essential for understanding cellular functions. However, a powerful and useful technology for SR microscopy of PPIs remains elusive. Here, we develop a highly efficient photoconvertible fluorescent probe, named split-Dendra2, for SR microscopy of PPIs in the cell. We found that split-Dendra2 enables a highly efficient detection of PPIs, making it possible to perform SR microscopy of PPIs with high spatial resolution and high image reconstruction fidelity. We demonstrate the utility of split-Dendra2 by visualizing PPIs occurring in small subcellular structures at the superresolution, such as clathrin-coated pits and focal adhesions, which cannot be visualized by the existing tools. Split-Dendra2 offers a powerful and useful tool that greatly expands the possibility of SR microscopy and can contribute to revealing the function of PPIs at the nanoscale resolution.

Single image Fourier ring correlation

We address resolution assessment for (light super-resolution) microscopy imaging. In modalities where imaging is not diffraction limited, correlation between two noise independent images is the standard way to infer the resolution. Here we take away the need for two noise independent images by computationally splitting one image acquisition into two noise independent realizations. This procedure generates two Poisson noise distributed images if the input is Poissonian distributed. As most modern cameras are shot-noise limited this procedure is directly applicable. However, also in the presence of readout noise we can compute the resolution faithfully via a correction factor. We evaluate our method on simulations and experimental data of widefield microscopy, STED microscopy, rescan confocal microscopy, image scanning microscopy, conventional confocal microscopy, and transmission electron microscopy. In all situations we find that using one image instead of two results in the same computed image resolution.

hydroSIM: super-resolution speckle illumination microscopy with a hydrogel diffuser

Super-resolution microscopy has emerged as an indispensable methodology for probing the intricacies of cellular biology. Structured illumination microscopy (SIM), in particular, offers an advantageous balance of spatial and temporal resolution, allowing for visualizing cellular processes with minimal disruption to biological specimens. However, the broader adoption of SIM remains hampered by the complexity of instrumentation and alignment. Here, we introduce speckle-illumination super-resolution microscopy using hydrogel diffusers (hydroSIM). The study utilizes the high scattering and optical transmissive properties of hydrogel materials and realizes a remarkably simplified approach to plug-in super-resolution imaging via a common epi-fluorescence platform. We demonstrate the hydroSIM system using various phantom and biological samples, and the results exhibited effective 3D resolution doubling, optical sectioning, and high contrast. We foresee hydroSIM, a cost-effective, biocompatible, and user-accessible super-resolution methodology, to significantly advance a wide range of biomedical imaging and applications.

Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy

Electron microscopy (EM) revolutionized the way to visualize cellular ultrastructure. Volume EM (vEM) has further broadened its three-dimensional nanoscale imaging capacity. However, intrinsic trade-offs between imaging speed and quality of EM restrict the attainable imaging area and volume. Isotropic imaging with vEM for large biological volumes remains unachievable. Here, we developed EMDiffuse, a suite of algorithms designed to enhance EM and vEM capabilities, leveraging the cutting-edge image generation diffusion model. EMDiffuse generates realistic predictions with high resolution ultrastructural details and exhibits robust transferability by taking only one pair of images of 3 megapixels to fine-tune in denoising and super-resolution tasks. EMDiffuse also demonstrated proficiency in the isotropic vEM reconstruction task, generating isotropic volume even in the absence of isotropic training data. We demonstrated the robustness of EMDiffuse by generating isotropic volumes from seven public datasets obtained from different vEM techniques and instruments. The generated isotropic volume enables accurate three-dimensional nanoscale ultrastructure analysis. EMDiffuse also features self-assessment functionalities on predictions' reliability. We envision EMDiffuse to pave the way for investigations of the intricate subcellular nanoscale ultrastructure within large volumes of biological systems.

