Instant super-resolution imaging in live cells and embryos via analog image processing

The GTP-tubulin cap is not the determinant of microtubule end stability in cells

Microtubules are dynamic cytoskeletal polymers essential for cell division, motility, and intracellular transport. Microtubule dynamics are characterized by dynamic instability-the ability of individual microtubules to switch between phases of growth and shrinkage. Dynamic instability can be explained by the GTP-cap model, suggesting that a "cap" of GTP-tubulin subunits at the growing microtubule end has a stabilizing effect, protecting against microtubule catastrophe-the switch from growth to shrinkage. Although the GTP-cap is thought to protect the growing microtubule end, whether the GTP-cap size affects microtubule stability in cells is not known. Notably, microtubule end-binding proteins, EBs, recognize the nucleotide state of tubulin and display comet-like localization at growing microtubule ends, which can be used as a proxy for the GTP-cap. Here, we employ high spatiotemporal resolution imaging to compare the relationship between EB comet size and microtubule dynamics in interphase LLC-PK1 cells to that measured in vitro. Our data reveal that the GTP-cap size in cells scales with the microtubule growth rate in the same way as in vitro. However, we find that microtubule ends in cells can withstand transition to catastrophe even after the EB comet is lost. Thus, our findings suggest that the presence of the GTP-cap is not the determinant of microtubule end stability in cells.

Neural space-time model for dynamic multi-shot imaging

Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.

A comparison of super-resolution microscopy techniques for imaging tightly packed microcolonies of an obligate intracellular bacterium

Conventional optical microscopy imaging of obligate intracellular bacteria is hampered by the small size of bacterial cells, tight clustering exhibited by some bacterial species and challenges relating to labelling such as background from host cells, a lack of validated reagents, and a lack of tools for genetic manipulation. In this study we imaged intracellular bacteria from the species Orientia tsutsugamushi (Ot) using five different fluorescence microscopy techniques: standard confocal, Airyscan confocal, instant Structured Illumination Microscopy (iSIM), three-dimensional Structured Illumination Microscopy (3D-SIM) and Stimulated Emission Depletion Microscopy (STED). We compared the ability of each to resolve bacterial cells in intracellular clumps in the lateral (xy) axis, using full width half maximum (FWHM) measurements of a labelled outer membrane protein (ScaA) and the ability to detect small, outer membrane vesicles external to the cells. We next compared the ability of each technique to sufficiently resolve bacteria in the axial (z) direction and found 3D-STED to be the most successful method for this. We then combined this approach with a custom 3D cell segmentation and analysis pipeline using the open-source, deep learning software, Cellpose to segment the cells and subsequently the commercial software Imaris to analyze their 3D shape and size. Using this combination, we demonstrated differences in bacterial shape, but not their size, when grown in different mammalian cell lines. Overall, we compare the advantages and disadvantages of different super-resolution microscopy techniques for imaging this cytoplasmic obligate intracellular bacterium based on the specific research question being addressed.

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT based backbone model from pairwise high-low Signal-to-Noise Ratio (SNR) image volumes, gathered from a single type of fluorescence microscope, an instant Structured Illumination Microscope. Fast adaptation to new microscopes is achieved by fine-tuning the backbone on only 5-10 image volume pairs per new experiment. Results show that the CNNT backbone and fine-tuning scheme significantly reduces training time and improves image quality, outperforming models trained using only CNNs such as 3D-RCAN and Noise2Fast. We show three examples of efficacy of this approach in wide-field, two-photon, and confocal fluorescence microscopy.

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 into the imaging path. 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.

Single-cell topographical profiling of the immune synapse reveals a biomechanical signature of cytotoxicity

Immune cells have intensely physical lifestyles characterized by structural plasticity and force exertion. To investigate whether specific immune functions require stereotyped mechanical outputs, we used super-resolution traction force microscopy to compare the immune synapses formed by cytotoxic T cells with contacts formed by other T cell subsets and by macrophages. T cell synapses were globally compressive, which was fundamentally different from the pulling and pinching associated with macrophage phagocytosis. Spectral decomposition of force exertion patterns from each cell type linked cytotoxicity to compressive strength, local protrusiveness, and the induction of complex, asymmetric topography. These features were validated as cytotoxic drivers by genetic disruption of cytoskeletal regulators, live imaging of synaptic secretion, and in silico analysis of interfacial distortion. Synapse architecture and force exertion were sensitive to target stiffness and size, suggesting that the mechanical potentiation of killing is biophysically adaptive. We conclude that cellular cytotoxicity and, by implication, other effector responses are supported by specialized patterns of efferent force.

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.

