Structured Illumination Microscopy Images Research Articles

Detection of α-Synuclein in Oral Mucosa by Seed Amplification Assay in Synucleinopathies and Isolated REM Sleep Behavior Disorder.

Evidence of abnormal α-synuclein (α-Syn) deposition in the brain is required for definitive diagnosis of synucleinopathies, which remains challenging. The seed amplification assay (SAA) is an innovative technique that can detect the seeding activity of misfolded α-Syn, enabling the amplification and detection of minute quantities of pathogenic α-Syn aggregates. This study aimed to evaluate oral mucosa α-Syn SAA as possible diagnostic and prodromal biomarkers for synucleinopathies. A total of 107 Parkinson's disease (PD) patients, 99 multiple system atrophy (MSA) patients, 33 patients with isolated rapid eye movement sleep behavior disorder (iRBD) and 103 healthy controls (HC) were included. The SAA was applied to detect the seeding activity of α-Syn from oral mucosa. A combination of morphological, biochemical, and biophysical methods was also used to analyze the fibrils generated from the oral mucosa α-Syn SAA. Structured illumination microscopy images revealed the increased α-Syn species in oral mucosa of PD, MSA, and iRBD patients than in HCs. Oral mucosa α-Syn SAA distinguished patients with PD from HC with 67.3% sensitivity and 90.3% specificity. Oral mucosa was α-Syn SAA positive in 53.5% MSA patients and 63.6% iRBD patients. Furthermore, the α-Syn fibrils generated from MSA demonstrated greater resistance to proteinase K digestion and exhibited stronger cytotoxicity compared to those from PD patients. Oral mucosa α-Syn seeding activity may serve as novel non-invasive diagnostic and prodromal biomarkers for synucleinopathies. The α-Syn aggregates amplified from the oral mucosa of PD and MSA exhibited distinct biochemical and biophysical properties. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging

Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a large amount of high-quality training data severely limits their applications. Here we developed a pixel-realignment-based self-supervised denoising framework for SIM (PRS-SIM) that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction artifacts. We demonstrated that PRS-SIM generates artifact-free images with 20-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth (GT). Moreover, we developed an easy-to-use plugin that enables both training and implementation of PRS-SIM for multimodal SIM platforms including 2D/3D and linear/nonlinear SIM. With PRS-SIM, we achieved long-term super-resolution live-cell imaging of various vulnerable bioprocesses, revealing the clustered distribution of Clathrin-coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.

Improving Reconstruction of Structured Illumination Microscopy Images via Dual-Domain Learning

Structured illumination microscopy (SIM) provides an enhanced resolving power surpassing the optical diffraction limit by optical modulation of patterned illuminations. Although end-to-end deep learning techniques have recently advanced the reconstruction of SIM images, the reconstruction fidelity of existing networks is still moderate. We experimentally point out the crux lies in the inability of these models for faithful frequency learning. As a remedy, we propose a dual-domain learning strategy for SIM reconstruction, namely DDL-SIM, which learns to reconstruct SIM images from raw images in the spatial domain and raw image spectra in the frequency domain simultaneously, with the goal of narrowing the reconstruction gaps in both domains, thereby better recovering modulated frequencies and resolving more fine structures. Reconstruction experiments across various biological structures demonstrate the proposed DDL-SIM significantly improves the reconstruction fidelity of SIM images and shows great robustness against reconstruction artifacts.