Tales from the crick: The art of demo

Equipment demonstrations (demos) play an important role in the evaluation of new systems. As well as the excitement of exploring emerging technologies, a well-organised demo can help guide procurement decisions and support funding applications. However, it is easy to underestimate the substantial effort required both before and following the demo to maximise its potential impact. Here, we discuss how our approach to demos at the Crick Advanced Light Microscopy Science and Technology Platform (CALM-STP) has evolved over the last few years, emphasising the importance of a documented approach that combines quantitative with qualitative comparisons and engages with your user base in order to build up support for any potential system purchase.

Untrained neural network enabling fast and universal structured-illumination microscopy

Structured-illumination microscopy (SIM) offers a twofold resolution enhancement beyond the optical diffraction limit. At present, SIM requires several raw structured-illumination (SI) frames to reconstruct a super-resolution (SR) image, especially the time-consuming reconstruction of speckle SIM, which requires hundreds of SI frames. Considering this, we herein propose an untrained structured-illumination reconstruction neural network (USRNN) with known illumination patterns to reduce the amount of raw data that is required for speckle SIM reconstruction by 20 times and thus improve its temporal resolution. Benefiting from the unsupervised optimizing strategy and CNNs' structure priors, the high-frequency information is obtained from the network without the requirement of datasets; as a result, a high-fidelity SR image with approximately twofold resolution enhancement can be reconstructed using five frames or less. Experiments on reconstructing non-biological and biological samples demonstrate the high-speed and high-universality capabilities of our method.

Selective-plane-activation structured illumination microscopy

The background light from out-of-focus planes hinders resolution enhancement in structured illumination microscopy when observing volumetric samples. Here we used selective plane illumination and reversibly photoswitchable fluorescent proteins to realize structured illumination within the focal plane and eliminate the out-of-focus background. Theoretical investigation of the imaging properties and experimental demonstrations show that selective plane activation is beneficial for imaging dense microstructures in cells and cell spheroids.

Bright and stable monomeric green fluorescent protein derived from StayGold

The high brightness and photostability of the green fluorescent protein StayGold make it a particularly attractive probe for long-term live-cell imaging; however, its dimeric nature precludes its application as a fluorescent tag for some proteins. Here, we report the development and crystal structures of a monomeric variant of StayGold, named mBaoJin, which preserves the beneficial properties of its precursor, while serving as a tag for structural proteins and membranes. Systematic benchmarking of mBaoJin against popular green fluorescent proteins and other recently introduced monomeric and pseudomonomeric derivatives of StayGold established mBaoJin as a bright and photostable fluorescent protein, exhibiting rapid maturation and high pH/chemical stability. mBaoJin was also demonstrated for super-resolution, long-term live-cell imaging and expansion microscopy. We further showed the applicability of mBaoJin for neuronal labeling in model organisms, including Caenorhabditis elegans and mice.

Adaptive Optics in an Oblique Plane Microscope

Adaptive optics (AO) can restore diffraction limited performance when imaging beyond superficial cell layers in vivo and in vitro, and as such is of interest for advanced 3D microscopy methods such as light-sheet fluorescence microscopy (LSFM). In a typical LSFM system, the illumination and detection paths are separate and subject to different optical aberrations. To achieve optimal microscope performance, it is necessary to sense and correct these aberrations in both light paths, resulting in a complex microscope system. Here, we show that in an oblique plane microscope (OPM), a type of LSFM with a single primary objective lens, the same deformable mirror can correct both the illumination and fluorescence detection. Besides reducing the complexity, we show that AO in OPM also restores the relative alignment of the light-sheet and focal plane, and that a projection imaging mode can stabilize and improve the wavefront correction in a sensorless AO format. We demonstrate OPM with AO on fluorescent nanospheres and by imaging the vasculature and cancer cells in zebrafish embryos embedded in a glass capillary, restoring diffraction limited resolution and improving the signal strength twofold.