Speckle-enabled in vivo demixing of neural activity in the mouse brain

Functional imaging of neuronal activity in awake animals, using a combination of fluorescent reporters of neuronal activity and various types of microscopy modalities, has become an indispensable tool in neuroscience. While various imaging modalities based on one-photon (1P) excitation and parallel (camera-based) acquisition have been successfully used for imaging more transparent samples, when imaging mammalian brain tissue, due to their scattering properties, two-photon (2P) microscopy systems are necessary. In 2P microscopy, the longer excitation wavelengths reduce the amount of scattering while the diffraction-limited 3D localization of excitation largely eliminates out-of-focus fluorescence. However, this comes at the cost of time-consuming serial scanning of the excitation spot and more complex and expensive instrumentation. Thus, functional 1P imaging modalities that can be used beyond the most transparent specimen are highly desirable. Here, we transform light scattering from an obstacle into a tool. We use speckles with their unique patterns and contrast, formed when fluorescence from individual neurons propagates through rodent cortical tissue, to encode neuronal activity. Spatiotemporal demixing of these patterns then enables functional recording of neuronal activity from a group of discriminable sources. For the first time, we provide an experimental, in vivo characterization of speckle generation, speckle imaging and speckle-assisted demixing of neuronal activity signals in the scattering mammalian brain tissue. We found that despite an initial fast speckle decorrelation, substantial correlation was maintained over minute-long timescales that contributed to our ability to demix temporal activity traces in the mouse brain in vivo. Informed by in vivo quantifications of speckle patterns from single and multiple neurons excited using 2P scanning excitation, we recorded and demixed activity from several sources excited using 1P oblique illumination. In our proof-of-principle experiments, we demonstrate in vivo speckle-assisted demixing of functional signals from groups of sources in a depth range of 220-320 µm in mouse cortex, limited by available speckle contrast. Our results serve as a basis for designing an in vivo functional speckle imaging modality and for maximizing the key resource in any such modality, the speckle contrast. We anticipate that our results will provide critical quantitative guidance to the community for designing techniques that overcome light scattering as a fundamental limitation in bioimaging.

Live-cell imaging powered by computation

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

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.

Advancements and Practical Considerations for Biophysical Research: Navigating the Challenges and Future of Super-resolution Microscopy

The introduction of super-resolution microscopy (SRM) has significantly advanced our understanding of cellular and molecular dynamics, offering a detailed view previously beyond our reach. Implementing SRM in biophysical research, however, presents numerous challenges. This review addresses the crucial aspects of utilizing SRM effectively, from selecting appropriate fluorophores and preparing samples to analyzing complex data sets. We explore recent technological advancements and methodological improvements that enhance the capabilities of SRM. Emphasizing the integration of SRM with other analytical methods, we aim to overcome inherent limitations and expand the scope of biological insights achievable. By providing a comprehensive guide for choosing the most suitable SRM methods based on specific research objectives, we aim to empower researchers to explore complex biological processes with enhanced precision and clarity, thereby advancing the frontiers of biophysical research.

Mechanical-scan-free multicolor super-resolution imaging with diffractive spot array illumination

Point-scanning microscopy approaches are transforming super-resolution imaging. Despite achieving parallel high-speed imaging using multifocal techniques, efficient multicolor imaging methods with high-quality illumination are currently lacking. In this paper, we present for the first time Mechanical-scan-free multiColor Super-resolution Microscopy (MCoSM) with spot array illumination, which enables mechanical-scan-free super-resolution imaging with adjustable resolution and a good effective field-of-view based on spatial light modulators. Through 100-2,500 s super-resolution spot illumination with different effective fields of view for imaging, we demonstrate the adjustable capacity of MCoSM. MCoSM extends existing spectral imaging capabilities through a time-sharing process involving different color illumination with phase-shift scanning while retaining the spatial flexibility of super-resolution imaging with diffractive spot array illumination. To demonstrate the prospects of MCoSM, we perform four-color imaging of fluorescent beads at high resolution. MCoSM provides a versatile platform for studying molecular interactions in complex samples at the nanoscale level.

Food perception promotes phosphorylation of MFFS131 and mitochondrial fragmentation in liver

Liver mitochondria play a central role in metabolic adaptations to changing nutritional states, yet their dynamic regulation upon anticipated changes in nutrient availability has remained unaddressed. Here, we found that sensory food perception rapidly induced mitochondrial fragmentation in the liver through protein kinase B/AKT (AKT)-dependent phosphorylation of serine 131 of the mitochondrial fission factor (MFFS131). This response was mediated by activation of hypothalamic pro-opiomelanocortin (POMC)-expressing neurons. A nonphosphorylatable MFFS131G knock-in mutation abrogated AKT-induced mitochondrial fragmentation in vitro. In vivo, MFFS131G knock-in mice displayed altered liver mitochondrial dynamics and impaired insulin-stimulated suppression of hepatic glucose production. Thus, rapid activation of a hypothalamus-liver axis can adapt mitochondrial function to anticipated changes of nutritional state in control of hepatic glucose metabolism.

Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation

Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically with tens to hundreds of image pairs) it can then be used to enhance new images that are like the training data. In this study, we proposed a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), to outperform the CNN networks for image denoising. In our scheme we have trained a single CNNT based "backbone model" from pairwise high-low SNR images for one type of fluorescence microscope (instance structured illumination, iSim). Fast adaption to new applications was achieved by fine-tuning the backbone on only 5-10 sample pairs per new experiment. Results show the CNNT backbone and fine-tuning scheme significantly reduces the training time and improves the image quality, outperformed training separate models using CNN approaches such as - RCAN and Noise2Fast. Here we show three examples of the efficacy of this approach on denoising wide-field, two-photon and confocal fluorescence data. In the confocal experiment, which is a 5×5 tiled acquisition, the fine-tuned CNNT model reduces the scan time form one hour to eight minutes, with improved quality.

