Enhanced resolution through thick tissue with structured illumination and adaptive optics

Tandem aberration correction optics (TACO) in wide-field structured illumination microscopy

Structured illumination microscopy (SIM) is a powerful super-resolution imaging technique that uses patterned illumination to down-modulate high spatial-frequency information of samples. However, the presence of spatially-dependent aberrations can severely disrupt the illumination pattern, limiting the quality of SIM imaging. Conventional adaptive optics (AO) techniques that employ wavefront correctors at the pupil plane are not capable of effectively correcting these spatially-dependent aberrations. We introduce the Tandem Aberration Correction Optics (TACO) approach that combines both pupil AO and conjugate AO for aberration correction in SIM. TACO incorporates a deformable mirror (DM) for pupil AO in the detection path to correct for global aberrations, while a spatial light modulator (SLM) is placed at the plane conjugate to the aberration source near the sample plane, termed conjugate AO, to compensate spatially-varying aberrations in the illumination path. Our numerical simulations and experimental results show that the TACO approach can recover the illumination pattern close to an ideal condition, even when severely misshaped by aberrations, resulting in high-quality super-resolution SIM reconstruction. The TACO approach resolves a critical traditional shortcoming of aberration correction for structured illumination. This advance significantly expands the application of SIM imaging in the study of complex, particularly biological, samples and should be effective in other wide-field microscopies.

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.

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.

Adaptive optics for optical microscopy [Invited]

Optical microscopy is widely used to visualize fine structures. When applied to bioimaging, its performance is often degraded by sample-induced aberrations. In recent years, adaptive optics (AO), originally developed to correct for atmosphere-associated aberrations, has been applied to a wide range of microscopy modalities, enabling high- or super-resolution imaging of biological structure and function in complex tissues. Here, we review classic and recently developed AO techniques and their applications in optical microscopy.

Super-resolution microscopy: a brief history and new avenues

Super-resolution microscopy (SRM) is a fast-developing field that encompasses fluorescence imaging techniques with the capability to resolve objects below the classical diffraction limit of optical resolution. Acknowledged with the Nobel prize in 2014, numerous SRM methods have meanwhile evolved and are being widely applied in biomedical research, all with specific strengths and shortcomings. While some techniques are capable of nanometre-scale molecular resolution, others are geared towards volumetric three-dimensional multi-colour or fast live-cell imaging. In this editorial review, we pick on the latest trends in the field. We start with a brief historical overview of both conceptual and commercial developments. Next, we highlight important parameters for imaging successfully with a particular super-resolution modality. Finally, we discuss the importance of reproducibility and quality control and the significance of open-source tools in microscopy. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 2)'.

Adaptive optics in super-resolution microscopy

Fluorescence microscopy has become a routine tool in biology for interrogating life activities with minimal perturbation. While the resolution of fluorescence microscopy is in theory governed only by the diffraction of light, the resolution obtainable in practice is also constrained by the presence of optical aberrations. The past two decades have witnessed the advent of super-resolution microscopy that overcomes the diffraction barrier, enabling numerous biological investigations at the nanoscale. Adaptive optics, a technique borrowed from astronomical imaging, has been applied to correct for optical aberrations in essentially every microscopy modality, especially in super-resolution microscopy in the last decade, to restore optimal image quality and resolution. In this review, we briefly introduce the fundamental concepts of adaptive optics and the operating principles of the major super-resolution imaging techniques. We highlight some recent implementations and advances in adaptive optics for active and dynamic aberration correction in super-resolution microscopy.

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)'.

Subcellular three-dimensional imaging deep through multicellular thick samples by structured illumination microscopy and adaptive optics

Structured Illumination Microscopy enables live imaging with sub-diffraction resolution. Unfortunately, optical aberrations can lead to loss of resolution and artifacts in Structured Illumination Microscopy rendering the technique unusable in samples thicker than a single cell. Here we report on the combination of Adaptive Optics and Structured Illumination Microscopy enabling imaging with 150 nm lateral and 570 nm axial resolution at a depth of 80 µm through Caenorhabditis elegans. We demonstrate that Adaptive Optics improves the three-dimensional resolution, especially along the axial direction, and reduces artifacts, successfully realizing 3D-Structured Illumination Microscopy in a variety of biological samples.

