Fluorescence Microscopy Images Research Articles

Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy

Fluorescence microscopic imaging is essentially a convolution process distorted by random noise, limiting critical parameters such as imaging speed, duration, and resolution. Though algorithmic compensation has shown great potential to enhance these pivotal aspects, its fidelity remains questioned. Here we develop a physics-rooted computational resolution extension and denoising method with ensured fidelity. Our approach employs a multi-resolution analysis (MRA) framework to extract the two main characteristics of fluorescence images against noise: across-edge contrast, and along-edge continuity. By constraining the two features in a model-solution framework using framelet and curvelet, we develop MRA deconvolution algorithms, which improve the signal-to-noise ratio (SNR) up to 10 dB higher than spatial derivative based penalties, and can provide up to two-fold fidelity-ensured resolution improvement rather than the artifact-prone Richardson-Lucy inference. We demonstrate our methods can improve the performance of various diffraction-limited and super-resolution microscopies with ensured fidelity, enabling accomplishments of more challenging imaging tasks.

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.

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Background and ObjectivesTechniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. MethodsTo construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered optical coherence microscopy (OCM) and confocal fluorescence microscopy (CFM) images. ResultsComparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95% confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95% CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images. ConclusionsThe cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

A Saturation Artifacts Inpainting Method Based on Two-Stage GAN for Fluorescence Microscope Images.

Fluorescence microscopic images of cells contain a large number of morphological features that are used as an unbiased source of quantitative information about cell status, through which researchers can extract quantitative information about cells and study the biological phenomena of cells through statistical and analytical analysis. As an important research object of phenotypic analysis, images have a great influence on the research results. Saturation artifacts present in the image result in a loss of grayscale information that does not reveal the true value of fluorescence intensity. From the perspective of data post-processing, we propose a two-stage cell image recovery model based on a generative adversarial network to solve the problem of phenotypic feature loss caused by saturation artifacts. The model is capable of restoring large areas of missing phenotypic features. In the experiment, we adopt the strategy of progressive restoration to improve the robustness of the training effect and add the contextual attention structure to enhance the stability of the restoration effect. We hope to use deep learning methods to mitigate the effects of saturation artifacts to reveal how chemical, genetic, and environmental factors affect cell state, providing an effective tool for studying the field of biological variability and improving image quality in analysis.

Clustering Methods for the Characterization of Synchrotron Radiation X‐Ray Fluorescence Images of Human Carotid Atherosclerotic Plaque

This study employs computational algorithms to automatically identify and classify features in X‐Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X‐Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X‐Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for manual labeling. However, they can be accurately classified in an automated fashion using GM. Prior to clustering, PCA is used to deal with repeated patterns and background areas. The resulting clusters are associated with different types of features, which can be identified as specific tissues confirmed by histology. Regions of high concentrations of phosphorus, sulfur, calcium, and iron are found in the samples. These regions are also observed in the STXM results as spots of low transmission that typically are associated with calcium deposits only.

3D printing assisted surface patterning process on acrylated hydrogels for contact guidance of fibroblasts

Generating stable and customizable topography on hydrogel surfaces with contact guidance potential is critical as it can direct/influence cell growth. This necessitates the development of new techniques for surface patterning of the hydrogels. We report on the design of a square grid template for surface patterning hydrogels. The template was 3-D printed and has the diameter of a well in a 24-well plate. Hyaluronic acid methacrylate (HA) hydrogel precursor solutions were cast on the 3D printed template's surface, which generated 3D square shape topographies on the HA hydrogel surface upon demolding. The 3D Laser Microscopy has shown the formation of a periodic array of 3D topographies on hydrogel surfaces. 3D Laser and Electron Microscopy Imaging have revealed that this new method has increased the surface area and exposed the underlying pore structure of the HA hydrogels. To demonstrate the method's versatility, we have successfully applied this technique to generate 3D topography on two more acrylate hydrogel formulations, gelatin Methacrylate and polyethylene glycol dimethacrylate. Human neonatal dermal fibroblast cells were used as a model cell line to evaluate the cell guidance potential of patterned HA hydrogel. Confocal fluorescence microscopy imaging has revealed that the 3D surface topographies on HA hydrogels can guide and align the actin filaments of the fibroblasts presumably due to the contact guidance mechanism. The newly developed methodology of 3D topography generation in acrylate hydrogels may influence the cell responses on hydrogel surfaces which can impact biomedical applications such as tissue engineering, wound healing, and disease modeling.

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

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

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds.