Nano-clusters of ligand-activated integrins organize immobile, signalling active, nano-clusters of phosphorylated FAK required for mechanosignaling in focal adhesions

Transmembrane signalling receptors, such as integrins, organise as nanoclusters that are thought to provide several advantages including, increasing avidity, sensitivity (increasing the signal-to-noise ratio) and robustness (signalling above a threshold rather than activation by a single receptor) of the signal compared to signalling by single receptors. Compared to large micron-sized clusters, nanoclusters offer the advantage of rapid turnover for the disassembly of the signal. However, if nanoclusters function as signalling hubs remains poorly understood. Here, we employ fluorescence nanoscopy combined with photoactivation and photobleaching at sub-diffraction limited resolution of ~100nm length scale within a focal adhesion to examine the dynamics of diverse focal adhesion proteins. We show that (i) subregions of focal adhesions are enriched in immobile population of integrin β3 organised as nanoclusters, which (ii) in turn serve to organise nanoclusters of associated key adhesome proteins- vinculin, focal adhesion kinase (FAK) and paxillin, demonstrating that signalling proceeds by formation of nanoclusters rather than through individual proteins. (iii) Distinct focal adhesion protein nanoclusters exhibit distinct dynamics dependent on function. (iv) long-lived nanoclusters function as signalling hubs- wherein phosphorylated FAK and paxillin formed stable nanoclusters in close proximity to immobile integrin nanoclusters which are disassembled in response to inactivation signal by phosphatase PTPN12 (v) signalling takes place in response to an external signal such as force or geometric arrangement of the nanoclusters and when the signal is removed, these nanoclusters disassemble. Taken together, these results demonstrate that signalling downstream of transmembrane receptors is organised as hubs of signalling proteins (FAK, paxillin, vinculin) seeded by nanoclusters of the transmembrane receptor (integrin).

In situ architecture of Opa1-dependent mitochondrial cristae remodeling

Cristae membrane state plays a central role in regulating mitochondrial function and cellular metabolism. The protein Optic atrophy 1 (Opa1) is an important crista remodeler that exists as two forms in the mitochondrion, a membrane-anchored long form (l-Opa1) and a processed short form (s-Opa1). The mechanisms for how Opa1 influences cristae shape have remained unclear due to lack of native three-dimensional views of cristae. We perform in situ cryo-electron tomography of cryo-focused ion beam milled mouse embryonic fibroblasts with defined Opa1 states to understand how each form of Opa1 influences cristae architecture. In our tomograms, we observe a variety of cristae shapes with distinct trends dependent on s-Opa1:l-Opa1 balance. Increased l-Opa1 levels promote cristae stacking and elongated mitochondria, while increased s-Opa1 levels correlated with irregular cristae packing and round mitochondria shape. Functional assays indicate a role for l-Opa1 in wild-type apoptotic and calcium handling responses, and show a compromised respiratory function under Opa1 imbalance. In summary, we provide three-dimensional visualization of cristae architecture to reveal relationships between mitochondrial ultrastructure and cellular function dependent on Opa1-mediated membrane remodeling.

Pixel-reassigned line-scanning microscopy for fast volumetric super-resolution imaging

Super-resolution microscopy has revolutionized the field of biophotonics by revealing detailed 3D biological structures. Nonetheless, the technique is still largely limited by the low throughput and hampered by increased background signals for dense or thick biological specimens. In this paper, we present a pixel-reassigned continuous line-scanning microscope for large-scale high-speed 3D super-resolution imaging, which achieves an imaging resolution of 0.41 µm in the lateral direction, i.e., a 2× resolution enhancement from the raw images. Specifically, the recorded line images are first reassigned to the line-excitation center at each scanning position to enhance the resolution. Next, a modified HiLo algorithm is applied to reduce the background signals. Parametric models have been developed to simulate the imaging results of randomly distributed fluorescent beads. Imaging experiments were designed and performed to verify the predicted performance on various biological samples, which demonstrated an imaging speed of 3400 pixels/ms on millimeter-scale specimens. These results confirm the pixel-reassigned line-scanning microscopy is a facile and powerful method to realize large-area super-resolution imaging on thick or dense biological samples.

Image scanning microscopy: a vectorial physical optics analysis

Image scanning microscopy (ISM) achieves resolution beyond the diffraction limit by a factor of 2. However, prior ISM research predominantly employs scalar diffraction theory, neglecting critical physical effects such as polarization, aberrations, and Stokes shift. This paper presents a comprehensive vectorial ISM point spread function (PSF) model that accounts for these phenomena. By considering the effect of polarization in emission and excitation paths, as well as aberrations and Stokes shift, our model provides a more accurate representation of ISM. We analyze the differences between scalar and vectorial theories in ISM and investigate the impact of pinhole size and aberration strength on resolution. At a numerical aperture of 1.2, the full width half maximum (FWHM) discrepancy between scalar and vectorial ISM PSFs can reach 45 nm, representing a 30% deviation from the vectorial model. Additionally, we explore multiphoton excitation in ISM and observe increased FWHM for 2-photon and 3-photon excitation compared to 1-photon excitation. The FWHM of the 2-photon excitation ISM PSF increases by 20% and the FWHM of the 3-photon excitation ISM PSF increases by 28% compared to the 1-photon excitation ISM. In addition, we found that the optimal sweep factor for 2-photon ISM is 1.22, and the optimal sweep factor of 3-photon ISM is 1.12 instead of the 2 predicted by the one-photon scalar ISM theory. Our work improves the understanding of ISM and contributes to its advancement as a high-resolution imaging technique.

Three-dimensional multifocal scanning microscopy for super-resolution cell and tissue imaging

Recent advancements in image-scanning microscopy have significantly enriched super-resolution biological research, providing deeper insights into cellular structures and processes. However, current image-scanning techniques often require complex instrumentation and alignment, constraining their broader applicability in cell biological discovery and convenient, cost-effective integration into commonly used frameworks like epi-fluorescence microscopes. Here, we introduce three-dimensional multifocal scanning microscopy (3D-MSM) for super-resolution imaging of cells and tissue with substantially reduced instrumental complexity. This method harnesses the inherent 3D movement of specimens to achieve stationary, multi-focal excitation and super-resolution microscopy through a standard epi-fluorescence platform. We validated the system using a range of phantom, single-cell, and tissue specimens. The combined strengths of structured illumination, confocal detection, and epi-fluorescence setup result in two-fold resolution improvement in all three dimensions, effective optical sectioning, scalable volume acquisition, and compatibility with general imaging and sample protocols. We anticipate that 3D-MSM will pave a promising path for future super-resolution investigations in cell and tissue biology.