A versatile Wavelet-Enhanced CNN-Transformer for improved fluorescence microscopy image restoration

Fluorescence microscopes are indispensable tools for the life science research community. Nevertheless, the presence of optical component limitations, coupled with the maximum photon budget that the specimen can tolerate, inevitably leads to a decline in imaging quality and a lack of useful signals. Therefore, image restoration becomes essential for ensuring high-quality and accurate analyses. This paper presents the Wavelet-Enhanced Convolutional-Transformer (WECT), a novel deep learning technique developed specifically for the purpose of reducing noise in microscopy images and attaining super-resolution. Unlike traditional approaches, WECT integrates wavelet transform and inverse-transform for multi-resolution image decomposition and reconstruction, resulting in an expanded receptive field for the network without compromising information integrity. Subsequently, multiple consecutive parallel CNN-Transformer modules are utilized to collaboratively model local and global dependencies, thus facilitating the extraction of more comprehensive and diversified deep features. In addition, the incorporation of generative adversarial networks (GANs) into WECT enhances its capacity to generate high perceptual quality microscopic images. Extensive experiments have demonstrated that the WECT framework outperforms current state-of-the-art restoration methods on real fluorescence microscopy data under various imaging modalities and conditions, in terms of quantitative and qualitative analysis.

PCNA as Protein-Based Nanoruler for Sub-10 nm Fluorescence Imaging

Super-resolution microscopy has revolutionized biological imaging enabling direct insight into cellular structures and protein arrangements with so far unmatched spatial resolution. Today, refined single-molecule localization microscopy methods achieve spatial resolutions in the one-digit nanometer range. As the race for molecular resolution fluorescence imaging with visible light continues, reliable biologically compatible reference structures will become essential to validate the resolution power. Here, PicoRulers (protein-based imaging calibration optical rulers), multilabeled oligomeric proteins designed as advanced molecular nanorulers for super-resolution fluorescence imaging are introduced. Genetic code expansion (GCE) is used to site-specifically incorporate three noncanonical amino acids (ncAAs) into the homotrimeric proliferating cell nuclear antigen (PCNA) at 6 nm distances. Bioorthogonal click labeling with tetrazine-dyes and tetrazine-functionalized oligonucleotides allows efficient labeling of the PicoRuler with minimal linkage error. Time-resolved photoswitching fingerprint analysis is used to demonstrate the successful synthesis and DNA-based points accumulation for imaging in nanoscale topography (DNA-PAINT) is used to resolve 6 nm PCNA PicoRulers. Since PicoRulers maintain their structural integrity under cellular conditions they represent ideal molecular nanorulers for benchmarking the performance of super-resolution imaging techniques, particularly in complex biological environments.

Full field-of-view hexagonal lattice structured illumination microscopy based on the phase shift of electro-optic modulators

High throughput has become an important research direction in the field of super-resolution (SR) microscopy, especially in improving the capability of dynamic observations. In this study, we present a hexagonal lattice structured illumination microscopy (hexSIM) system characterized by a large field of view (FOV), rapid imaging speed, and high power efficiency. Our approach employs spatial light interference to generate a two-dimensional hexagonal SIM pattern, and utilizes electro-optical modulators for high-speed phase shifting. This design enables the achievement of a 210-µm diameter SIM illumination FOV when using a 100×/1.49 objective lens, capturing 2048 × 2048 pixel images at an impressive 98 frames per second (fps) single frame rate. Notably, this method attains a near 100% full field-of-view and power efficiency, with the speed limited only by the camera's capabilities. Our hexSIM demonstrates a substantial 1.73-fold improvement in spatial resolution and necessitates only seven phase-shift images, thus enhancing the imaging speed compared to conventional 2D-SIM.

Easily scalable multi-color DMD-based structured illumination microscopy

Structured illumination microscopy (SIM) achieves super-resolution imaging using a series of phase-shifted sinusoidal illumination patterns to down-modulate high spatial-frequency information of samples. Digital micromirror devices (DMDs) have been increasingly used to generate SIM illumination patterns due to their high speed and moderate cost. However, a DMD micromirror array's blazed grating structure causes strong angular dispersion for different wavelengths of light, thus severely hampering its application in multicolor imaging. We developed a multi-color DMD-SIM setup that employs a diffraction grating to compensate the DMD's dispersion and demonstrate super-resolution SIM imaging of both fluorescent beads and live cells samples with four color channels. This simple but effective approach can be readily scaled to more color channels, thereby greatly expanding the application of SIM in the study of complex multi-component structures and dynamics in soft matter systems.

