Super-resolution Multispectral Imaging Nanoimmunosensor for Simultaneous Detection of Diverse Early Cancer Biomarkers.

In medical diagnosis, relying on only one type of biomarker is insufficient to accurately identify cancer. Blood-based multicancer early detection can help identify more than one type of cancer from a single blood sample. In this study, a super-resolution multispectral imaging nanoimmunosensor (srMINI) based on three quantum dots (QDs) of different color conjugated with streptavidin was developed for the simultaneous screening of various cancer biomarkers in blood at the single-molecule level. In the experiment, the srMINI chip was used to simultaneously detect three key cancer biomarkers: carcinoembryonic antigen (CEA), C-reactive protein (CRP), and alpha-fetoprotein (AFP). The srMINI chip exhibited 108 times higher detection sensitivity of 0.18-0.5 ag/mL (1.1-2.6 zM) for these cancer biomarkers than commercial enzyme-linked immunosorbent assay kits because of the absence of interfering signals from the substrate, establishing considerable potential for multiplex detection of cancer biomarkers in blood. Therefore, the simultaneous detection of various cancer biomarkers using the developed srMINI chip with high diagnostic precision and accuracy is expected to play a decisive role in early diagnosis or community screening as a single-molecule biosensor.

Omnidirectional image super-resolution via position attention network.

For convenient transmission, omnidirectional images (ODIs) usually follow the equirectangular projection (ERP) format and are low-resolution. To provide better immersive experience, omnidirectional image super resolution (ODISR) is essential. However, ERP ODIs suffer from serious geometric distortion and pixel stretching across latitudes, generating massive redundant information at high latitudes. This characteristic poses a huge challenge for the traditional SR methods, which can only obtain the suboptimal ODISR performance. To address this issue, we propose a novel position attention network (PAN) for ODISR in this paper. Specifically, a two-branch structure is introduced, in which the basic enhancement branch (BE) serves to achieve coarse deep feature enhancement for extracted shallow features. Meanwhile, the position attention enhancement branch (PAE) builds a positional attention mechanism to dynamically adjust the contribution of features at different latitudes in the ERP representation according to their positions and stretching degrees, which achieves the enhancement for the differentiated information, suppresses the redundant information, and modulate the deep features with spatial distortion. Subsequently, the features of two branches are fused effectively to achieve the further refinement and adapt the distortion characteristic of ODIs. After that, we exploit a long-term memory module (LM), promoting information interactions and fusions between the branches to enhance the perception of the distortion, aggregating the prior hierarchical features to keep the long-term memory and boosting the ODISR performance. Extensive results demonstrate the state-of-the-art performance and the high efficiency of our PAN in ODISR.

Predicting the Prognosis of HIFU Ablation of Uterine Fibroids Using a Deep Learning-Based 3D Super-Resolution DWI Radiomics Model: A Multicenter Study.

RATIONALE AND OBJECTIVES: To assess the feasibility and efficacy of a deep learning-based three-dimensional (3D) super-resolution diffusion-weighted imaging (DWI) radiomics model in predicting the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids. METHODS: This retrospective study included 360 patients with uterine fibroids who received HIFU treatment, including Center A (training set: N = 240; internal testing set: N = 60) and Center B (external testing set: N = 60) and were classified as having a favorable or unfavorable prognosis based on the postoperative non-perfusion volume ratio. A deep transfer learning approach was used to construct super-resolution DWI (SR-DWI) based on conventional high-resolution DWI (HR-DWI), and 1198 radiomics features were extracted from manually segmented regions of interest in both image types. Following data preprocessing and feature selection, radiomics models were constructed for HR-DWI and SR-DWI using Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM) algorithms, with performance evaluated using area under the curve (AUC) and decision curves. RESULT: All DWI radiomics models demonstrated superior AUC in predicting HIFU ablated uterine fibroids prognosis compared to expert radiologists (AUC: 0.706, 95% CI: 0.647-0.748). When utilizing different machine learning algorithms, the HR-DWI model achieved AUC values of 0.805 (95% CI: 0.679-0.931) with SVM, 0.797 (95% CI: 0.672-0.921) with RF, and 0.770 (95% CI: 0.631-0.908) with LightGBM. Meanwhile, the SR-DWI model outperformed the HR-DWI model (P < 0.05) across all algorithms, with AUC values of 0.868 (95% CI: 0.775-0.960) with SVM, 0.824 (95% CI: 0.715-0.934) with RF, and 0.821 (95% CI: 0.709-0.933) with LightGBM. And decision curve analysis further confirmed the good clinical value of the models. CONCLUSION: Deep learning-based 3D SR-DWI radiomics model demonstrated favorable feasibility and effectiveness in predicting the prognosis of HIFU ablated uterine fibroids, which was superior to HR-DWI model and assessment by expert radiologists.