Fluorescence Microscopy: a statistics-optics perspective

Fundamental properties of light unavoidably impose features on images collected using fluorescence microscopes. Modeling these features is ever more important in quantitatively interpreting microscopy images collected at scales on par or smaller than light's wavelength. Here we review the optics responsible for generating fluorescent images, fluorophore properties, microscopy modalities leveraging properties of both light and fluorophores, in addition to the necessarily probabilistic modeling tools imposed by the stochastic nature of light and measurement.

Simultaneous Multicolor Multifocal Scanning Microscopy

Super-resolution fluorescence microscopy has revolutionized cell biology over the past decade, enabling the visualization of subcellular complexity with unparalleled clarity and detail. However, the rapid development of image-scanning-based super-resolution systems still restrains convenient access to commonly used instruments such as epi-fluorescence microscopes. Here, we present multifocal scanning microscopy (MSM) for super-resolution imaging with simultaneous multicolor acquisition and minimal instrumental complexity. MSM implements a stationary, interposed multifocal multicolor excitation by exploiting the motion of the specimens, realizing super-resolution microscopy through a general epi-fluorescence platform without compromising the image-scanning mechanism or inducing complex instrument alignment. The system is demonstrated with various phantom and biological specimens, and the results present effective resolution doubling, optical sectioning, and contrast enhancement. We anticipate MSM, as a highly accessible and compatible super-resolution technique, to offer a promising methodological pathway for broad cell biological discoveries.

Amphiphiles Formed from Synthetic DNA-Nanomotifs Mimic the Stepwise Dispersal of Transcriptional Clusters in the Cell Nucleus

Stem cells exhibit prominent clusters controlling the transcription of genes into RNA. These clusters form by a phase-separation mechanism, and their size and shape are controlled via an amphiphilic effect of transcribed genes. Here, we construct amphiphile-nanomotifs purely from DNA, and we achieve similar size and shape control for phase-separated droplets formed from fully synthetic, self-interacting DNA-nanomotifs. Increasing amphiphile concentrations induce rounding of droplets, prevent droplet fusion, and, at high concentrations, cause full dispersal of droplets. Super-resolution microscopy data obtained from zebrafish embryo stem cells reveal a comparable transition for transcriptional clusters with increasing transcription levels. Brownian dynamics and lattice simulations further confirm that the addition of amphiphilic particles is sufficient to explain the observed changes in shape and size. Our work reproduces key aspects of transcriptional cluster formation in biological cells using relatively simple DNA sequence-programmable nanostructures, opening novel ways to control the mesoscopic organization of synthetic nanomaterials.

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.

Deep-MSIM: Fast Image Reconstruction with Deep Learning in Multifocal Structured Illumination Microscopy

Fast and precise reconstruction algorithm is desired for for multifocal structured illumination microscopy (MSIM) to obtain the super-resolution image. This work proposes a deep convolutional neural network (CNN) to learn a direct mapping from raw MSIM images to super-resolution image, which takes advantage of the computational advances of deep learning to accelerate the reconstruction. The method is validated on diverse biological structures and in vivo imaging of zebrafish at a depth of 100 µm. The results show that high-quality, super-resolution images can be reconstructed in one-third of the runtime consumed by conventional MSIM method, without compromising spatial resolution. Last but not least, a fourfold reduction in the number of raw images required for reconstruction is achieved by using the same network architecture, yet with different training data.

Deep self-learning enables fast, high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy

One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its introduction is the unmatched resolution in the lateral and axial directions (i.e., resolution anisotropy), which severely deteriorates the quality, reconstruction, and analysis of 3D volume images. By leveraging the natural anisotropy, we present a deep self-learning method termed Self-Net that significantly improves the resolution of axial images by using the lateral images from the same raw dataset as rational targets. By incorporating unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery, our method can effectively suppress the hallucination with substantially enhanced image quality compared to previously reported methods. In the experiments, we show that Self-Net can reconstruct high-fidelity isotropic 3D images from organelle to tissue levels via raw images from various microscopy platforms, e.g., wide-field, laser-scanning, or super-resolution microscopy. For the first time, Self-Net enables isotropic whole-brain imaging at a voxel resolution of 0.2 × 0.2 × 0.2 μm3, which addresses the last-mile problem of data quality in single-neuron morphology visualization and reconstruction with minimal effort and cost. Overall, Self-Net is a promising approach to overcoming the inherent resolution anisotropy for all classes of 3D fluorescence microscopy.

Targeting N-linked Glycosylation for the Therapy of Aggressive Lymphomas

Diffuse large B-cell lymphoma (DLBCL) can be subdivided into the activated B-cell (ABC) and germinal center B cell-like (GCB) subtypes. Self-antigen engagement of B-cell receptors (BCR) in ABC tumors induces their clustering, thereby initiating chronic active signaling and activation of NF-κB and PI3 kinase. Constitutive BCR signaling is essential in some GCB tumors but primarily activates PI3 kinase. We devised genome-wide CRISPR-Cas9 screens to identify regulators of IRF4, a direct transcriptional target of NF-κB and an indicator of proximal BCR signaling in ABC DLBCL. Unexpectedly, inactivation of N-linked protein glycosylation by the oligosaccharyltransferase-B (OST-B) complex reduced IRF4 expression. OST-B inhibition of BCR glycosylation reduced BCR clustering and internalization while promoting its association with CD22, which attenuated PI3 kinase and NF-κB activation. By directly interfering with proximal BCR signaling, OST-B inactivation killed models of ABC and GCB DLBCL, supporting the development of selective OST-B inhibitors for the treatment of these aggressive cancers.