Corrections to “Structured Illumination Microscopy Image Reconstruction Algorithm”

Corrections to “Structured Illumination Microscopy Image Reconstruction Algorithm”

Enhancing structural illumination microscopy with hybrid CNN-transformer and dynamic frequency loss

Deep learning techniques have augmented structured illumination microscopy (SIM) imaging by employing convolutional neural networks (CNNs) for the super-resolution reconstruction of SIM images. However, due to the intrinsic shortcomings of CNNs, i.e. limited receptive field and inadaptability to input content, their reconstruction faithfulness and imaging resolution are limited. Recently, another class of neural backbone, Transformer, has shown complementary advantages to CNNs. To this end, this work aims to borrow the recent Transformers to enhance CNN-based SIM reconstruction, thereby fully unleashing the power of neural networks for SIM imaging. Concretely, we first experimentally explore the pros and cons of CNNs and Transformers for SIM reconstruction. Then, we propose a Hybrid CNN-Transformer (HCT) model, to inherit merits of the both. To further encourage the recovery of high frequencies down-modulated by patterned illumination, we also devise a dynamic frequency loss based on the phase spectrum information, helping models focus on hard frequencies adaptively. Experiments on diverse subcellular structures demonstrate that the proposed method can evidently boost SIM reconstruction performance, even under low-light conditions or with fewer raw frames.

Regularized deconvolution for structured illumination microscopy via accelerated linearized ADMM

Due to the ill-posedness of the inverse deconvolution for structured illumination microscopy (SIM), the results of Richardson–Lucy algorithm are not ideal in the presence of noise. Here, we propose an accelerated linearized alternating direction method of multipliers (AL-ADMM) method for solving the regularized SIM deconvolution problem. A modification of the generalized inverse is introduced to overcome the large condition number of the convolution operator. This study shows that regularization or priori knowledge can effectively suppress noise and improve the resolution and contrast of the recovered SIM image. Simulations and experiments demonstrate that the proposed algorithm can efficiently extract higher-frequency information beyond the microscope optical transfer function for the corrupted SIM images to achieve computational super-resolution (SR) without hardware modifications.

A novel multi-frame wavelet generative adversarial network for scattering reconstruction of structured illumination microscopy.

Structured illumination microscopy (SIM) is widely used in various fields of life science research. In clinical practice, it has low phototoxicity, fast imaging speed and no special fluorescent markers. However, SIM is still affected by the scattering medium of biological tissues, resulting in insufficient resolution of the obtained images, which limits the development of life sciences. A novel multi-frame wavelet generation adversarial network (MWGAN) is proposed to improve the scattering reconstruction capability of SIM.
Approach: MWGAN is based on two components derived from the original image. A generative adversarial network constructed by wavelet transform is trained to reconstruct some complex details in the cell structure. Multi-frame adversarial network is used to obtain the inter-frame information of the image and use the complementary information of the before and after frames to improve the quality of the model reconstruction.
Results: To demonstrate the robustness of MWGAN, multiple low-quality SIM image datasets are tested. Compared with the state-of-the-art methods, the proposed method achieves superior performance in both of the subjective and objective evaluation.
Conclusion: MWGAN is effective for improving the clarity of SIM images. Meanwhile, the SIM images reconstructed by multiple frames improve the reconstruction quality of complex regions and allow clearer and dynamic observation of cellular functions.

260 Breast Cancer Biopsy Triage with Ex-Vivo Microscopy for Downstream Analysis

OBJECTIVES/GOALS: In this study, the ability of a pathologist to detect malignancy on digital pseudo-H&E slides obtained via structured illumination microscopy (SIM) imaging of fresh diagnostic breast biopsies was assessed. The speed of imaging and processing was also assessed for potential clinical implementation. METHODS/STUDY POPULATION: This study was conducted in accordance with an Ochsner Medical Center of New Orleans IRB. 200 patients undergoing either stereotactic or ultrasound-guided diagnostic breast biopsies were consented and an additional core from the suspicious lesion was collected for research use. Research biopsies were transported to the lab and stained with DRAQ5 and Eosin-Y and imaged with SIM before being submitted for histology processing. Imaging and digital processing times were recorded. The resulting SIM images and histology slides were given to a pathologist for blind review to assess accuracy. RESULTS/ANTICIPATED RESULTS: The ex-vivo structured illumination microscopy images and subsequent histology slides from 79 research cores have been assessed to date. Some samples were excluded from the total data set and not included in the final assessment due to technical failures of the imaging protocol. Of the current set, the pathologist has a specificity of 88% and a sensitivity of 65%, as well as an NPV of 88% and a PPV of 65%. Staining time for each biopsy was completed within 3 and a half minutes and imaging at 20x magnification took between 4 and 12 minutes, depending on size and implementation of autofocus to the imaging system. Image processing took approximately 5 minutes per biopsy and is a direct function of biopsy size. DISCUSSION/SIGNIFICANCE: Decreased time between cancer suspicion and treatment will improve the prognosis of breast cancer patients. SIM imaging of fresh breast biopsies could ultimately allow primary and secondary histology to be performed simultaneously and minimize histopathology time, thus allowing clinicians and patients to implement treatment course more quickly.