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

A generalized deep neural network approach for improving resolution of fluorescence microscopy images

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed, imaging resolution, and imaging flux. This paper proposes a deep neural network based on a generative adversarial network (GAN). The generator employs a U-Net-based network, which integrates DenseNet for the downsampling component. The proposed method has excellent properties, for example, the network model is trained with several different datasets of biological structures; the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging; and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets. In addition, experimental results showed that the method improved the resolution of caveolin-coated pits (CCPs) structures from 264[Formula: see text]nm to 138[Formula: see text]nm, a 1.91-fold increase, and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

Quantifying superimposed protein flow dynamics in live cells using spatial filtering and spatiotemporal image correlation spectroscopy.

Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin-based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton.

In vivo imaging of nickel-rich laticifers: A breakthrough in metal hyperaccumulation

The discovery of the nickel-rich latex of the New Caledonian endemic tree Pycnandra acuminata introduced the term ‘hyperaccumulator’ and gave rise to a new field of research. This then instigated a global quest for these unusual hyperaccumulator plants, even while the underlying mechanisms of nickel acquisition, transport, and internal elemental distribution remained unknown for this original laticifer-bearing hyperaccumulator plant. Here we reveal for the first time the distribution of nickel-filled laticifers in the different plant organs of P. acuminata. The pressurised nickel laticifers were imaged multimodally with a combination of synchrotron X-ray fluorescence (XRF) microscopy, microtomography (XRF-μCT) and synchrotron X-ray phase contrast imaging microtomography (PCI-μCT). These advanced synchrotron methodologies allowed for complimentary non-invasive reconstructions of an in-situ model of the laticiferous system in this species. The data shows the distribution of the nickel-rich laticifers within whole plant tissues from roots to apical tip, thus suggesting nickel trafficking in the laticifer network. The extraordinary concentration of nickel within P. acuminata laticifers functions as an effective natural tracer for XRF-μCT and PCI-μCT to probe the structure and organization of these cells, thereby permitting insights into the development and physiological functioning of this unique duct system.

Regulated anthocyanin release through novel pH-responsive peptide hydrogels in simulated digestive environment

The instability of anthocyanins significantly reduces their bioavailability as food nutrients. This proof-of-concept study aimed to develop efficient carriers for anthocyanins to overcome this challenge. Characterization of the hydrogels via SEM (scanning electron microscope) and rheological analysis revealed the formation of typical gel structures. MTT (methyl thiazolyl tetrazolium) and hemolysis assays confirmed that their high biocompatibility. Encapsulation efficiency analysis and fluorescence microscopy images demonstrated successful and efficient encapsulation of anthocyanins by pH-responsive hydrogels. Stability studies further validated the effect of peptide hydrogels in helping anthocyanin molecules withstand factors such as gastric acid, high temperatures, and heavy metals. Subsequently, responsive studies in simulated gastric (intestinal) fluid demonstrated that the pH-responsive peptide hydrogels could protect anthocyanin molecules from gastric acid while achieving rapid and complete release in intestinal fluid environments. These results indicate that these peptide hydrogels could stabilize anthocyanins and facilitate their controlled release, potentially leading to personalized delivery systems.

Nanoscale architecture of synaptic vesicles and scaffolding complexes revealed by cryo-electron tomography

The spatial distribution of proteins and their arrangement within the cellular ultrastructure regulates the opening of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in response to glutamate release at the synapse. Fluorescence microscopy imaging revealed that the postsynaptic density (PSD) and scaffolding proteins in the presynaptic active zone (AZ) align across the synapse to form a trans-synaptic "nanocolumn," but the relation to synaptic vesicle release sites is uncertain. Here, we employ focused-ion beam (FIB) milling and cryoelectron tomography to image synapses under near-native conditions. Improved image contrast, enabled by FIB milling, allows simultaneous visualization of supramolecular nanoclusters within the AZ and PSD and synaptic vesicles. Surprisingly, membrane-proximal synaptic vesicles, which fuse to release glutamate, are not preferentially aligned with AZ or PSD nanoclusters. These synaptic vesicles are linked to the membrane by peripheral protein densities, often consistent in size and shape with Munc13, as well as globular densities bridging the synaptic vesicle and plasma membrane, consistent with prefusion complexes of SNAREs, synaptotagmins, and complexin. Monte Carlo simulations of synaptic transmission events using biorealistic models guided by our tomograms predict that clustering AMPARs within PSD nanoclusters increases the variability of the postsynaptic response but not its average amplitude. Together, our data support a model in which synaptic strength is tuned at the level of single vesicles by the spatial relationship between scaffolding nanoclusters and single synaptic vesicle fusion sites.