SIGNIFICANCE

DLBCL depends on constitutive BCR activation and signaling. There are currently no therapeutics that target the BCR directly and attenuate its pathologic signaling. Here, we unraveled a therapeutically exploitable, OST-B-dependent glycosylation pathway that drives BCR organization and proximal BCR signaling. This article is highlighted in the In This Issue feature, p. 1749.

Structured illumination microscopy with a phase-modulated spinning disk for optical sectioning

Among various super-resolution microscopic techniques, structured illumination microscopy (SIM) stands out for live-cell imaging because of its higher imaging speed. However, conventional SIM lacks optical sectioning capability. Here we demonstrate a new, to the best of our knowledge, approach using a phase-modulated spinning disk (PMSD) that enhances the optical sectioning capability of SIM. The PMSD consists of a pinhole array for confocal imaging and a transparent polymer layer for light phase modulation. The light phase modulation was designed to cancel the zeroth-order diffracted beam and create a sharp lattice illumination pattern using the interference of four first-order diffracted beams. In the detection optical path, the PMSD serves as a spatial filter to physically reject about 80% of the out-of-focus signals, an approach that allows for real-time optical reconstruction of super-resolved images with enhanced contrast. Furthermore, the simplicity of the design makes it easy to upgrade a conventional fluorescence microscope to a PMSD SIM system.

Image scanning microscopy with a doughnut beam: signal strength and integrated intensity

We discuss the effects of image scanning microscopy using doughnut beam illumination on the properties of signal strength and integrated intensity. Doughnut beam illumination can give better optical sectioning and background rejection than Airy disk illumination. The outer pixels of a detector array give a signal from defocused regions, so digital processing of these (e.g., by simple subtraction) can further improve optical sectioning and background rejection from a single in-focus scan.

Proteins rather than mRNAs regulate nucleation and persistence of Oskar germ granules in Drosophila

RNA granules are membraneless condensates that provide functional compartmentalization within cells. The mechanisms by which RNA granules form are under intense investigation. Here, we characterize the role of mRNAs and proteins in the formation of germ granules in Drosophila. Super-resolution microscopy reveals that the number, size, and distribution of germ granules is precisely controlled. Surprisingly, germ granule mRNAs are not required for the nucleation or the persistence of germ granules but instead control their size and composition. Using an RNAi screen, we determine that RNA regulators, helicases, and mitochondrial proteins regulate germ granule number and size, while the proteins of the endoplasmic reticulum, nuclear pore complex, and cytoskeleton control their distribution. Therefore, the protein-driven formation of Drosophila germ granules is mechanistically distinct from the RNA-dependent condensation observed for other RNA granules such as stress granules and P-bodies.

Mitochondrial networks through the lens of mathematics

Mitochondria serve a wide range of functions within cells, most notably via their production of ATP. Although their morphology is commonly described as bean-like, mitochondria often form interconnected networks within cells that exhibit dynamic restructuring through a variety of physical changes. Further, though relationships between form and function in biology are well established, the extant toolkit for understanding mitochondrial morphology is limited. Here, we emphasize new and established methods for quantitatively describing mitochondrial networks, ranging from unweighted graph-theoretic representations to multi-scale approaches from applied topology, in particular persistent homology. We also show fundamental relationships between mitochondrial networks, mathematics, and physics, using ideas of graph planarity and statistical mechanics to better understand the full possible morphological space of mitochondrial network structures. Lastly, we provide suggestions for how examination of mitochondrial network form through the language of mathematics can inform biological understanding, and vice versa.

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.

Topographical analysis of immune cell interactions reveals a biomechanical signature for immune cytolysis

Immune cells live intensely physical lifestyles characterized by structural plasticity, mechanosensitivity, and force exertion. Whether specific immune functions require stereotyped patterns of mechanical output, however, is largely unknown. To address this question, we used super-resolution traction force microscopy to compare cytotoxic T cell immune synapses with contacts formed by other T cell subsets and macrophages. T cell synapses were globally and locally protrusive, which was fundamentally different from the coupled pinching and pulling of macrophage phagocytosis. By spectrally decomposing the force exertion patterns of each cell type, we associated cytotoxicity with compressive strength, local protrusiveness, and the induction of complex, asymmetric interfacial topographies. These features were further validated as cytotoxic drivers by genetic disruption of cytoskeletal regulators, direct imaging of synaptic secretory events, and in silico analysis of interfacial distortion. We conclude that T cell-mediated killing and, by implication, other effector responses are supported by specialized patterns of efferent force.