The function of ER-phagy receptors is regulated through phosphorylation-dependent ubiquitination pathways

Selective autophagy of the endoplasmic reticulum (ER), known as ER-phagy, is an important regulator of ER remodeling and essential to maintain cellular homeostasis during environmental changes. We recently showed that members of the FAM134 family play a critical role during stress-induced ER-phagy. However, the mechanisms on how they are activated remain largely unknown. In this study, we analyze phosphorylation of FAM134 as a trigger of FAM134-driven ER-phagy upon mTOR (mechanistic target of rapamycin) inhibition. An unbiased screen of kinase inhibitors reveals CK2 to be essential for FAM134B- and FAM134C-driven ER-phagy after mTOR inhibition. Furthermore, we provide evidence that ER-phagy receptors are regulated by ubiquitination events and that treatment with E1 inhibitor suppresses Torin1-induced ER-phagy flux. Using super-resolution microscopy, we show that CK2 activity is essential for the formation of high-density FAM134B and FAM134C clusters. In addition, dense clustering of FAM134B and FAM134C requires phosphorylation-dependent ubiquitination of FAM134B and FAM134C. Treatment with the CK2 inhibitor SGC-CK2-1 or mutation of FAM134B and FAM134C phosphosites prevents ubiquitination of FAM134 proteins, formation of high-density clusters, as well as Torin1-induced ER-phagy flux. Therefore, we propose that CK2-dependent phosphorylation of ER-phagy receptors precedes ubiquitin-dependent activation of ER-phagy flux.

Spatial redundancy transformer for self-supervised fluorescence image denoising

Fluorescence imaging with high signal-to-noise ratios has become the foundation of accurate visualization and analysis of biological phenomena. However, the inevitable noise poses a formidable challenge to imaging sensitivity. Here we provide the spatial redundancy denoising transformer (SRDTrans) to remove noise from fluorescence images in a self-supervised manner. First, a sampling strategy based on spatial redundancy is proposed to extract adjacent orthogonal training pairs, which eliminates the dependence on high imaging speed. Second, we designed a lightweight spatiotemporal transformer architecture to capture long-range dependencies and high-resolution features at low computational cost. SRDTrans can restore high-frequency information without producing oversmoothed structures and distorted fluorescence traces. Finally, we demonstrate the state-of-the-art denoising performance of SRDTrans on single-molecule localization microscopy and two-photon volumetric calcium imaging. SRDTrans does not contain any assumptions about the imaging process and the sample, thus can be easily extended to various imaging modalities and biological applications.

High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation

Live-cell super-resolution microscopy enables the imaging of biological structure dynamics below the diffraction limit. Here we present enhanced super-resolution radial fluctuations (eSRRF), substantially improving image fidelity and resolution compared to the original SRRF method. eSRRF incorporates automated parameter optimization based on the data itself, giving insight into the trade-off between resolution and fidelity. We demonstrate eSRRF across a range of imaging modalities and biological systems. Notably, we extend eSRRF to three dimensions by combining it with multifocus microscopy. This realizes live-cell volumetric super-resolution imaging with an acquisition speed of ~1 volume per second. eSRRF provides an accessible super-resolution approach, maximizing information extraction across varied experimental conditions while minimizing artifacts. Its optimal parameter prediction strategy is generalizable, moving toward unbiased and optimized analyses in super-resolution microscopy.

DBlink: dynamic localization microscopy in super spatiotemporal resolution via deep learning

Single-molecule localization microscopy (SMLM) has revolutionized biological imaging, improving the spatial resolution of traditional microscopes by an order of magnitude. However, SMLM techniques require long acquisition times, typically a few minutes, to yield a single super-resolved image, because they depend on accumulation of many localizations over thousands of recorded frames. Hence, the capability of SMLM to observe dynamics at high temporal resolution has always been limited. In this work, we present DBlink, a deep-learning-based method for super spatiotemporal resolution reconstruction from SMLM data. The input to DBlink is a recorded video of SMLM data and the output is a super spatiotemporal resolution video reconstruction. We use a convolutional neural network combined with a bidirectional long short-term memory network architecture, designed for capturing long-term dependencies between different input frames. We demonstrate DBlink performance on simulated filaments and mitochondria-like structures, on experimental SMLM data under controlled motion conditions and on live-cell dynamic SMLM. DBlink's spatiotemporal interpolation constitutes an important advance in super-resolution imaging of dynamic processes in live cells.

