Molecular Profiling of COVID-19 Autopsies Uncovers Novel Disease Mechanisms

Pujadas, E.; Beaumont, M.; Shah, H.; Schrode, N.; Francoeur, N.; Shroff, S.; Bryce, C.; Grimes, Z.; Gregory, J.; Donnelly, R.; Fowkes, M.; Beaumont, K.; Sebra, R.; Cordon-Cardo, C.

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.