Recruiting Rate Determines the Blinking Propensity of Rhodamine Fluorophores for Super-Resolution Imaging

Live-cell single-molecule localization microscopy has advanced with the development of self-blinking rhodamines. A pK_cycling of <6 is recognized as the criterion for self-blinking, yet a few rhodamines matching the standard fail for super-resolution reconstruction. To resolve this controversy, we constructed two classic rhodamines (pK_cycling < 6) and four sulfonamide rhodamines with three exhibited exceptional larger pK_cycling characteristics (6.91-7.34). A kinetic study uncovered slow equilibrium rates, and limited switch numbers resulted in the reconstruction failure of some rhodamines. From the kinetic disparity, a recruiting rate was first abstracted to reveal the natural switching frequency of spirocycling equilibrium. The new parameter independent from applying a laser satisfactorily explained the imaging failure, efficacious for determining the propensity of self-blinking from a kinetic perspective. Following the prediction from this parameter, the sulfonamide rhodamines enabled live-cell super-resolution imaging of various organelles through Halo-tag technology. It is determined that the recruiting rate would be a practical indicator of self-blinking and imaging performance.

Imaging nanoscale axial dynamics at the basal plasma membrane

Understanding of how energetically unfavorable plasma membrane shapes form, especially in the context of dynamic processes in living cells or tissues like clathrin-mediated endocytosis is in its infancy. Even though cutting-edge microscopy techniques that bridge this gap exist, they remain underused in biomedical sciences. Here, we demystify the perceived complexity of these advanced microscopy approaches and demonstrate their power in resolving nanometer axial dynamics in living cells. Total internal reflection fluorescence microscopy based approaches are the main focus of this review. We present clathrin-mediated endocytosis as a model system when describing the principles, data acquisition requirements, data interpretation strategies, and limitations of the described techniques. We hope this standardized description will bring the approaches for measuring nanoscale axial dynamics closer to the potential users and help in choosing the right approach to the right question.

Resolution and uniformity improvement of parallel confocal microscopy based on microlens arrays and a spatial light modulator

In traditional fluorescence microscopy, it is hard to achieve a large uniform imaging field with high resolution. In this manuscript, we developed a confocal fluorescence microscope combining the microlens array with spatial light modulator to address this issue. In our system, a multi-spot array generated by a spatial light modulator passes through the microlens array to form an optical probe array. Then multi-spot adaptive pixel-reassignment method for image scanning microscopy (MAPR-ISM) will be introduced in this parallelized imaging to improve spatial resolution. To generate a uniform image, we employ an optimized double weighted Gerchberg-Saxton algorithm (ODWGS) using signal feedback from the camera. We have built a prototype system with a FOV of 3.5 mm × 3.5 mm illuminated by 2500 confocal points. The system provides a lateral resolution of ∼0.82 µm with ∼1.6 times resolution enhancement after ISM processing. And the nonuniformity across the whole imaging field is 3%. Experimental results of fluorescent beads, mouse brain slices and melanoma slices are presented to validate the applicability and effectiveness of our system.

Myonuclear alterations associated with exercise are independent of age in humans

Age-related decline in skeletal muscle structure and function can be mitigated by regular exercise. However, the precise mechanisms that govern this are not fully understood. The nucleus plays an active role in translating forces into biochemical signals (mechanotransduction), with the nuclear lamina protein lamin A regulating nuclear shape, nuclear mechanics and ultimately gene expression. Defective lamin A expression causes muscle pathologies and premature ageing syndromes, but the roles of nuclear structure and function in physiological ageing and in exercise adaptations remain obscure. Here, we isolated single muscle fibres and carried out detailed morphological and functional analyses on myonuclei from young and older exercise-trained individuals. Strikingly, myonuclei from trained individuals were more spherical, less deformable, and contained a thicker nuclear lamina than those from untrained individuals. Complementary to this, exercise resulted in increased levels of lamin A and increased myonuclear stiffness in mice. We conclude that exercise is associated with myonuclear remodelling, independently of age, which may contribute to the preservative effects of exercise on muscle function throughout the lifespan. KEY POINTS: The nucleus plays an active role in translating forces into biochemical signals. Myonuclear aberrations in a group of muscular dystrophies called laminopathies suggest that the shape and mechanical properties of myonuclei are important for maintaining muscle function. Here, striking differences are presented in myonuclear shape and mechanics associated with exercise, in both young and old humans. Myonuclei from trained individuals were more spherical, less deformable and contained a thicker nuclear lamina than untrained individuals. It is concluded that exercise is associated with age-independent myonuclear remodelling, which may help to maintain muscle function throughout the lifespan.

Incorporating the image formation process into deep learning improves network performance

We present Richardson-Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, establishing a connection to the image formation process and thereby improving network performance. Containing only roughly 16,000 parameters, RLN enables four- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms classic Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides four- to sixfold faster reconstructions of large, cleared-tissue datasets than classic multi-view pipelines. We demonstrate RLN's performance on cells, tissues and embryos imaged with widefield-, light-sheet-, confocal- and super-resolution microscopy.

Multiscale fluorescence imaging of living samples

Fluorescence microscopy is a highly effective tool for interrogating biological structure and function, particularly when imaging across multiple spatiotemporal scales. Here we survey recent innovations and applications in the relatively understudied area of multiscale fluorescence imaging of living samples. We discuss fundamental challenges in live multiscale imaging and describe successful examples that highlight the power of this approach. We attempt to synthesize general strategies from these test cases, aiming to help accelerate progress in this exciting area.

Event-driven acquisition for content-enriched microscopy

A common goal of fluorescence microscopy is to collect data on specific biological events. Yet, the event-specific content that can be collected from a sample is limited, especially for rare or stochastic processes. This is due in part to photobleaching and phototoxicity, which constrain imaging speed and duration. We developed an event-driven acquisition framework, in which neural-network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope. Our setup adapts acquisitions on-the-fly by switching between a slow imaging rate while detecting the onset of events, and a fast imaging rate during their progression. Thus, we capture mitochondrial and bacterial divisions at imaging rates that match their dynamic timescales, while extending overall imaging durations. Because event-driven acquisition allows the microscope to respond specifically to complex biological events, it acquires data enriched in relevant content.