Super-resolved live-cell imaging using random illumination microscopy

Current super-resolution microscopy (SRM) methods suffer from an intrinsic complexity that might curtail their routine use in cell biology. We describe here random illumination microscopy (RIM) for live-cell imaging at super-resolutions matching that of 3D structured illumination microscopy, in a robust fashion. Based on speckled illumination and statistical image reconstruction, easy to implement and user-friendly, RIM is unaffected by optical aberrations on the excitation side, linear to brightness, and compatible with multicolor live-cell imaging over extended periods of time. We illustrate the potential of RIM on diverse biological applications, from the mobility of proliferating cell nuclear antigen (PCNA) in U2OS cells and kinetochore dynamics in mitotic S. pombe cells to the 3D motion of myosin minifilaments deep inside Drosophila tissues. RIM's inherent simplicity and extended biological applicability, particularly for imaging at increased depths, could help make SRM accessible to biology laboratories.

Adaptive optics for structured illumination microscopy based on deep learning

Structured illumination microscopy (SIM) is widely used in biological imaging for its high resolution, fast imaging speed, and simple optical setup. However, when imaging thick samples, the structured illumination patterns in SIM will suffer from optical aberrations, leading to a serious deterioration in resolution. Therefore, it is necessary to reconstruct structured illumination patterns with high quality and efficiency in deep tissue imaging. Here we demonstrate an adaptive optics (AO) correction method based on deep learning in wide-field SIM imaging system. The mapping between the coefficients of the first 15 Zernike modes and their corresponding distorted patterns is established to train the convolution neural network (CNN). The results show that the optimized CNN can predict the aberration phase within ~10.1 ms with a personal computer. The correlation index between the aberration phases and their corresponding predicted aberration phase is up to 0.9986. This method is highly robust and effective for patterns with various spatial densities and illumination conditions and able to effectively correct the imaging distortion caused by optical aberration in SIM system.

Upconversion Nonlinear Structured Illumination Microscopy

Video-rate super-resolution imaging through biological tissue can visualize and track biomolecule interplays and transportations inside cellular organisms. Structured illumination microscopy allows for wide-field super resolution observation of biological samples but is limited by the strong extinction of light by biological tissues, which restricts the imaging depth and degrades its imaging resolution. Here we report a photon upconversion scheme using lanthanide-doped nanoparticles for wide-field super-resolution imaging through the biological transparent window, featured by near-infrared and low-irradiance nonlinear structured illumination. We demonstrate that the 976 nm excitation and 800 nm upconverted emission can mitigate the aberration. We found that the nonlinear response of upconversion emissions from single nanoparticles can effectively generate the required high spatial frequency components in the Fourier domain. These strategies lead to a new modality in microscopy with a resolution below 131 nm, 1/7th of the excitation wavelength, and an imaging rate of 1 Hz.

25th Anniversary of STED Microscopy and the 20th Anniversary of SIM: feature introduction

This feature issue commemorating 25 years of STED microscopy and 20 years of SIM is intended to highlight the incredible progress and growth in the field of superresolution microscopy since Stefan Hell and Jan Wichmann published the article Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy in Optics Letters in 1994.

BeamDelta: simple alignment tool for optical systems

BeamDelta is a tool to help align optical systems. It greatly assists in assembling bespoke optical systems by providing a live view of the current laser beam position and a reference position. Even a simple optical setup has multiple degrees of freedom that affect the alignment of beam paths. These degrees of freedom rise exponentially with the complexity of the system. The process of aligning all the optical components for a specific system is often esoteric and poorly documented, if it is documented at all. Alignment methods used often rely on visual inspection of beams impinging on pinholes in the beam path, typically requiring an experienced operator staring at diffuse reflections for extended periods of time. This can lead to a decline in accuracy due to eye strain, flash blindness as well as symptoms such as headaches and, possibly, more serious retinal damage. Here we present BeamDelta a simple alignment tool and accompanying software interface which allows users to obtain accurate alignment as well as removing the necessity of staring at diffuse laser reflections. BeamDelta is a robust alignment tool as it doesn't require any precise alignment itself.

Fluorescence imaging with tailored light

Fluorescence microscopy has long been a valuable tool for biological and medical imaging. Control of optical parameters such as the amplitude, phase, polarization and propagation angle of light gives fluorescence imaging great capabilities ranging from super-resolution imaging to long-term real-time observation of living organisms. In this review, we discuss current fluorescence imaging techniques in terms of the use of tailored or structured light for the sample illumination and fluorescence detection, providing a clear overview of their working principles and capabilities.

