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Am J Pathol . Molecular Profiling of COVID-19 Autopsies Uncovers Novel Disease Mechanisms

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  • Am J Pathol . Molecular Profiling of COVID-19 Autopsies Uncovers Novel Disease Mechanisms


    Am J Pathol


    . 2021 Sep 7;S0002-9440(21)00382-5.
    doi: 10.1016/j.ajpath.2021.08.009. Online ahead of print.
    Molecular Profiling of COVID-19 Autopsies Uncovers Novel Disease Mechanisms


    Elisabet Pujadas 1 , Michael Beaumont 2 , Hardik Shah 2 , Nadine Schrode 2 , Nancy Francoeur 2 , Sanjana Shroff 2 , Clare Bryce 1 , Zachary Grimes 1 , Jill Gregory 3 , Ryan Donnelly 1 , Mary E Fowkes 1 , Kristin G Beaumont 2 , Robert Sebra 4 , Carlos Cordon-Cardo 5



    AffiliationsFree PMC article

    Abstract

    Current understanding of COVID-19 pathophysiology is limited by disease heterogeneity, complexity, and a paucity of studies assessing patient tissues with advanced molecular tools. Rapid autopsy tissues 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. 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 receptor like receptor or decorin. Single-nuclei RNA sequencing of olfactory bulb and prefrontal cortex highlighted remarkable diversity of coronavirus receptors. ACE2 was rarely expressed, while BSG showed diffuse expression, and ANPEP was associated with vascular/mesenchymal cell types. Comparison of lung and lymph node tissues from patients with different symptomatology (one died after a month-long hospitalization with multi-organ involvement, the other after a few days of respiratory symptoms) with digital spatial profiling resulted in distinct molecular phenotypes. Evaluation of COVID-19 rapid autopsy tissues with advanced molecular techniques can identify pathways and effectors, map diverse receptors at the single-cell level, and help dissect differences driving diverging clinical courses among individual patients. Extension of this approach to larger datasets will substantially advance the understanding of the mechanisms behind COVID-19 pathophysiology.


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