In vivo super-resolution of the brain - How to visualize the hidden nanoplasticity?

Super-resolution fluorescence microscopy has entered most biological laboratories worldwide and its benefit is undisputable. Its application to brain imaging, for example in living mice, enables the study of sub-cellular structural plasticity and brain function directly in a living mammal. The demands of brain imaging on the different super-resolution microscopy techniques (STED, RESOLFT, SIM, ISM) and labeling strategies are discussed here as well as the challenges of the required cranial window preparation. Applications of super-resolution in the anesthetized mouse brain enlighten the stability and plasticity of synaptic nanostructures. These studies show the potential of in vivo super-resolution imaging and justify its application more widely in vivo to investigate the role of nanostructures in memory and learning.

Nanotopography modulates cytoskeletal organization and dynamics during T cell activation

Exposure to MHC-antigen complexes on the surface of antigen-presenting cells (APCs) activates T cells, inducing the formation of the immune synapse (IS). Antigen detection at the APC surface is thus a critical step in the adaptive immune response. The physical properties of antigen-presenting surfaces encountered by T cells in vivo are believed to modulate T cell activation and proliferation. Although stiffness and ligand mobility influence IS formation, the effect of the complex topography of the APC surface on this process is not well understood. Here we investigate how nanotopography modulates cytoskeletal dynamics and signaling during the early stages of T cell activation using high-resolution fluorescence microscopy on nanofabricated surfaces with parallel nanoridges of different spacings. We find that although nanoridges reduce the maximum spread area as compared with cells on flat surfaces, the ridges enhance the accumulation of actin and the signaling kinase ZAP-70 at the IS. Actin polymerization is more dynamic in the presence of ridges, which influence the directionality of both actin flows and microtubule (MT) growth. Our results demonstrate that the topography of the activating surface exerts both global effects on T cell morphology and local changes in actin and MT dynamics, collectively influencing T cell signaling.

Visualizing and quantifying molecular and cellular processes in C. elegans using light microscopy

Light microscopes are the cell and developmental biologists' "best friend", providing a means to see structures and follow dynamics from the protein to the organism level. A huge advantage of C. elegans as a model organism is its transparency, which coupled with its small size means that nearly every biological process can be observed and measured with the appropriate probe and light microscope. Continuous improvement in microscope technologies along with novel genome editing techniques to create transgenic probes have facilitated the development and implementation of a dizzying array of methods for imaging worm embryos, larvae and adults. In this review we provide an overview of the molecular and cellular processes that can be visualized in living worms using light microscopy. A partial inventory of fluorescent probes and techniques successfully used in worms to image the dynamics of cells, organelles, DNA, and protein localization and activity is followed by a practical guide to choosing between various imaging modalities, including widefield, confocal, lightsheet, and structured illumination microscopy. Finally, we discuss the available tools and approaches, including machine learning, for quantitative image analysis tasks, such as colocalization, segmentation, object tracking, and lineage tracing. Hopefully, this review will inspire worm researchers who have not yet imaged their worms to begin, and push those who are imaging to go faster, finer, and longer.

Paving the Way: Contributions of Big Data to Apicomplexan and Kinetoplastid Research

In the age of big data an important question is how to ensure we make the most out of the resources we generate. In this review, we discuss the major methods used in Apicomplexan and Kinetoplastid research to produce big datasets and advance our understanding of Plasmodium, Toxoplasma, Cryptosporidium, Trypanosoma and Leishmania biology. We debate the benefits and limitations of the current technologies, and propose future advancements that may be key to improving our use of these techniques. Finally, we consider the difficulties the field faces when trying to make the most of the abundance of data that has already been, and will continue to be, generated.

Deep-learning microscopy image reconstruction with quality control reveals second-scale rearrangements in RNA polymerase II clusters

Fluorescence microscopy, a central tool of biological research, is subject to inherent trade-offs in experiment design. For instance, image acquisition speed can only be increased in exchange for a lowered signal quality, or for an increased rate of photo-damage to the specimen. Computational denoising can recover some loss of signal, extending the trade-off margin for high-speed imaging. Recently proposed denoising on the basis of neural networks shows exceptional performance but raises concerns of errors typical of neural networks. Here, we present a work-flow that supports an empirically optimized reduction of exposure times, as well as per-image quality control to exclude images with reconstruction errors. We implement this work-flow on the basis of the denoising tool Noise2Void and assess the molecular state and 3D shape of RNA polymerase II (Pol II) clusters in live zebrafish embryos. Image acquisition speed could be tripled, achieving 2-s time resolution and 350-nm lateral image resolution. The obtained data reveal stereotyped events of approximately 10 s duration: initially, the molecular mark for recruited Pol II increases, then the mark for active Pol II increases, and finally Pol II clusters take on a stretched and unfolded shape. An independent analysis based on fixed sample images reproduces this sequence of events, and suggests that they are related to the transient association of genes with Pol II clusters. Our work-flow consists of procedures that can be implemented on commercial fluorescence microscopes without any hardware or software modification, and should, therefore, be transferable to many other applications.

Understanding immune signaling using advanced imaging techniques

Advanced imaging is key for visualizing the spatiotemporal regulation of immune signaling which is a complex process involving multiple players tightly regulated in space and time. Imaging techniques vary in their spatial resolution, spanning from nanometers to micrometers, and in their temporal resolution, ranging from microseconds to hours. In this review, we summarize state-of-the-art imaging methodologies and provide recent examples on how they helped to unravel the mysteries of immune signaling. Finally, we discuss the limitations of current technologies and share our insights on how to overcome these limitations to visualize immune signaling with unprecedented fidelity.