Revisiting the standards of cancer detection and therapy alongside their comparison to modern methods.

In accordance with the World Health Organization data, cancer remains at the forefront of fatal diseases. An upward trend in cancer incidence and mortality has been observed globally, emphasizing that efforts in developing detection and treatment methods should continue. The diagnostic path typically begins with learning the medical history of a patient; this is followed by basic blood tests and imaging tests to indicate where cancer may be located to schedule a needle biopsy. Prompt initiation of diagnosis is crucial since delayed cancer detection entails higher costs of treatment and hospitalization. Thus, there is a need for novel cancer detection methods such as liquid biopsy, elastography, synthetic biosensors, fluorescence imaging, and reflectance confocal microscopy. Conventional therapeutic methods, although still common in clinical practice, pose many limitations and are unsatisfactory. Nowadays, there is a dynamic advancement of clinical research and the development of more precise and effective methods such as oncolytic virotherapy, exosome-based therapy, nanotechnology, dendritic cells, chimeric antigen receptors, immune checkpoint inhibitors, natural product-based therapy, tumor-treating fields, and photodynamic therapy. The present paper compares available data on conventional and modern methods of cancer detection and therapy to facilitate an understanding of this rapidly advancing field and its future directions. As evidenced, modern methods are not without drawbacks; there is still a need to develop new detection strategies and therapeutic approaches to improve sensitivity, specificity, safety, and efficacy. Nevertheless, an appropriate route has been taken, as confirmed by the approval of some modern methods by the Food and Drug Administration.

Echogenic Gold Nanorod Incorporated Hybrid Poly(2-oxazoline) Nanocapsules for Real-Time Ultrasound/Fluorescent Imaging and Targeted Cancer Theranostics.

In recent years, various nanocarrier systems have been explored to enhance the targeting of cancer cells by improving the ligand-receptor interactions between the nanocarrier and cancer cells for selective cancer cell imaging and targeted delivery of anticancer drugs. Herein, we report multifunctional hydrogen-bonded multilayer nanocapsules functionalized with both folic acid-derived quantum dots (FAQDs) and gold nanorods (AuNRs) for targeted cancer therapy and cancer cell imaging using fluorescence microscopy and medical-range ultrasound imaging systems. The encapsulation efficiency of nanocapsules was found to be 49% for 5-fluorouracil (5-FU). The release percentage reached a plateau at 37% after 1 h at pH 7.4 and increased to 57% after 3 h when the release pH was decreased to pH 5.5 (i.e., the pH of the tumor environment). Under ultrasound irradiation, the release was significantly accelerated, with a total release of 52% and 68% after only 6 min at pH 7.4 and pH 5.5, respectively. While the sonoporation process plays an important role in anticancer activity experiments under ultrasound exposure by generating temporary pores, the targeting ability of FAQDs brings the capsules closer to the cell membrane and improves the cellular uptake of the released drug, thereby increasing local drug concentration. In vitro cytotoxicity experiments with HCT-116 and HEp-2 cells demonstrated anticancer activities of 96% and 98%, respectively. The nanocapsules showed enhanced ultrasound scattering signal intensity and bright spots under ultrasound exposure, most likely caused by high scattering ability and internal reflections of preloaded AuNRs in the interior structure of the nanocapsules. Hence, the demonstrated nanocapsule system not only has the potential to be used as an integrated system for early- stage detection and treatment of cancer cells but also has the ability for live tracking and imaging of cancer cells while undergoing treatment with chemotherapy and radiation therapy.

Zero-shot denoising of microscopy images recorded at high-resolution limits.

Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation.

First-in-man study of the PSMA Minibody IR800-IAB2M for molecularly targeted intraoperative fluorescence guidance during radical prostatectomy

PurposeProstate-specific membrane antigen (PSMA) is increasingly used to image prostate cancer in clinical practice. We sought to develop and test a humanised PSMA minibody IAB2M conjugated to the fluorophore IRDye 800CW-NHS ester in men undergoing robot-assisted laparoscopic radical prostatectomy (RARP) to image prostate cancer cells during surgery.MethodsThe minibody was evaluated pre-clinically using PSMA positive/negative xenograft models, following which 23 men undergoing RARP between 2018 and 2020 received between 2.5 mg and 20 mg of IR800-IAB2M intravenously, at intervals between 24 h and 17 days prior to surgery. At every step of the procedure, the prostate, pelvic lymph node chains and extra-prostatic surrounding tissue were imaged with a dual Near-infrared (NIR) and white light optical platform for fluorescence in vivo and ex vivo. Histopathological evaluation of intraoperative and postoperative microscopic fluorescence imaging was undertaken for verification.ResultsTwenty-three patients were evaluated to optimise both the dose of the reagent and the interval between injection and surgery and secure the best possible specificity of fluorescence images. Six cases are presented in detail as exemplars. Overall sensitivity and specificity in detecting non-lymph-node extra-prostatic cancer tissue were 100% and 65%, and 64% and 64% respectively for lymph node positivity. There were no side-effects associated with administration of the reagent.ConclusionIntraoperative imaging of prostate cancer tissue is feasible and safe using IR800-IAB2M. Further evaluation is underway to assess the benefit of using the technique in improving completion of surgical excision during RARP.RegistrationISCRCTN10046036: https://www.isrctn.com/ISRCTN10046036.