Extremely high spatiotemporal resolution microscopy for live cell imaging by single photon counting, noise elimination, and a novel restoration algorithm based on probability calculation.

Optical microscopy is essential for direct observation of dynamic phenomena in living cells. According to the classic optical theories, the images obtained through light microscopes are blurred for about half the wavelength of light, and therefore small structures below this "diffraction limit" were thought unresolvable by conventional optical microscopy. In reality, accurately obtained optical images contain complete information about the observed objects. Temporal resolution is also important for the observation of dynamic phenomena. A challenge exists here to overcome the trade-off between the time required for measurement and the accuracy of the measurement. The present paper describes a concrete methodology for reconstructing the structure of an observed object, based on the information contained in the image obtained by optical microscopy. It is realized by accurate single photon counting, complete noise elimination, and a novel restoration algorithm based on probability calculation. This method has been implemented in the Super-resolution Confocal Live Imaging Microscopy (SCLIM) we developed. The new system named SCLIM2M achieves unprecedented high spatiotemporal resolution. We have succeeded in capturing sub-diffraction-limit structures with millisecond-level dynamics of organelles and vesicles in living cells, which were never observed by conventional optical microscopy. Actual examples of the high-speed and high-resolution 4D observation of living cells are presented.

Unveiling the limits of precision in iterative MINFLUX.

In single-molecule microscopy, a big question is how precisely we can estimate the location of a single molecule. Our research shows that by using iterative localisation microscopy and factoring in the prior information, we can boost precision and reduce the number of photons needed. Leveraging the Van Trees inequality aids in determining the optimal precision achievable. Our approach holds promise for wider application in discerning the optimal precision across diverse imaging scenarios, encompassing various illumination strategies, point spread functions and overarching control methodologies.

Mild Hyperthermia-Induced Thermogenesis in the Endoplasmic Reticulum Defines Stress Response Mechanisms.

Previous studies reported that a mild, non-protein-denaturing, fever-like temperature increase induced the unfolded protein response (UPR) in mammalian cells. Our dSTORM super-resolution microscopy experiments revealed that the master regulator of the UPR, the IRE1 (inositol-requiring enzyme 1) protein, is clustered as a result of UPR activation in a human osteosarcoma cell line (U2OS) upon mild heat stress. Using ER thermo yellow, a temperature-sensitive fluorescent probe targeted to the endoplasmic reticulum (ER), we detected significant intracellular thermogenesis in mouse embryonic fibroblast (MEF) cells. Temperatures reached at least 8 °C higher than the external environment (40 °C), resulting in exceptionally high ER temperatures similar to those previously described for mitochondria. Mild heat-induced thermogenesis in the ER of MEF cells was likely due to the uncoupling of the Ca2+/ATPase (SERCA) pump. The high ER temperatures initiated a pronounced cytosolic heat-shock response in MEF cells, which was significantly lower in U2OS cells in which both the ER thermogenesis and SERCA pump uncoupling were absent. Our results suggest that depending on intrinsic cellular properties, mild hyperthermia-induced intracellular thermogenesis defines the cellular response mechanism and determines the outcome of hyperthermic stress.

Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging.