Deep learning enables stochastic optical reconstruction microscopy-like superresolution image reconstruction from conventional microscopy

Despite its remarkable potential for transforming low-resolution images, deep learning faces significant challenges in achieving high-quality superresolution microscopy imaging from wide-field (conventional) microscopy. Here, we present X-Microscopy, a computational tool comprising two deep learning subnets, UR-Net-8 and X-Net, which enables STORM-like superresolution microscopy image reconstruction from wide-field images with input-size flexibility. X-Microscopy was trained using samples of various subcellular structures, including cytoskeletal filaments, dot-like, beehive-like, and nanocluster-like structures, to generate prediction models capable of producing images of comparable quality to STORM-like images. In addition to enabling multicolour superresolution image reconstructions, X-Microscopy also facilitates superresolution image reconstruction from different conventional microscopic systems. The capabilities of X-Microscopy offer promising prospects for making superresolution microscopy accessible to a broader range of users, going beyond the confines of well-equipped laboratories.

Deep learning-driven adaptive optics for single-molecule localization microscopy

The inhomogeneous refractive indices of biological tissues blur and distort single-molecule emission patterns generating image artifacts and decreasing the achievable resolution of single-molecule localization microscopy (SMLM). Conventional sensorless adaptive optics methods rely on iterative mirror changes and image-quality metrics. However, these metrics result in inconsistent metric responses and thus fundamentally limit their efficacy for aberration correction in tissues. To bypass iterative trial-then-evaluate processes, we developed deep learning-driven adaptive optics for SMLM to allow direct inference of wavefront distortion and near real-time compensation. Our trained deep neural network monitors the individual emission patterns from single-molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter and drives a deformable mirror to compensate sample-induced aberrations. We demonstrated that our method simultaneously estimates and compensates 28 wavefront deformation shapes and improves the resolution and fidelity of three-dimensional SMLM through >130-µm-thick brain tissue specimens.

3D structured illumination microscope using a spinning disk [Invited]

Three-dimensional (3D) structured illumination microscopy (SIM) improves spatial resolution by a factor of two in both lateral and axial directions. However, the adoption of 3D SIM is limited by low imaging speed, susceptibility to out-of-focus light, and likelihood of reconstruction errors. Here we present a novel approach for 3D SIM using a spinning disk. The disk generates a 3D lattice illumination pattern on the sample and optically reconstructs super-resolved images in real time. This technique achieves a 2-times resolution improvement with a speed up to 100 frames per second while physically rejecting 90% of the background signal.

Three-dimensional multi-color optical nanoscopy at sub-10-nm resolution based on small-molecule organic probes

Achieving nanometer-scale resolution remains challenging in expansion microscopy due to photon loss. To address this concern, here we develop a multi-color expansion stimulated emission depletion technique based on small-molecule probes to realize high labeling density and intensity. Our method substantially lowers the barrier to visualizing diverse intracellular proteins and their interactions in three dimensions. It enables us to achieve sub-10-nm resolution in structures such as microfilaments, lysosomes, and mitochondria, providing new insights into cell biology.

An intelligent workflow for sub-nanoscale 3D reconstruction of intact synapses from serial section electron tomography

BACKGROUND

As an extension of electron tomography (ET), serial section electron tomography (serial section ET) aims to align the tomographic images of multiple thick tissue sections together, to break through the volume limitation of the single section and preserve the sub-nanoscale voxel size. It could be applied to reconstruct the intact synapse, which expands about one micrometer and contains nanoscale vesicles. However, there are several drawbacks of the existing serial section ET methods. First, locating and imaging regions of interest (ROIs) in serial sections during the shooting process is time-consuming. Second, the alignment of ET volumes is difficult due to the missing information caused by section cutting and imaging. Here we report a workflow to simplify the acquisition of ROIs in serial sections, automatically align the volume of serial section ET, and semi-automatically reconstruct the target synaptic structure.