Construction of a One-Dimensional Al-Rich ZSM-48 Zeolite with a Hollow Structure.

Diffusion limitation and acid deficiency are two main challenges that the ZSM-48 zeolite faces in practical application. To date, there have been few effective strategies to solve both problems, simultaneously. Also, it is also a challenge to construct a hollow structure in a one-dimensional (1D) zeolite. Herein, an Al-rich ZSM-48 zeolite with a hollow structure is constructed through an alumination-recrystallization strategy, thereby solving the problems related to diffusion and acidity simultaneously. The hollowness and enrichment of aluminum can be controlled by judiciously matching the desilication and recrystallization. The silica to alumina ratio (SAR) of the ZSM-48 zeolite can be tuned from 130 to 45, which breaks the SAR limitation of conventional synthesis. On the basis of the different characterization results, the whole crystallization can be divided into two stages: rapid desilication-alumination and time-consuming recrystallization. In the selective desilication-recrystallization process, the preferential special distribution of the organic template leads to the formation of a hollow structure and the healing of mesopores at the outer shell, as evidenced by structured illumination microscopy images. Due to the enhancement in diffusion ability and acid density, the obtained hollow Al-rich ZSM-48 zeolite exhibits excellent catalytic stability and high p-xylene yield (∼26%) in the m-xylene isomerization reaction (WHSV = 18 h-1), indicating its strong industrial application potential.

Promotion Effect of Carboxymethyl Chitosan on Dental Caries via Intrafibrillar Mineralization of Collagen and Dentin Remineralization.

Objective: To observe ultrastructural changes during the process of carboxymethyl chitosan (CMC)-mediated intrafibrillar mineralization, we evaluated the biomimetic remineralization potential of CMC in type-I collagen fibrils and membranes, and further explored the bond strength as well as the bond interfacial integrity of the biomimetic remineralized artificial caries-affected dentin (ACAD). Methods: A mineralized solution containing 200 μg/mL CMC was used to induce type-I collagen biomimetic remineralization in ACAD, while traditional mineralization without CMC was used as a control. The process and pattern of mineralization were investigated by transmission electron microscopy (TEM), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) as well as structured illumination microscopy (SIM). The Vickers hardness test was used to quantify the dentin hardness, while the microtensile bond strength (µTBS) test was used to assess the bond strength and durability. The bond interfacial integrity was evaluated by a confocal laser scanning microscope (CLSM). Results: TEM, SEM, and SIM images showed that CMC had a positive effect on stabilizing amorphous calcium phosphate (ACP) and promoting intrafibrillar mineralization, while extrafibrillar mineralization was formed without CMC. Furthermore, hardness evaluation and µTBS proved that CMC significantly increased dentin hardness and bond strength. CLSM indicated that CMC could create a significantly better bond interfacial integrity with less of a micro-gap in ACAD. Significance: CMC possessed the ability to promote intrafibrillar mineralization and remineralization in demineralized caries dentin lesions, as well as improve bond performance, which implied its potential in carious dentin demineralization or dentin hypersensitivity and possibly even as a possible material for indirect pulp-capping, to deal with deep caries. Highlights: CMC possessed the ability to induce intrafibrillar mineralization effectively; the bond strength and bond durability of demineralized caries dentin were improved via CMC-induced remineralization; the CMC-induced remineralization complex is a potential material for indirect pulp-capping, to deal with deep caries.

