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Molecular Profiling of COVID-19 Autopsies Uncovers Novel Disease Mechanisms

By Elisabet Pujadas, Michael Beaumont, Hardik Shah, Nadine Schrode, Nancy Francoeur, Sanjana Shroff, Clare Bryce, Zachary Grimes, Jill Gregory, Ryan Donnelly, Mary Fowkes, Kristin Beaumont, Robert Sebra, Carlos Cordon-Cardo

Posted 07 Apr 2021
medRxiv DOI: 10.1101/2021.04.04.21253205

Background: Current understanding of COVID-19 pathophysiology is limited by disease heterogeneity, complexity, and a paucity of studies evaluating patient tissues with advanced molecular tools. Methods: Autopsy tissues from two COVID-19 patients, one of whom died after a month-long hospitalization with multi-organ involvement while the other died after a few days of respiratory symptoms, were evaluated using multi-scale RNASeq methods (bulk, single-nuclei, and spatial RNASeq next-generation sequencing) to provide unprecedented molecular resolution of COVID-19 induced damage. Findings: Comparison of infected/uninfected tissues revealed four major regulatory pathways. Effectors within these pathways could constitute novel therapeutic targets, including the complement receptor C3AR1, calcitonin-like receptor or decorin. Single-nuclei RNA sequencing of olfactory bulb and prefrontal cortex highlighted remarkable diversity of coronavirus receptors. Angiotensin I converting enzyme 2 was rarely expressed, while Basignin showed diffuse expression, and alanyl aminopeptidase was associated with vascular/mesenchymal cell types. Comparison of lung and lymph node tissues from patients with different symptomatology with Digital Spatial Profiling resulted in distinct molecular phenotypes. Interpretation: COVID-19 is a far more complex and heterogeneous disease than initially anticipated. Evaluation of COVID-19 rapid autopsy tissues with advanced molecular techniques can identify pathways and effectors at play in individual patients, measure the staggering diversity of receptors in specific brain areas and other well-defined tissue compartments at the single-cell level, and help dissect differences driving diverging clinical courses among patients. Extension of this approach to larger datasets will substantially advance the understanding of the mechanisms behind COVID-19 pathophysiology. Funding: No external funding was used in this study.

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