Three-dimensional super-resolution imaging of live whole cells using galvanometer-based structured illumination microscopy

Optical sectioning methods in three-dimensional bioimaging

In recent advancements in life sciences, optical microscopy has played a crucial role in acquiring high-quality three-dimensional structural and functional information. However, the quality of 3D images is often compromised due to the intense scattering effect in biological tissues, compounded by several issues such as limited spatiotemporal resolution, low signal-to-noise ratio, inadequate depth of penetration, and high phototoxicity. Although various optical sectioning techniques have been developed to address these challenges, each method adheres to distinct imaging principles for specific applications. As a result, the effective selection of suitable optical sectioning techniques across diverse imaging scenarios has become crucial yet challenging. This paper comprehensively overviews existing optical sectioning techniques and selection guidance under different imaging scenarios. Specifically, we categorize the microscope design based on the spatial relationship between the illumination and detection axis, i.e., on-axis and off-axis. This classification provides a unique perspective to compare the implementation and performances of various optical sectioning approaches. Lastly, we integrate selected optical sectioning methods on a custom-built off-axis imaging system and present a unique perspective for the future development of optical sectioning techniques.

A one-step protocol to generate impermeable fluorescent HaloTag substrates for in situ live cell application and super-resolution imaging

Communication between cells is largely orchestrated by proteins on the cell surface, which allow information transfer across the cell membrane. Super-resolution and single-molecule visualization of these proteins can be achieved by genetically grafting HTP (HaloTag Protein) into the protein of interest followed by brief incubation of cells with a dye-HTL (dye-linked HaloTag Ligand). This approach allows for use of cutting-edge fluorophores optimized for specific optical techniques or a cell-impermeable dye-HTL to selectively label surface proteins without labeling intracellular copies. However, these two goals often conflict, as many high-performing dyes exhibit membrane permeability. Traditional methods to eliminate cell permeability face synthetic bottlenecks and risk altering photophysical properties. Here we report that dye-HTL reagents can be made cell-impermeable by inserting a charged sulfonate directly into the HTL, leaving the dye moiety unperturbed. This simple, one-step method requires no purification and is compatible with both the original HTL and second-generation HTL.2, the latter offering accelerated labeling. We validate such compounds, termed dye-SHTL ('dye shuttle') conjugates, in live cells via widefield microscopy, demonstrating exclusive membrane staining of extracellular HTP fusion proteins. In transduced primary hippocampal neurons, we label mGluR2, a neuromodulatory G protein-coupled receptor (GPCR), with dyes optimized for stimulated emission by depletion (STED) super-resolution microscopy, allowing unprecedented accuracy in distinguishing surface and receptors from those in internal compartments of the presynaptic terminal, important in neural communication. This approach offers broad utility for surface-specific protein labelling.

Ultra-high spatio-temporal resolution imaging with parallel acquisition-readout structured illumination microscopy (PAR-SIM)

Structured illumination microscopy (SIM) has emerged as a promising super-resolution fluorescence imaging technique, offering diverse configurations and computational strategies to mitigate phototoxicity during real-time imaging of biological specimens. Traditional efforts to enhance system frame rates have concentrated on processing algorithms, like rolling reconstruction or reduced frame reconstruction, or on investments in costly sCMOS cameras with accelerated row readout rates. In this article, we introduce an approach to elevate SIM frame rates and region of interest (ROI) coverage at the hardware level, without necessitating an upsurge in camera expenses or intricate algorithms. Here, parallel acquisition-readout SIM (PAR-SIM) achieves the highest imaging speed for fluorescence imaging at currently available detector sensitivity. By using the full frame-width of the detector through synchronizing the pattern generation and image exposure-readout process, we have achieved a fundamentally stupendous information spatial-temporal flux of 132.9 MPixels · s-1, 9.6-fold that of the latest techniques, with the lowest SNR of -2.11 dB and 100 nm resolution. PAR-SIM demonstrates its proficiency in successfully reconstructing diverse cellular organelles in dual excitations, even under conditions of low signal due to ultra-short exposure times. Notably, mitochondrial dynamic tubulation and ongoing membrane fusion processes have been captured in live COS-7 cell, recorded with PAR-SIM at an impressive 408 Hz. We posit that this novel parallel exposure-readout mode not only augments SIM pattern modulation for superior frame rates but also holds the potential to benefit other complex imaging systems with a strategic controlling approach.