Targeting Gut Bacteria Using Inulin‐Conjugated Mesoporous Silica Nanoparticles (Adv. Mater. Interfaces 14/2022)

Inulin-Conjugated Mesoporous Silica Nanoparticles In article number 2102558, Cornelia von Baeckmann, David Berry, Freddy Kleitz, and co-workers demonstrate the possible application of inulin-conjugated mesoporous silica nanoparticles (pink) for targeting the human gut bacteria (blue). Accumulation of the bacteria on the nanoparticles are highlighted, paving the way for a system to actively influence the bacteria composition in the microbiome once it is loaded with anti-microbials. TEM of the conjugate and a structured illumination microscopy image are shown.

SIM reconstruction framework for high-speed multi-dimensional super-resolution imaging.

Structured illumination microscopy (SIM) holds great promise for live cell imaging applications due to its potential to obtain multidimensional information such as intensity, spectrum and polarization (I, λ , p) at high spatial-temporal resolution, enabling the observation of more complex dynamic interactions between subcellular structures. However, the reconstruction results of polarized samples are prone to artifacts because all current SIM reconstruction frameworks use incomplete imaging models which neglect polarization modulation. Such polarization-related artifacts are especially prevalent for SIM reconstruction using a reduced number of raw images (RSIM) and severely undermine the ability of SIM to capture multi-dimensional information. Here, we report a new SIM reconstruction framework (PRSIM) that can recover multi-dimensional information (I, λ, p) using a reduced number of raw images. PRSIM adopts a complete imaging model that is versatile for normal and polarized samples and uses a frequency-domain iterative reconstruction algorithm for artifact-free super-resolution (SR) reconstruction. It can simultaneously obtain the SR spatial structure and polarization orientation of polarized samples using 6 raw SIM images and can perform SR reconstruction using 4 SIM images for normal samples. In addition, PRSIM has less spatial computational complexity and achieves reconstruction speeds tens of times higher than that of the state-of-the-art non-iterative RSIM, making it more suitable for large field-of-view imaging. Thus, PRSIM is expected to facilitate the development of SIM into an ultra-high-speed and multi-dimensional SR imaging tool.

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

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

Deep learning 2D and 3D optical sectioning microscopy using cross-modality Pix2Pix cGAN image translation.

Structured illumination microscopy (SIM) reconstructs optically-sectioned images of a sample from multiple spatially-patterned wide-field images, but the traditional single non-patterned wide-field images are more inexpensively obtained since they do not require generation of specialized illumination patterns. In this work, we translated wide-field fluorescence microscopy images to optically-sectioned SIM images by a Pix2Pix conditional generative adversarial network (cGAN). Our model shows the capability of both 2D cross-modality image translation from wide-field images to optical sections, and further demonstrates potential to recover 3D optically-sectioned volumes from wide-field image stacks. The utility of the model was tested on a variety of samples including fluorescent beads and fresh human tissue samples.

Organic Hyperbolic Material Assisted Illumination Nanoscopy.

Resolution capability of the linear structured illumination microscopy (SIM) plays a key role in its applications in physics, medicine, biology, and life science. Many advanced methodologies have been developed to extend the resolution of structured illumination by using subdiffraction‐limited optical excitation patterns. However, obtaining SIM images with a resolution beyond 40 nm at visible frequency remains as an insurmountable obstacle due to the intrinsic limitation of spatial frequency bandwidth of the involved materials and the complexity of the illumination system. Here, a low‐loss natural organic hyperbolic material (OHM) that can support record high spatial‐frequency modes beyond 50k 0, i.e., effective refractive index larger than 50, at visible frequencies is reported. OHM‐based speckle structured illumination microscopy demonstrates imaging resolution at 30 nm scales with enhanced fluorophore photostability, biocompatibility, easy to use and low cost. This study will open up a new route in super‐resolution microscopy by utilizing OHM films for various applications including bioimaging and sensing.