RESULTS

We propose an intelligent workflow to reconstruct the intact synapse with sub-nanometer voxel size. Our workflow includes rapid localization of ROIs in serial sections, automatic alignment, restoration, assembly of serial ET volumes, and semi-automatic target structure segmentation. For the localization and acquisition of ROIs in serial sections, we use affine transformations to calculate their approximate position based on their relative location in orderly placed sections. For the alignment of consecutive ET volumes with significantly distinct appearances, we use multi-scale image feature matching and the elastic with belief propagation (BP-Elastic) algorithm to align them from coarse to fine. For the restoration of the missing information in ET, we first estimate the number of lost images based on the pixel changes of adjacent volumes after alignment. Then, we present a missing information generation network that is appropriate for small-sample of ET volume using pre-training interpolation network and distillation learning. And we use it to generate the missing information to achieve the whole volume reconstruction. For the reconstruction of synaptic ultrastructures, we use a 3D neural network to obtain them quickly. In summary, our workflow can quickly locate and acquire ROIs in serial sections, automatically align, restore, assemble serial sections, and obtain the complete segmentation result of the target structure with minimal manual manipulation. Multiple intact synapses in wild-type rat were reconstructed at a voxel size of 0.664 nm/voxel to demonstrate the effectiveness of our workflow.

CONCLUSIONS

Our workflow contributes to obtaining intact synaptic structures at the sub-nanometer scale through serial section ET, which contains rapid ROI locating, automatic alignment, volume reconstruction, and semi-automatic synapse reconstruction. We have open-sourced the relevant code in our workflow, so it is easy to apply it to other labs and obtain complete 3D ultrastructures which size is similar to intact synapses with sub-nanometer voxel size.

Neural network-assisted single-molecule localization microscopy with a weak-affinity protein tag

Single-molecule localization microscopy achieves nanometer spatial resolution by localizing single fluorophores separated in space and time. A major challenge of single-molecule localization microscopy is the long acquisition time, leading to low throughput, as well as to a poor temporal resolution that limits its use to visualize the dynamics of cellular structures in live cells. Another challenge is photobleaching, which reduces information density over time and limits throughput and the available observation time in live-cell applications. To address both challenges, we combine two concepts: first, we integrate the neural network DeepSTORM to predict super-resolution images from high-density imaging data, which increases acquisition speed. Second, we employ a direct protein label, HaloTag7, in combination with exchangeable ligands (xHTLs), for fluorescence labeling. This labeling method bypasses photobleaching by providing a constant signal over time and is compatible with live-cell imaging. The combination of both a neural network and a weak-affinity protein label reduced the acquisition time up to ∼25-fold. Furthermore, we demonstrate live-cell imaging with increased temporal resolution, and capture the dynamics of the endoplasmic reticulum over extended time without signal loss.

Three-dimensional structured illumination microscopy with enhanced axial resolution

The axial resolution of three-dimensional structured illumination microscopy (3D SIM) is limited to ∼300 nm. Here we present two distinct, complementary methods to improve axial resolution in 3D SIM with minimal or no modification to the optical system. We show that placing a mirror directly opposite the sample enables four-beam interference with higher spatial frequency content than 3D SIM illumination, offering near-isotropic imaging with ∼120-nm lateral and 160-nm axial resolution. We also developed a deep learning method achieving ∼120-nm isotropic resolution. This method can be combined with denoising to facilitate volumetric imaging spanning dozens of timepoints. We demonstrate the potential of these advances by imaging a variety of cellular samples, delineating the nanoscale distribution of vimentin and microtubule filaments, observing the relative positions of caveolar coat proteins and lysosomal markers and visualizing cytoskeletal dynamics within T cells in the early stages of immune synapse formation.