Author: 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; Khanna, Hargun K.; LaSalle, Thomas J.; Lavin-Parsons, Kendall M.; Lilley, Brendan M.; Lodenstein, Carl L.; Manakongtreecheep, Kasidet; Margolin, Justin D.; McKaig, Brenna N.; Rojas-Lopez, Maricarmen; Russo, Brian C.; Sharma, Nihaarika; Tantivit, Jessica; Thomas, Molly F.; Gerszten, Robert E.; Heimberg, Graham S.; Hoover, Paul J.; Lieb, David J.; Lin, Brian; Ngo, Debby; Pelka, Karin; Reyes, Miguel; Smillie, Christopher S.; Waghray, Avinash; Wood, Thomas E.; Zajac, Amanda S.; Jennings, Lori L.; Grundberg, Ida; Bhattacharyya, Roby P.; Parry, Blair Alden; Villani, Alexandra-Chloé; Sade-Feldman, Moshe; Hacohen, Nir; Goldberg, Marcia B.
Title: Longitudinal proteomic analysis of plasma from patients with severe COVID-19 reveal patient survival-associated signatures, tissue-specific cell death, and cell-cell interactions Cord-id: xca1bkcv Document date: 2021_5_3
ID: xca1bkcv
Snippet: Mechanisms underlying severe 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 scRNAseq datasets reveals contributions from circulating immune and tissue cells. Sixteen percent of patients display reduced inflammation yet comparably poor outcomes. Comparison of patients who
Document: Mechanisms underlying severe 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 scRNAseq 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 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.
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