The fatal trajectory of pulmonary COVID-19 is driven by lobular ischemia and fibrotic remodelling.

BACKGROUND: COVID-19 is characterized by a heterogeneous clinical presentation, ranging from mild symptoms to severe courses of disease. 9-20% of hospitalized patients with severe lung disease die from COVID-19 and a substantial number of survivors develop long-COVID. Our objective was to provide comprehensive insights into the pathophysiology of severe COVID-19 and to identify liquid biomarkers for disease severity and therapy response. METHODS: We studied a total of 85 lungs (n = 31 COVID autopsy samples; n = 7 influenza A autopsy samples; n = 18 interstitial lung disease explants; n = 24 healthy controls) using the highest resolution Synchrotron radiation-based hierarchical phase-contrast tomography, scanning electron microscopy of microvascular corrosion casts, immunohistochemistry, matrix-assisted laser desorption ionization mass spectrometry imaging, and analysis of mRNA expression and biological pathways. Plasma samples from all disease groups were used for liquid biomarker determination using ELISA. The anatomic/molecular data were analyzed as a function of patients' hospitalization time. FINDINGS: The observed patchy/mosaic appearance of COVID-19 in conventional lung imaging resulted from microvascular occlusion and secondary lobular ischemia. The length of hospitalization was associated with increased intussusceptive angiogenesis. This was associated with enhanced angiogenic, and fibrotic gene expression demonstrated by molecular profiling and metabolomic analysis. Increased plasma fibrosis markers correlated with their pulmonary tissue transcript levels and predicted disease severity. Plasma analysis confirmed distinct fibrosis biomarkers (TSP2, GDF15, IGFBP7, Pro-C3) that predicted the fatal trajectory in COVID-19. INTERPRETATION: Pulmonary severe COVID-19 is a consequence of secondary lobular microischemia and fibrotic remodelling, resulting in a distinctive form of fibrotic interstitial lung disease that contributes to long-COVID. FUNDING: This project was made possible by a number of funders. The full list can be found within the Declaration of interests / Acknowledgements section at the end of the manuscript.

Finite Element Modelling of a Cellular Electric Microenvironment.

Clinical studies show electrical stimulation (ES) to be a potential therapy for the healing and regeneration of various tissues. Understanding the mechanisms of cell response when exposed to electrical fields can therefore guide the optimization of clinical applications. In vitro experiments aim to help uncover those, offering the advantage of wider input and output ranges that can be ethically and effectively assessed. However, the advancements in in vitro experiments are difficult to reproduce directly in clinical settings. Mainly, that is because the ES devices used in vitro differ significantly from the ones suitable for patient use, and the path from the electrodes to the targeted cells is different. Translating the in vitro results into in vivo procedures is therefore not straightforward. We emphasize that the cellular microenvironment's structure and physical properties play a determining role in the actual experimental testing conditions and suggest that measures of charge distribution can be used to bridge the gap between in vitro and in vivo. Considering this, we show how in silico finite element modelling (FEM) can be used to describe the cellular microenvironment and the changes generated by electric field (EF) exposure. We highlight how the EF couples with geometric structure to determine charge distribution. We then show the impact of time dependent inputs on charge movement. Finally, we demonstrate the relevance of our new in silico model methodology using two case studies: (i) in vitro fibrous Poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate) (PEDOT-PSS) scaffolds and (ii) in vivo collagen in extracellular matrix (ECM).

MicroCT imaging reveals differential 3D micro-scale remodelling of the murine aorta in ageing and Marfan syndrome.

Aortic wall remodelling is a key feature of both ageing and genetic connective tissue diseases, which are associated with vasculopathies such as Marfan syndrome (MFS). Although the aorta is a 3D structure, little attention has been paid to volumetric assessment, primarily due to the limitations of conventional imaging techniques. Phase-contrast microCT is an emerging imaging technique, which is able to resolve the 3D micro-scale structure of large samples without the need for staining or sectioning. Methods: Here, we have used synchrotron-based phase-contrast microCT to image aortae of wild type (WT) and MFS Fbn1C1039G/+ mice aged 3, 6 and 9 months old (n=5). We have also developed a new computational approach to automatically measure key histological parameters. Results: This analysis revealed that WT mice undergo age-dependent aortic remodelling characterised by increases in ascending aorta diameter, tunica media thickness and cross-sectional area. The MFS aortic wall was subject to comparable remodelling, but the magnitudes of the changes were significantly exacerbated, particularly in 9 month-old MFS mice with ascending aorta wall dilations. Moreover, this morphological remodelling in MFS aorta included internal elastic lamina surface breaks that extended throughout the MFS ascending aorta and were already evident in animals who had not yet developed aneurysms. Conclusions: Our 3D microCT study of the sub-micron wall structure of whole, intact aorta reveals that histological remodelling of the tunica media in MFS could be viewed as an accelerated ageing process, and that phase-contrast microCT combined with computational image analysis allows the visualisation and quantification of 3D morphological remodelling in large volumes of unstained vascular tissues.