Probing the dynamic crosstalk of lysosomes and mitochondria with structured illumination microscopy

Structured illumination microscopy (SIM) is a super-resolution technology for imaging living cells and has been used for studying the dynamics of lysosomes and mitochondria. Recently, new probes and analyzing methods have been developed for SIM imaging, enabling the quantitative analysis of these subcellular structures and their interactions. This review provides an overview of the working principle and advances of SIM, as well as the organelle-targeting principles and types of fluorescence probes, including small molecules, metal complexes, nanoparticles, and fluorescent proteins. Additionally, quantitative methods based on organelle morphology and distribution are outlined. Finally, the review provides an outlook on the current challenges and future directions for improving the combination of SIM imaging and image analysis to further advance the study of organelles. We hope that this review will be useful for researchers working in the field of organelle research and help to facilitate the development of SIM imaging and analysis techniques.

Super-Resolution Imaging of Neuronal Structures with Structured Illumination Microscopy

Super-resolution structured illumination microscopy (SR-SIM) is an optical fluorescence microscopy method which is suitable for imaging a wide variety of cells and tissues in biological and biomedical research. Typically, SIM methods use high spatial frequency illumination patterns generated by laser interference. This approach provides high resolution but is limited to thin samples such as cultured cells. Using a different strategy for processing raw data and coarser illumination patterns, we imaged through a 150-micrometer-thick coronal section of a mouse brain expressing GFP in a subset of neurons. The resolution reached 144 nm, an improvement of 1.7-fold beyond conventional widefield imaging.

Open-3DSIM: an open-source three-dimensional structured illumination microscopy reconstruction platform

Open-3DSIM is an open-source reconstruction platform for three-dimensional structured illumination microscopy. We demonstrate its superior performance for artifact suppression and high-fidelity reconstruction relative to other algorithms on various specimens and over a range of signal-to-noise levels. Open-3DSIM also offers the capacity to extract dipole orientation, paving a new avenue for interpreting subcellular structures in six dimensions (xyzθλt). The platform is available as MATLAB code, a Fiji plugin and an Exe application to maximize user-friendliness.

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.

Super-resolution imaging of neuronal structure with structured illumination microscopy

Super-resolution structured illumination microscopy (SR-SIM) is a method in optical fluorescence microscopy which is suitable for imaging a wide variety of cells and tissues in biological and biomedical research. Typically, SIM methods use high spatial frequency illumination patterns generated by laser interference. This approach provides high resolution but is limited to thin samples such as cultured cells. Using a different strategy for processing the raw data and coarser illumination patterns, we imaged through a 150 µm thick coronal section of a mouse brain expressing GFP in a subset of neurons. The resolution reached 144 nm, an improvement of 1.7 fold beyond conventional widefield imaging.

Flexible Multiplane Structured Illumination Microscope with a Four-Camera Detector

Fluorescence microscopy provides an unparalleled tool for imaging biological samples. However, producing high-quality volumetric images quickly and without excessive complexity remains a challenge. Here, we demonstrate a four-camera structured illumination microscope (SIM) capable of simultaneously imaging multiple focal planes, allowing for the capture of 3D fluorescent images without any axial movement of the sample. This setup allows for the acquisition of many different 3D imaging modes, including 3D time lapses, high-axial-resolution 3D images, and large 3D mosaics. We imaged mitochondrial motions in live cells, neuronal structure in Drosophila larvae, and imaged up to 130 µm deep into mouse brain tissue. After SIM processing, the resolution measured using one of the four cameras improved from 357 nm to 253 nm when using a 30×/1.05 NA objective.

Large-field lattice structured illumination microscopy

In this paper, we present large-field, five-step lattice structured illumination microscopy (Lattice SIM). This method utilizes a 2D grating for lattice projection and a spatial light modulator (SLM) for phase shifting. Five phase-shifted intensity images are recorded to reconstruct a super-resolution image, enhancing the imaging speed and reducing the photo-bleaching both by 17%, compared to conventional two-direction and three-shift SIM. Furthermore, lattice SIM has a three-fold spatial bandwidth product (SBP) enhancement compared to SLM/DMD-based SIM, of which the fringe number is limited by the SLM/DMD pixel number. We believe that the proposed technique will be further developed and widely applied in many fields.

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)'.