OBJECTIVES: This study aims to assess the effectiveness of super-resolution deep-learning-based reconstruction (SR-DLR), which leverages k-space data, on the image quality of lumbar spine magnetic resonance (MR) bone imaging using a 3D multi-echo in-phase sequence. MATERIALS AND METHODS: In this retrospective study, 29 patients who underwent lumbar spine MRI, including an MR bone imaging sequence between January and April 2023, were analyzed. Images were reconstructed with and without SR-DLR (Matrix sizes: 960 × 960 and 320 × 320, respectively). The signal-to-noise ratio (SNR) of the vertebral body and spinal canal and the contrast and contrast-to-noise ratio (CNR) between the vertebral body and spinal canal were quantitatively evaluated. Furthermore, the slope at half-peak points of the profile curve drawn across the posterior border of the vertebral body was calculated. Two radiologists independently assessed image noise, contrast, artifacts, sharpness, and overall image quality of both image types using a 4-point scale. Interobserver agreement was evaluated using weighted kappa coefficients, and quantitative and qualitative scores were compared via the Wilcoxon signed-rank test. RESULTS: SNRs of the vertebral body and spinal canal were notably improved in images with SR-DLR (p < 0.001). Contrast and CNR were significantly enhanced with SR-DLR compared to those without SR-DLR (p = 0.023 and p = 0.022, respectively). The slope of the profile curve at half-peak points across the posterior border of the vertebral body and spinal canal was markedly higher with SR-DLR (p < 0.001). Qualitative scores (noise: p < 0.001, contrast: p < 0.001, artifact p = 0.042, sharpness: p < 0.001, overall image quality: p < 0.001) were superior in images with SR-DLR compared to those without. Kappa analysis indicated moderate to good agreement (noise: κ = 0.56, contrast: κ = 0.51, artifact: κ = 0.46, sharpness: κ = 0.76, overall image quality: κ = 0.44). CONCLUSION: SR-DLR, which is based on k-space data, has the potential to enhance the image quality of lumbar spine MR bone imaging utilizing a 3D gradient echo in-phase sequence. CLINICAL RELEVANCE STATEMENT: The application of SR-DLR can lead to improvements in lumbar spine MR bone imaging quality.

Deciphering the sub-Golgi localization of glycosyltransferases via 3D super-resolution imaging.

The Golgi apparatus, a crucial organelle involved in protein processing, including glycosylation, exhibits complex sub-structures, i.e., cis-, medial, and trans-cisternae. This study investigated the distribution of glycosyltransferases within the Golgi apparatus of mammalian cells via 3D super-resolution imaging. Focusing on human glycosyltransferases involved in N-glycan modification, we found that even enzymes presumed to coexist in the same Golgi compartment exhibit nuanced variations in localization. By artificially making their N-terminal regions [composed of a cytoplasmic, transmembrane, and stem segment (CTS)] identical, it was possible to enhance the degree of their colocalization, suggesting the decisive role of this region in determining the sub-Golgi localization of enzymes. Ultimately, this study reveals the molecular codes within CTS regions as key determinants of glycosyltransferase localization, providing insights into precise control over the positioning of glycosyltransferases, and consequently, the interactions between glycosyltransferases and substrate glycoproteins as cargoes in the secretory pathway. This study advances our understanding of Golgi organization and opens avenues for programming the glycosylation of proteins for clinical applications.Key words: Golgi apparatus, glycosyltransferase, 3D super-resolution imaging, N-glycosylation.

A Novel Simulation Method for 3D Digital-Image Correlation: Combining Virtual Stereo Vision and Image Super-Resolution Reconstruction.

3D digital-image correlation (3D-DIC) is a non-contact optical technique for full-field shape, displacement, and deformation measurement. Given the high experimental hardware costs associated with 3D-DIC, the development of high-fidelity 3D-DIC simulations holds significant value. However, existing research on 3D-DIC simulation was mainly carried out through the generation of random speckle images. This study innovatively proposes a complete 3D-DIC simulation method involving optical simulation and mechanical simulation and integrating 3D-DIC, virtual stereo vision, and image super-resolution reconstruction technology. Virtual stereo vision can reduce hardware costs and eliminate camera-synchronization errors. Image super-resolution reconstruction can compensate for the decrease in precision caused by image-resolution loss. An array of software tools such as ANSYS SPEOS 2024R1, ZEMAX 2024R1, MECHANICAL 2024R1, and MULTIDIC v1.1.0 are used to implement this simulation. Measurement systems based on stereo vision and virtual stereo vision were built and tested for use in 3D-DIC. The results of the simulation experiment show that when the synchronization error of the basic stereo-vision system (BSS) is within 10-3 time steps, the reconstruction error is within 0.005 mm and the accuracy of the virtual stereo-vision system is between the BSS's synchronization error of 10-7 and 10-6 time steps. In addition, after image super-resolution reconstruction technology is applied, the reconstruction error will be reduced to within 0.002 mm. The simulation method proposed in this study can provide a novel research path for existing researchers in the field while also offering the opportunity for researchers without access to costly hardware to participate in related research.