Highly Sensitive Shack-Hartmann Wavefront Sensor: Application to Non-Transparent Tissue Mimic Imaging with Adaptive Light-Sheet Fluorescence Microscopy

High-quality in-depth imaging of three-dimensional samples remains a major challenge in modern microscopy. Selective plane illumination microscopy (SPIM) is a widely used technique that enables imaging of living tissues with subcellular resolution. However, scattering, absorption, and optical aberrations limit the depth at which useful imaging can be done. Adaptive optics (AOs) is a method capable of measuring and correcting aberrations in different kinds of fluorescence microscopes, thereby improving the performance of the optical system. We have incorporated a wavefront sensor adaptive optics scheme to SPIM (_WAOSPIM) to correct aberrations induced by optically-thick samples, such as multi-cellular tumor spheroids (MCTS). Two-photon fluorescence provides us with a tool to produce a weak non-linear guide star (NGS) in any region of the field of view. The faintness of NGS; however, led us to develop a high-sensitivity Shack–Hartmann wavefront sensor (SHWS). This paper describes this newly developed SHWS and shows the correction capabilities of _WAOSPIM using NGS in thick, inhomogeneous samples like MCTS. We report improvements of up to 79% for spatial frequencies corresponding to cellular and subcellular size features.

Dynamic super-resolution structured illumination imaging in the living brain

Cells in the brain act as components of extended networks. Therefore, to understand neurobiological processes in a physiological context, it is essential to study them in vivo. Super-resolution microscopy has spatial resolution beyond the diffraction limit, thus promising to provide structural and functional insights that are not accessible with conventional microscopy. However, to apply it to in vivo brain imaging, we must address the challenges of 3D imaging in an optically heterogeneous tissue that is constantly in motion. We optimized image acquisition and reconstruction to combat sample motion and applied adaptive optics to correcting sample-induced optical aberrations in super-resolution structured illumination microscopy (SIM) in vivo. We imaged the brains of live zebrafish larvae and mice and observed the dynamics of dendrites and dendritic spines at nanoscale resolution.

IsoSense: frequency enhanced sensorless adaptive optics through structured illumination

We present IsoSense, a wavefront sensing method that mitigates sample dependency in image-based sensorless adaptive optics applications in microscopy. Our method employs structured illumination to create additional high spatial frequencies in the image through custom illumination patterns. This improves the reliability of image quality metric calculations and enables sensorless wavefront measurement even in samples with sparse spatial frequency content. We demonstrate the feasibility of IsoSense for aberration correction in a deformable-mirror-based structured illumination super-resolution fluorescence microscope.

Imaging neural events in zebrafish larvae with linear structured illumination light sheet fluorescence microscopy

Light sheet fluorescence microscopy (LSFM) is a powerful tool for investigating model organisms including zebrafish. However, due to scattering and refractive index variations within the sample, the resulting image often suffers from low contrast. Structured illumination (SI) has been combined with scanned LSFM to remove out-of-focus and scattered light using square-law detection. Here, we demonstrate that the combination of LSFM with linear reconstruction SI can further increase resolution and contrast in the vertical and axial directions compared to the widely adopted root-mean square reconstruction method while using the same input images. We apply this approach to imaging neural activity in 7-day postfertilization zebrafish larvae. We imaged two-dimensional sections of the zebrafish central nervous system in two colors at an effective frame rate of 7 frames per second.

Faster, sharper, and deeper: structured illumination microscopy for biological imaging

Structured illumination microscopy (SIM) allows rapid, super-resolution (SR) imaging in live specimens. We review recent technical advances in SR-SIM, with emphasis on imaging speed, resolution, and depth. Since its introduction decades ago, the technique has grown to offer myriad implementations, each with its own strengths and weaknesses. We discuss these, aiming to provide a practical guide for biologists and to highlight which approach is best suited to a given application.

Adaptive optics stochastic optical reconstruction microscopy (AO-STORM) by particle swarm optimization

Stochastic optical reconstruction microscopy (STORM) can achieve resolutions of better than 20nm imaging single fluorescently labeled cells. However, when optical aberrations induced by larger biological samples degrade the point spread function (PSF), the localization accuracy and number of localizations are both reduced, destroying the resolution of STORM. Adaptive optics (AO) can be used to correct the wavefront, restoring the high resolution of STORM. A challenge for AO-STORM microscopy is the development of robust optimization algorithms which can efficiently correct the wavefront from stochastic raw STORM images. Here we present the implementation of a particle swarm optimization (PSO) approach with a Fourier metric for real-time correction of wavefront aberrations during STORM acquisition. We apply our approach to imaging boutons 100 μm deep inside the central nervous system (CNS) of Drosophila melanogaster larvae achieving a resolution of 146 nm.

Adaptive optics improves multiphoton super-resolution imaging

We improve multiphoton structured illumination microscopy using a nonlinear guide star to determine optical aberrations and a deformable mirror to correct them. We demonstrate our method on bead phantoms, cells in collagen gels, nematode larvae and embryos, Drosophila brain, and zebrafish embryos. Peak intensity is increased (up to 40-fold) and resolution recovered (up to 176 ± 10 nm laterally, 729 ± 39 nm axially) at depths ∼250 μm from the coverslip surface.