SARS-CoV-2 Viroporin E Induces Ca2+ Release and Neuron Cell Death in Primary Cultures of Rat Hippocampal Cells Aged In Vitro.

The COVID-19 pandemic was caused by infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which may lead to serious respiratory, vascular and neurological dysfunctions. The SARS-CoV-2 envelope protein (E protein) is a structural viroporin able to form ion channels in cell membranes, which is critical for viral replication. However, its effects in primary neurons have not been addressed. Here we used fluorescence microscopy and calcium imaging to study SARS-CoV-2 viroporin E localization and the effects on neuron damage and intracellular Ca2+ homeostasis in a model of rat hippocampal neurons aged in vitro. We found that the E protein quickly enters hippocampal neurons and colocalizes with the endoplasmic reticulum (ER) in both short-term (6-8 days in vitro, DIV) and long-term (20-22 DIV) cultures resembling young and aged neurons, respectively. Strikingly, E protein treatment induces apoptosis in aged neurons but not in young neurons. The E protein induces variable increases in cytosolic Ca2+ concentration in hippocampal neurons. Ca2+ responses to the E protein are due to Ca2+ release from intracellular stores at the ER. Moreover, E protein-induced Ca2+ release is very small in young neurons and increases dramatically in aged neurons, consistent with the enhanced Ca2+ store content in aged neurons. We conclude that the SARS-CoV-2 E protein quickly translocates to ER endomembranes of rat hippocampal neurons where it releases Ca2+, probably acting like a viroporin, thus producing Ca2+ store depletion and neuron apoptosis in aged neurons and likely contributing to neurological damage in COVID-19 patients.

Alkyl anchor–modified artificial viral capsid budding outside-to-inside and inside-to-outside giant vesicles

ABSTRACT The budding of human immunodeficiency virus from an infected host cell is induced by the modification of structural proteins bearing long-chain fatty acids, followed by their anchoring to the cell membrane. Although many model budding systems using giant unilamellar vesicles (GUVs) induced by various stimuli have been developed, constructing an artificial viral budding system of GUVs using only synthesized molecules remains challenging. Herein, we report the construction of an artificial viral capsid budding system from a lipid bilayer of GUV. The C-terminus of the β-annulus peptide was modified using an octyl chain as an alkyl anchor via a disulfide bond. The self-assembly of the β-annulus peptide with an octyl chain formed an artificial viral capsid aggregate. The fluorescence imaging and transmission electron microscopy observations revealed that the addition of the tetramethylrhodamine (TMR)-labeled octyl chain–bearing β-annulus peptide to the outer aqueous phase of GUV induced the budding of the capsid-encapsulated daughter vesicle outside-to-inside the mother GUV. Conversely, the encapsulation of the TMR-labeled octyl chain–bearing β-annulus peptide in the inner aqueous phase of GUV induced the budding of the capsid-encapsulated daughter vesicle inside-to-outside the mother GUV. Contrarily, the addition of the TMR-labeled β-annulus peptide to GUV barely induced budding. It was demonstrated that the higher the membrane fluidity of GUV, the more likely budding would be induced by the addition of the alkyl anchor–modified artificial viral capsid. The simple virus-mimicking material developed in this study, which buds off through membrane anchoring, can provide physicochemical insights into the mechanisms of natural viral budding from cells.

Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images

Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers. Deconwolf enables accurate quantification of crowded diffraction limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images and allows robust detection of individual transcripts in tissue sections imaged with ×20 air objectives. Deconvolution of in situ spatial transcriptomics images with Deconwolf increased the number of transcripts identified more than threefold, while the application of Deconwolf to images obtained by fluorescence in situ sequencing of barcoded Oligopaint probes drastically improved chromosome tracing. Deconwolf greatly facilitates the use of deconvolution in many bioimaging applications.