REMA: A Rich Elastic Mixed Attention Module for Single Image Super-Resolution.

Detail preservation is a major challenge for single image super-resolution (SISR). Many deep learning-based SISR methods focus on lightweight network design, but these may fall short in real-world scenarios where performance is prioritized over network size. To address these problems, we propose a novel plug-and-play attention module, rich elastic mixed attention (REMA), for SISR. REMA comprises the rich spatial attention module (RSAM) and the rich channel attention module (RCAM), both built on Rich Structure. Based on the results of our research on the module's structure, size, performance, and compatibility, Rich Structure is proposed to enhance REMA's adaptability to varying input complexities and task requirements. RSAM learns the mutual dependencies of multiple LR-HR pairs and multi-scale features, while RCAM accentuates key features through interactive learning, effectively addressing detail loss. Extensive experiments demonstrate that REMA significantly improves performance and compatibility in SR networks compared to other attention modules. The REMA-based SR network (REMA-SRNet) outperforms comparative algorithms in both visual effects and objective evaluation quality. Additionally, we find that module compatibility correlates with cardinality and in-branch feature bandwidth, and that networks with high effective parameter counts exhibit enhanced robustness across various datasets and scale factors in SISR.

A Novel Electrical Equipment Status Diagnosis Method Based on Super-Resolution Reconstruction and Logical Reasoning.

The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system's situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults.

Piezo1 ion channels are capable of conformational signaling.

Piezo1 is a mechanically activated ion channel that senses forces with short latency and high sensitivity. Piezos undergo large conformational changes, induce far-reaching deformation onto the membrane, and modulate the function of two-pore potassium (K2P) channels. Taken together, this led us to hypothesize that Piezos may be able to signal their conformational state to other nearby proteins. Here, we use chemical control to acutely restrict Piezo1 conformational flexibility and show that Piezo1 conformational changes, but not ion permeation through them, are required for modulating the K2P channel K2P2.1 (TREK1). Super-resolution imaging and stochastic simulations further reveal that both channels do not co-localize, which implies that modulation is not mediated through direct binding interactions; however, at high Piezo1 densities, most TREK1 channels are within the predicted Piezo1 membrane footprint, suggesting that the footprint may underlie conformational signaling. We speculate that physiological roles originally attributed to Piezo1 ionotropic function could, alternatively, involve conformational signaling.

Improved Imaging Surface for Quantitative Single-Molecule Microscopy.

Preventing nonspecific binding is essential for sensitive surface-based quantitative single-molecule microscopy. Here we report a much-simplified RainX-F127 (RF-127) surface with improved passivation. This surface achieves up to 100-fold less nonspecific binding from protein aggregates compared to commonly used polyethylene glycol (PEG) surfaces. The method is compatible with common single-molecule techniques including single-molecule pull-down (SiMPull), super-resolution imaging, antibody-binding screening and single exosome visualization. This method is also able to specifically detect alpha-synuclein (α-syn) and tau aggregates from a wide range of biofluids including human serum, brain extracts, cerebrospinal fluid (CSF) and saliva. The simplicity of this method further allows the functionalization of microplates for robot-assisted high-throughput single-molecule experiments. Overall, this simple but improved surface offers a versatile platform for quantitative single-molecule microscopy without the need for specialized equipment or personnel.

High-sensitivity whole-mount in situ Hybridization of Mouse Oocytes and Embryos Visualizes the Super-resolution Structures and Distributions of mRNA Molecules.