Characterization of wavefront errors in mouse cranial bone using second-harmonic generation

Optical aberrations significantly affect the resolution and signal-to-noise ratio of deep tissue microscopy. As multiphoton microscopy is applied deeper into tissue, the loss of resolution and signal due to propagation of light in a medium with heterogeneous refractive index becomes more serious. Efforts in imaging through the intact skull of mice cannot typically reach past the bone marrow ( ? 150 ?? ? m of depth) and have limited resolution and penetration depth. Mechanical bone thinning or optical ablation of bone enables deeper imaging, but these methods are highly invasive and may impact tissue biology. Adaptive optics is a promising noninvasive alternative for restoring optical resolution. We characterize the aberrations present in bone using second-harmonic generation imaging of collagen. We simulate light propagation through highly scattering bone and evaluate the effect of aberrations on the point spread function. We then calculate the wavefront and expand it in Zernike orthogonal polynomials to determine the strength of different optical aberrations. We further compare the corrected wavefront and the residual wavefront error, and suggest a correction element with high number of elements or multiconjugate wavefront correction for this highly scattering environment.

3D resolution enhancement of deep-tissue imaging based on virtual spatial overlap modulation microscopy

During the last decades, several resolution enhancement methods for optical microscopy beyond diffraction limit have been developed. Nevertheless, those hardware-based techniques typically require strong illumination, and fail to improve resolution in deep tissue. Here we develop a high-speed computational approach, three-dimensional virtual spatial overlap modulation microscopy (3D-vSPOM), which immediately solves the strong-illumination issue. By amplifying only the spatial frequency component corresponding to the un-scattered point-spread-function at focus, plus 3D nonlinear value selection, 3D-vSPOM shows significant resolution enhancement in deep tissue. Since no iteration is required, 3D-vSPOM is much faster than iterative deconvolution. Compared to non-iterative deconvolution, 3D-vSPOM does not need a priori information of point-spread-function at deep tissue, and provides much better resolution enhancement plus greatly improved noise-immune response. This method is ready to be amalgamated with two-photon microscopy or other laser scanning microscopy to enhance deep-tissue resolution.

In vivo volumetric imaging of biological dynamics in deep tissue via wavefront engineering

Biological systems undergo dynamical changes continuously which span multiple spatial and temporal scales. To study these complex biological dynamics in vivo, high-speed volumetric imaging that can work at large imaging depth is highly desired. However, deep tissue imaging suffers from wavefront distortion, resulting in reduced Strehl ratio and image quality. Here we combine the two wavefront engineering methods developed in our lab, namely the optical phase-locked ultrasound lens based volumetric imaging and the iterative multiphoton adaptive compensation technique, and demonstrate in vivo volumetric imaging of microglial and mitochondrial dynamics at large depth in mouse brain cortex and lymph node, respectively.

Aberrations and adaptive optics in super-resolution microscopy

As one of the most powerful tools in the biological investigation of cellular structures and dynamic processes, fluorescence microscopy has undergone extraordinary developments in the past decades. The advent of super-resolution techniques has enabled fluorescence microscopy - or rather nanoscopy - to achieve nanoscale resolution in living specimens and unravelled the interior of cells with unprecedented detail. The methods employed in this expanding field of microscopy, however, are especially prone to the detrimental effects of optical aberrations. In this review, we discuss how super-resolution microscopy techniques based upon single-molecule switching, stimulated emission depletion and structured illumination each suffer from aberrations in different ways that are dependent upon intrinsic technical aspects. We discuss the use of adaptive optics as an effective means to overcome this problem.

Adaptive optics stochastic optical reconstruction microscopy (AO-STORM) using a genetic algorithm

The resolution of Single Molecule Localization Microscopy (SML) is dependent on the width of the Point Spread Function (PSF) and the number of photons collected. However, biological samples tend to degrade the shape of the PSF due to the heterogeneity of the index of refraction. In addition, there are aberrations caused by imperfections in the optical components and alignment, and the refractive index mismatch between the coverslip and the sample, all of which directly reduce the accuracy of SML. Adaptive Optics (AO) can play a critical role in compensating for aberrations in order to increase the resolution. However the stochastic nature of single molecule emission presents a challenge for wavefront optimization because the large fluctuations in photon emission do not permit many traditional optimization techniques to be used. Here we present an approach that optimizes the wavefront during SML acquisition by combining an intensity independent merit function with a Genetic algorithm (GA) to optimize the PSF despite the fluctuating intensity. We demonstrate the use of AO with GA in tissue culture cells and through ~50µm of tissue in the Drosophila Central Nervous System (CNS) to achieve a 4-fold increase in the localization precision.