bioRxiv Science⌕ Search

bioRxiv · 10.1101/2021.09.16.460594

3D virtual Histopathology of Cardiac Tissue from Covid-19 Patients based on Phase-Contrast X-ray Tomography

Abstract

For the first time, we have used phase-contrast x-ray tomography to characterize the three-dimensional (3d) structure of cardiac tissue from patients who succumbed to Covid-19. By extending conventional histopatholocigal examination by a third dimension, the delicate pathological changes of the vascular system of severe Covid-19 progressions can be analyzed, fully quantified and compared to other types of viral myocarditis and controls. To this end, cardiac samples with a cross section of 3.5mm were scanned at the synchrotron in a parallel beam configuration. The vascular network was segmented by a deep learning architecture suitable for 3d datasets (V-net), trained by sparse manual annotations. Pathological alterations of vessels, concerning the variation of diameters and the amount of small holes, were observed, indicative of elevated occurrence of intussusceptive angiogenesis, also confirmed by scanning electron microscopy. Further, we implemented a fully automated analysis of the tissue structure in form of shape measures based on the structure tensor. The corresponding distributions show that the histopathology of Covid-19 differs from both influenza and typical coxsackie virus myocarditis.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Reichardt, M., Moller Jensen, P., Andersen Dahl, V., Bjorholm Dahl, A., Ackermann, M., Shah, H., Länger, F., Werlein, C., Kuehnel, M. P., Jonigk, D., Salditt, T.. 2021-09-18. 3D virtual Histopathology of Cardiac Tissue from Covid-19 Patients based on Phase-Contrast X-ray Tomography. https://doi.org/10.1101/2021.09.16.460594

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

NAE1-Dependent Protein Neddylation Preserves Endothelial Identity and Vascular Integrity

Background: Endothelial dysfunction is a central driver of cardiovascular and inflammatory diseases, yet the post-translational mechanisms that preserve endothelial homeostasis remain incompletely understood. Protein neddylation, the covalent conjugation of a ubiquitin-like modifier, regulates diverse cellular processes, yet its physiological role in the vascular endothelium remains unknown. This study investigated whether protein neddylation is required to preserve endothelial identity and vascular homeostasis. Methods: We generated tamoxifen-inducible endothelial-specific Nae1 knockout mice to inhibit neddylation and combined bulk RNA sequencing, single-cell and single-nucleus transcriptomics, quantitative proteomics, biochemical analyses, and gain- and loss-of-function approaches to define the role of endothelial neddylation in vascular homeostasis and inflammatory injury. Results: Endothelial-specific Nae1 deletion caused rapid mortality associated with vascular leakage, platelet accumulation, inflammation, and multi-organ injury. Multi-omics analyses demonstrated profound loss of endothelial identity, characterized by suppression of core endothelial programs and activation of inflammatory, procoagulant, and pyroptotic pathways. Single-cell analyses revealed progressive endothelial dysfunction culminating in depletion of the endothelial population and remodeling of the vascular niche. Mechanistically, endothelial neddylation deficiency activated gasdermin D (GSDMD)- and gasdermin E (GSDME)-dependent pyroptosis, whereas dual inhibition of GSDMD and GSDME markedly attenuated inflammatory transcriptomic remodeling, vascular injury, hepatocyte death, immune cell infiltration, and platelet accumulation. Translational analyses demonstrated reduced endothelial neddylation in experimental endotoxemia and decreased expression of neddylation pathway components in human atherosclerosis and COVID-19 datasets. Conversely, restoration of endothelial neddylation partially reversed inflammatory endothelial transcriptomic reprogramming in vivo. Conclusions: NAE1-dependent protein neddylation is an essential regulator of endothelial identity and vascular integrity. Loss of endothelial neddylation promotes gasdermin-dependent pyroptosis and thrombo-inflammatory vascular injury, whereas restoration of the neddylation pathway mitigates inflammatory endothelial dysfunction. These findings identify endothelial neddylation as a fundamental mechanism maintaining vascular homeostasis and a potential therapeutic target for cardiovascular and inflammatory diseases.

pathology↗

Cetacean "gas-bubble thromboembolic polycystic liver disease": "Budd-Chiari-like syndrome" in dolphins?

Nearly two decades ago, pathologic examination results suggested acoustic factors, such as mid-frequency active naval military sonar (MFAS) could be the cause of acute decompression-like sickness in stranded beaked whales. Acute systemic gas embolism in these beaked whales was published together with enigmatic cystic liver lesions (CLL), characterized by intrahepatic encapsulated gas-filled cysts, tentatively interpreted as "gas-bubble" lesions in various cetacean species. Here we provide a pathologic reinterpretation of CLL in cetaceans. From 1,200 cetaceans necropsied, CLL were only observed in striped dolphins (Stenella coeruleoalba), with a low prevalence (2%), and recapitulated pathologic features of Budd-Chiari syndrome in humans. Our results strongly suggest that CLL are the result of the combination of pre-existing or concomitant hepatic vascular disorder (e.g., severe hepatobiliary trematodiasis) superimposed and exacerbated by gas bubbles, and clearly differ from acute systemic gas embolism in stranded beaked whales linked to MFAS.

pathology↗

Class-Controlled Copy-Paste Based Cell Segmentation for CoNIC Challenge

Muti-class cell segmentation in histopathology images is a challenging task. Here, we propose a copy-paste augmentation-based method for CoNIC challenge. As the challenge train data is severely class imbalanced. To deal with it, we copy all cell objects of train data and paste them to the train image on the fly while training model. The paste strategy is that we paste more cell objects of the insufficient classes and paste less cell objects for the sufficient classes. We experimented the method by stratified splitting train data in 4:1 ratio, the result shows the copy paste method can reach PQ 64.84 and mPQ 53.72, which improved and 0.66 compared to without copy pasted. Moreover, the improvements in those insufficient classes is more obvious.

pathology↗