Mammalian oocytes accumulate more than ten thousand mRNAs, of which three to four thousand mRNAs are translationally repressed. The timings and sites of translational activation of these dormant mRNAs are crucial for promoting oocyte maturation and embryonic development. How these mRNAs are accumulated and distributed in oocytes is therefore a fundamental issue to be explored. A method that enables visualization of mRNA molecules with high resolution in a simple manner would be valuable for understanding how oocytes accumulate and regulate the dormant mRNAs. We have developed a highly sensitive whole-mount in situ hybridization method using in vitro-synthesized RNA probes and the tyramide signal amplification (TSA) system optimized for mouse oocytes and embryos. By using this method, Pou5f1/Oct4, Emi2, and cyclin B1 mRNAs were detected in immature oocytes and 2-cell stage embryos. Confocal microscopy showed that these mRNAs formed granular structures in the oocyte cytoplasm. The structures of Pou5f1/Oct4 and cyclin B1 mRNAs persisted in 2-cell stage embryos. Pou5f1/Oct4 RNA granules exhibited a solid-like property in immature oocytes and became liquid-like droplets in 2-cell stage embryos. Double-staining of cyclin B1 mRNA with Emi2 or Pou5f1/Oct4 mRNA revealed that these mRNAs were distributed as different RNA granules without overlapping each other and that the size of cyclin B1 RNA granules tended to be larger than that of Emi2 RNA granules. The structures and distribution patterns of these mRNAs were further analyzed by N-SIM super-resolution microscopy. This analysis revealed that the large-sized RNA granules consist of many small-sized granules, suggesting the accumulation and regulation of dormant mRNAs as basal-sized RNA granules. The method established in this study can easily visualize the structure and distribution of mRNAs accumulated in mammalian oocytes and embryos with high sensitivity and super-resolution. This method is useful for investigating the cellular and molecular mechanisms of translational control of mRNAs by which maturation and early developmental processes are promoted.

Unveiling intracellular phase separation: advances in optical imaging of biomolecular condensates.

Intracellular biomolecular condensates, which form via phase separation, display a highly organized ultrastructure and complex properties. Recent advances in optical imaging techniques, including super-resolution microscopy and innovative microscopic methods that leverage the intrinsic properties of the molecules observed, have transcended the limitations of conventional microscopies. These advances facilitate the exploration of condensates at finer scales and in greater detail. The deployment of these emerging but sophisticated imaging tools allows for precise observations of the multiphasic organization and physicochemical properties of these condensates, shedding light on their functions in cellular processes. In this review, we highlight recent progress in methodological innovations and their profound implications for understanding the organization and dynamics of intracellular biomolecular condensates.

Kinesin-1-transported liposomes prefer to go straight in 3D microtubule intersections by a mechanism shared by other molecular motors.

Kinesin-1 ensembles maneuver vesicular cargoes through the three-dimensional (3D) intracellular microtubule (MT) network. To define how such cargoes navigate MT intersections, we first determined how many kinesins from an ensemble on a lipid-based cargo simultaneously engage a MT, and then determined the directional outcomes (straight, turn, terminate) for liposome cargoes at perpendicular MT intersections. Run lengths of 350-nm diameter liposomes decorated with up to 20, constitutively active, truncated kinesin-1 KIF5B (K543) were longer than single motor transported cargo, suggesting multiple motor engagement. However, detachment forces of lipid-coated beads with ~20 kinesins, measured using an optical trap, showed no more than three simultaneously engaged motors, with a single engaged kinesin predominating, indicating anticooperative MT binding. At two-dimensional (2D) and 3D in vitro MT intersections, liposomes frequently paused (~2 s), suggesting kinesins simultaneously bind both MTs and engage in a tug-of-war. Liposomes showed no directional outcome bias in 2D (1.1 straight:turn ratio) but preferentially went straight (1.8 straight:turn ratio) in 3D intersections. To explain these data, we developed a mathematical model of liposome transport incorporating the known mechanochemistry of kinesins, which diffuse on the liposome surface, and have stiff tails in both compression and extension that impact how motors engage the intersecting MTs. Our model predicts the ~3 engaged motor limit observed in the optical trap and the bias toward going straight in 3D intersections. The striking similarity of these results to our previous study of liposome transport by myosin Va suggests a "universal" mechanism by which cargoes navigate 3D intersections.

Research into super-resolution in medical imaging from 2000 to 2023: bibliometric analysis and visualization.