Caenorhabditis elegans DSB-3 reveals conservation and divergence among protein complexes promoting meiotic double-strand breaks

Meiotic recombination plays dual roles in the evolution and stable inheritance of genomes: Recombination promotes genetic diversity by reassorting variants, and it establishes temporary connections between pairs of homologous chromosomes that ensure their future segregation. Meiotic recombination is initiated by generation of double-strand DNA breaks (DSBs) by the conserved topoisomerase-like protein Spo11. Despite strong conservation of Spo11 across eukaryotic kingdoms, auxiliary complexes that interact with Spo11 complexes to promote DSB formation are poorly conserved. Here, we identify DSB-3 as a DSB-promoting protein in the nematode Caenorhabditis elegans Mutants lacking DSB-3 are proficient for homolog pairing and synapsis but fail to form crossovers. Lack of crossovers in dsb-3 mutants reflects a requirement for DSB-3 in meiotic DSB formation. DSB-3 concentrates in meiotic nuclei with timing similar to DSB-1 and DSB-2 (predicted homologs of yeast/mammalian Rec114/REC114), and DSB-1, DSB-2, and DSB-3 are interdependent for this localization. Bioinformatics analysis and interactions among the DSB proteins support the identity of DSB-3 as a homolog of MEI4 in conserved DSB-promoting complexes. This identification is reinforced by colocalization of pairwise combinations of DSB-1, DSB-2, and DSB-3 foci in structured illumination microscopy images of spread nuclei. However, unlike yeast Rec114, DSB-1 can interact directly with SPO-11, and in contrast to mouse REC114 and MEI4, DSB-1, DSB-2, and DSB-3 are not concentrated predominantly at meiotic chromosome axes. We speculate that variations in the meiotic program that have coevolved with distinct reproductive strategies in diverse organisms may contribute to and/or enable diversification of essential components of the meiotic machinery.

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.

Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images

Super-resolution structured illumination microscopy (SR-SIM) provides an up to twofold enhanced spatial resolution of fluorescently labeled samples. The reconstruction of high-quality SR-SIM images critically depends on patterned illumination with high modulation contrast. Noisy raw image data (e.g., as a result of low excitation power or low exposure time), result in reconstruction artifacts. Here, we demonstrate deep-learning based SR-SIM image denoising that results in high-quality reconstructed images. A residual encoding–decoding convolutional neural network (RED-Net) was used to successfully denoise computationally reconstructed noisy SR-SIM images. We also demonstrate the end-to-end deep-learning based denoising and reconstruction of raw SIM images into high-resolution SR-SIM images. Both image reconstruction methods prove to be very robust against image reconstruction artifacts and generalize very well across various noise levels. The combination of computational image reconstruction and subsequent denoising via RED-Net shows very robust performance during inference after training even if the microscope settings change.

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

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

Measuring Nanoscale Distances by Structured Illumination Microscopy and Image Cross-Correlation Spectroscopy (SIM-ICCS).

Since the introduction of super-resolution microscopy, there has been growing interest in quantifying the nanoscale spatial distributions of fluorescent probes to better understand cellular processes and their interactions. One way to check if distributions are correlated or not is to perform colocalization analysis of multi-color acquisitions. Among all the possible methods available to study and quantify the colocalization between multicolor images, there is image cross-correlation spectroscopy (ICCS). The main advantage of ICCS, in comparison with other co-localization techniques, is that it does not require pre-segmentation of the sample into single objects. Here we show that the combination of structured illumination microscopy (SIM) with ICCS (SIM-ICCS) is a simple approach to quantify colocalization and measure nanoscale distances from multi-color SIM images. We validate the SIM-ICCS analysis on SIM images of optical nanorulers, DNA-origami-based model samples containing fluorophores of different colors at a distance of 80 nm. The SIM-ICCS analysis is compared with an object-based analysis performed on the same samples. Finally, we show that SIM-ICCS can be used to quantify the nanoscale spatial distribution of functional nuclear sites in fixed cells.