Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions
Name
1-s2.0-S2666379121001154-main.pdf
Description
Published version
Size
8.08 MB
Format
Adobe PDF
Checksum (MD5)
34314e0774a5c9ff230388a0c998c565
Author(s)
Filbin, Michael R.
Mehta, Arnav
Schneider, Alexis M.
Kays, Kyle R.
Guess, Jamey R.
Gentili, Matteo
Fenyves, Bánk G.
Charland, Nicole C.
Gonye, Anna L.K.
Gushterova, Irena
Date Issued
May 2021
Journal
Cell Reports Medicine
Publisher
Elsevier BV
Citation
Filbin, Michael R. et al. "Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions." Cell Reports Medicine 2, 5 (May 2021): 100287 © 2021 The Authors
Version
Final published version
Abstract
Mechanisms underlying severe coronavirus disease 2019 (COVID-19) disease remain poorly understood. We analyze several thousand plasma proteins longitudinally in 306 COVID-19 patients and 78 symptomatic controls, uncovering immune and non-immune proteins linked to COVID-19. Deconvolution of our plasma proteome data using published scRNA-seq datasets reveals contributions from circulating immune and tissue cells. Sixteen percent of patients display reduced inflammation yet comparably poor outcomes. Comparison of patients who died to severely ill survivors identifies dynamic immune-cell-derived and tissue-associated proteins associated with survival, including exocrine pancreatic proteases. Using derived tissue-specific and cell-type-specific intracellular death signatures, cellular angiotensin-converting enzyme 2 (ACE2) expression, and our data, we infer whether organ damage resulted from direct or indirect effects of infection. We propose a model in which interactions among myeloid, epithelial, and T cells drive tissue damage. These datasets provide important insights and a rich resource for analysis of mechanisms of severe COVID-19 disease.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Broad Institute of MIT and Harvard
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.xcrm.2021.100287