Background: Super-resolution (SR) refers to the use of hardware or software methods to enhance the resolution of low-resolution (LR) images and produce high-resolution (HR) images. SR is applied frequently across a variety of medical imaging contexts, particularly in the enhancement of neuroimaging, with specific techniques including SR microscopy-used for diagnostic biomarkers-and functional magnetic resonance imaging (fMRI)-a neuroimaging method for the measurement and mapping of brain activity. This bibliometric analysis of the literature related to SR in medical imaging was conducted to identify the global trends in this field, and visualization via graphs was completed to offer insights into future research prospects. Methods: In order to perform a bibliometric analysis of the SR literature, this study sourced all publications from the Web of Science Core Collection (WoSCC) database published from January 1, 2000, to October 11, 2023. A total of 3,262 articles on SR in medical imaging were evaluated. VOSviewer was used to perform co-occurrence and co-authorship analysis, and network visualization of the literature data, including author, journal, publication year, institution, and keywords, was completed. Results: From 2000 to 2023, the annual publication volume surged from 13 to 366. The top three journals in this field in terms of publication volume were as follows: (I) Scientific Reports (86 publications), (II) IEEE Transactions on Medical Imaging (74 publications), and (III) IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control (56 publications). The most prolific country, institution, and author were the United States (1,017 publications; 31,301 citations), the Chinese Academy of Sciences (124 publications; 2,758 citations), and Dinggang Shen (20 publications; 671 citations), respectively. A cluster analysis of the top 100 keywords was conducted, which revealed the presence of five co-occurrence clusters: (I) SR and artificial intelligence (AI) for medical image enhancement, (II) SR and inverse problem processing concepts for positron emission tomography (PET) image processing, (III) SR ultrasound through microbubbles, (IV) SR microscopy for Alzheimer and Parkinson diseases, and (V) SR in brain fMRI: rapid acquisition and precise imaging. The most recent high-frequency keywords were deep learning (DL), magnetic resonance imaging (MRI), and convolutional neural networks (CNNs). Conclusions: Over the past two decades, the output of publications by countries, institutions, and authors in the field of SR in medical imaging has steadily increased. Based on bibliometric analysis of international trends, the resurgence of SR in medical imaging has been facilitated by advancements in AI. The increasing need for multi-center and multi-modal medical images has further incentivized global collaboration, leading to the diverse research paths in SR medical imaging among prominent scientists.

Integrating bioengineering, super-resolution microscopy and mechanobiology in autophagy research: addendum to the guidelines (4th edition).

Recent key technological developments, such as super-resolution microscopy and microfabrication, enabled investigation of biological processes, including macroautophagy/autophagy, with unprecedented spatiotemporal resolution and control over experimental conditions. Such disruptive innovations deepened our capability to provide mechanistic understandings of the autophagic process and its causes. This addendum aims to expand the guidelines on autophagy in three key directions: optical methods enabling visualization of autophagic machinery beyond the diffraction-limited resolution; bioengineering enabling accurate designs and control over experimental conditions; and theoretical advances in mechanobiology connecting autophagy and mechanical processes of the cell. Abbreviation: 3D: three-dimensional; SIM: structured illumination microscopy; STORM: stochastic optical reconstruction microscopy.

Inefficient tissue immune response against MPXV in an immunocompromised mpox patient.

The recent outbreak of monkeypox virus (MPXV) was unprecedented in its size and distribution. Those living with uncontrolled HIV and low CD4 T cell counts might develop a fulminant clinical mpox course with increased mortality, secondary infections, and necrotizing lesions. Fatal cases display a high and widespread MPXV tissue burden. The underlying pathomechanisms are not fully understood. We report here the pathological findings of an MPXV-driven abscess in gastrocnemius muscle requiring surgery in an immunocompromised patient with severe mpox. Presence of virus particles and infectivity were confirmed by electron microscopy, expansion microscopy, and virus culture, respectively. MPXV tissue distribution by immunohistochemistry (IHC) showed a necrotic core with infection of different cell types. In contrast, at the lesion rim fibroblasts were mainly infected. Immune cells were almost absent in the necrotic core, but were abundant at the infection rim and predominantly macrophages. Further, we detected high amounts of alternatively activated GPNMB+-macrophages at the lesion border. Of note, macrophages only rarely colocalized with virus-infected cells. Insufficient clearance of infected cells and infection of lesion-associated fibroblasts sustained by the abundance of profibrotic macrophages might lead to the coalescing of lesions and the severe and persistent clinical mpox course observed in immunocompromised patients.