Design of thin-structured-light projection system for small-height measurement

A thin-structured light projection system with simple setup is proposed and applied to small-height measurement. In this system, the design of structured light pattern is carried out on the computer. The structure, color, geometric dimension, motion mode, projection brightness, contrast, and other attributes of the pattern can be edited easily. The optical system with zoom function is designed, which can output thin-structured-light fringe with a minimum width of 10 μm. The camera and microscope are used to construct a vision system, which is used to capture the pattern of structured light. The three-dimensional (3D) shape reconstruction is realized by analyzing the pattern of structured light. The results show that this system can project a small width of thin-structured light pattern, which is very suitable for 3D shape reconstruction of microscopic objects with the small-height of tens of microns to hundreds of microns.

Fast 3D super-resolution imaging using a digital micromirror device and binary holography

SIGNIFICANCE

High-speed three-dimensional (3D) super-resolution microscopy is a unique tool to investigate various biological phenomena; yet the technology is not broadly adopted due to its high cost and complex system design.

AIM

We present a compact, low-cost, and high-speed 3D structured illumination microscopy (SIM) based on a digital micromirror device and binary holography to visualize fast biological events with super-resolution.

APPROACH

The 3D SIM uses a digital micromirror device to generate three laser foci with individually controllable positions, phases, and amplitudes via binary holography at the back aperture of objective lens to form optimal 3D structured patterns. Fifteen raw images are sequentially recorded and processed by the 3D SIM algorithm to reconstruct a super-resolved image.

RESULTS

Super-resolution 3D imaging at a speed of 26.7 frames per second is achieved with a lateral and axial resolution of 155 and 487 nm, which corresponds to a 1.65- and 1.63-times resolution enhancement, respectively, comparing with standard deconvolution microscopy.

CONCLUSIONS

The 3D SIM realizes fast super-resolution imaging with optimal 3D structured illumination, which may find important applications in biophotonics.

ML-SIM: universal reconstruction of structured illumination microscopy images using transfer learning

Structured illumination microscopy (SIM) has become an important technique for optical super-resolution imaging because it allows a doubling of image resolution at speeds compatible with live-cell imaging. However, the reconstruction of SIM images is often slow, prone to artefacts, and requires multiple parameter adjustments to reflect different hardware or experimental conditions. Here, we introduce a versatile reconstruction method, ML-SIM, which makes use of transfer learning to obtain a parameter-free model that generalises beyond the task of reconstructing data recorded by a specific imaging system for a specific sample type. We demonstrate the generality of the model and the high quality of the obtained reconstructions by application of ML-SIM on raw data obtained for multiple sample types acquired on distinct SIM microscopes. ML-SIM is an end-to-end deep residual neural network that is trained on an auxiliary domain consisting of simulated images, but is transferable to the target task of reconstructing experimental SIM images. By generating the training data to reflect challenging imaging conditions encountered in real systems, ML-SIM becomes robust to noise and irregularities in the illumination patterns of the raw SIM input frames. Since ML-SIM does not require the acquisition of experimental training data, the method can be efficiently adapted to any specific experimental SIM implementation. We compare the reconstruction quality enabled by ML-SIM with current state-of-the-art SIM reconstruction methods and demonstrate advantages in terms of generality and robustness to noise for both simulated and experimental inputs, thus making ML-SIM a useful alternative to traditional methods for challenging imaging conditions. Additionally, reconstruction of a SIM stack is accomplished in less than 200 ms on a modern graphics processing unit, enabling future applications for real-time imaging. Source code and ready-to-use software for the method are available at http://ML-SIM.github.io.

Light Microscopy of Mitochondria at the Nanoscale

Mitochondria are essential for eukaryotic life. These double-membrane organelles often form highly dynamic tubular networks interacting with many cellular structures. Their highly convoluted contiguous inner membrane compartmentalizes the organelle, which is crucial for mitochondrial function. Since the diameter of the mitochondrial tubules is generally close to the diffraction limit of light microscopy, it is often challenging, if not impossible, to visualize submitochondrial structures or protein distributions using conventional light microscopy. This renders super-resolution microscopy particularly valuable, and attractive, for studying mitochondria. Super-resolution microscopy encompasses a diverse set of approaches that extend resolution, as well as nanoscopy techniques that can even overcome the diffraction limit. In this review, we provide an overview of recent studies using super-resolution microscopy to investigate mitochondria, discuss the strengths and opportunities of the various methods in addressing specific questions in mitochondrial biology, and highlight potential future developments.