When Super-Resolution Localization Microscopy Meets Carbon Nanotubes

We recently assisted in a revolution in the realm of fluorescence microscopy triggered by the advent of super-resolution techniques that surpass the classic diffraction limit barrier. By providing optical images with nanometer resolution in the far field, super-resolution microscopy (SRM) is currently accelerating our understanding of the molecular organization of bio-specimens, bridging the gap between cellular observations and molecular structural knowledge, which was previously only accessible using electron microscopy. SRM mainly finds its roots in progress made in the control and manipulation of the optical properties of (single) fluorescent molecules. The flourishing development of novel fluorescent nanostructures has recently opened the possibility of associating super-resolution imaging strategies with nanomaterials’ design and applications. In this review article, we discuss some of the recent developments in the field of super-resolution imaging explicitly based on the use of nanomaterials. As an archetypal class of fluorescent nanomaterial, we mainly focus on single-walled carbon nanotubes (SWCNTs), which are photoluminescent emitters at near-infrared (NIR) wavelengths bearing great interest for biological imaging and for information optical transmission. Whether for fundamental applications in nanomaterial science or in biology, we show how super-resolution techniques can be applied to create nanoscale images “in”, “of” and “with” SWCNTs.

The HIV-1 Viral Protease Is Activated during Assembly and Budding Prior to Particle Release

HIV-1 encodes a viral protease that is essential for the maturation of infectious viral particles. While protease inhibitors are effective antiretroviral agents, recent studies have shown that prematurely activating, rather than inhibiting, protease function leads to the pyroptotic death of infected cells, with exciting implications for efforts to eradicate viral reservoirs. Despite 40 years of research into the kinetics of protease activation, it remains unclear exactly when protease becomes activated. Recent reports have estimated that protease activation occurs minutes to hours after viral release, suggesting that premature protease activation is challenging to induce efficiently. Here, monitoring viral protease activity with sensitive techniques, including nanoscale flow cytometry and instant structured illumination microscopy, we demonstrate that the viral protease is activated within cells prior to the release of free virions. Using genetic mutants that lock protease into a precursor conformation, we further show that both the precursor and mature protease have rapid activation kinetics and that the activity of the precursor protease is sufficient for viral fusion with target cells. Our finding that HIV-1 protease is activated within producer cells prior to release of free virions helps resolve a long-standing question of when protease is activated and suggests that only a modest acceleration of protease activation kinetics is required to induce potent and specific elimination of HIV-infected cells. IMPORTANCE HIV-1 protease inhibitors have been a mainstay of antiretroviral therapy for more than 2 decades. Although antiretroviral therapy is effective at controlling HIV-1 replication, persistent reservoirs of latently infected cells quickly reestablish replication if therapy is halted. A promising new strategy to eradicate the latent reservoir involves prematurely activating the viral protease, which leads to the pyroptotic killing of infected cells. Here, we use highly sensitive techniques to examine the kinetics of protease activation during and shortly after particle formation. We found that protease is fully activated before virus is released from the cell membrane, which is hours earlier than recent estimates. Our findings help resolve a long-standing debate as to when the viral protease is initially activated during viral assembly and confirm that prematurely activating HIV-1 protease is a viable strategy to eradicate infected cells following latency reversal.

Saturated-excitation image scanning microscopy

Image scanning microscopy (ISM) overcomes the trade-off between spatial resolution and signal volume in confocal microscopy by rearranging the signal distribution on a two-dimensional detector array to achieve a spatial resolution close to the theoretical limit achievable by infinitesimal pinhole detection without sacrificing the detected signal intensity. In this paper, we improved the spatial resolution of ISM in three dimensions by exploiting saturated excitation (SAX) of fluorescence. We theoretically investigated the imaging properties of ISM, when the fluorescence signals are nonlinearly induced by SAX, and show combined SAX-ISM fluorescence imaging to demonstrate the improvement of the spatial resolution in three dimensions. In addition, we confirmed that the SNR of SAX-ISM imaging of fluorescent beads and biological samples, which is one of the challenges in conventional SAX microscopy, was improved.

Answering some questions about structured illumination microscopy

Structured illumination microscopy (SIM) provides images of fluorescent objects at an enhanced resolution greater than that of conventional epifluorescence wide-field microscopy. Initially demonstrated in 1999 to enhance the lateral resolution twofold, it has since been extended to enhance axial resolution twofold (2008), applied to live-cell imaging (2009) and combined with myriad other techniques, including interferometric detection (2008), confocal microscopy (2010) and light sheet illumination (2012). Despite these impressive developments, SIM remains, perhaps, the most poorly understood 'super-resolution' method. In this article, we provide answers to the 13 questions regarding SIM proposed by Prakash et al. along with answers to a further three questions. After providing a general overview of the technique and its developments, we explain why SIM as normally used is still diffraction-limited. We then highlight the necessity for a non-polynomial, and not just nonlinear, response to the illuminating light in order to make SIM a true, diffraction-unlimited, super-resolution technique. In addition, we present a derivation of a real-space SIM reconstruction approach that can be used to process conventional SIM and image scanning microscopy (ISM) data and extended to process data with quasi-arbitrary illumination patterns. Finally, we provide a simple bibliometric analysis of SIM development over the past two decades and provide a short outlook on potential future work. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 2)'.

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)'.