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Prediction of COVID-19 disease progression by multiparametric analysis of circulating extracellular vesicles with flow cytometry

  • Evelyn Hammer
  • , Charlotte Flynn
  • , Johannes Rossler
  • , Johanna Erder
  • , Rudolf Napieralski
  • , Lisa Fricke
  • , Birgit Campbell
  • , Martin Feuerherd
  • , Felix Esslinger
  • , Albrecht von Brunn
  • , Timm Weber
  • , Siobhan King
  • , Sisareuth Tan
  • , Alain R. Brisson
  • , Ulrike Protzer
  • , Gabriele Schricker
  • , Kathrin Gartner
  • , Gregor Ebert
  • , Allessandra Moretti
  • , Florian Klein
  • Kevin Knoops, Ron Heeren, Wolfgang Hammerschmidt, Reinhard Zeidler, Olaf Wilhelm, Percy A. Knolle, Bastian Hochst*
*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Extracellular vesicles (EVs) are released from all cells of the body. They are considered to mirror the state of the cells from which they are released and circulate in the blood, suggesting a possible use of EV analysis for diagnostic purposes. Here, we report that the analysis of single EVs by flow cytometry can detect infection of cells with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by identifying expression of the SARS-CoV-2 spike (S) protein on the surface of EVs and the cellular origin of EVs by detecting cell-type-specific markers such as troponin (cTNT1) for cardiomyocytes. In coronavirus-associated disease 19 (COVID-19) patients, we detected a direct correlation of the frequencies of circulating S-expressing EVs, but not of cTNT/S-co-expressing EVs, with the subsequent development of a severe disease course. Detection of circulating S-expressing EVs indicates widespread SARS-CoV-2 infection in the body, which may contribute to the immune pathogenesis that triggers tissue and organ damage in COVID-19. Our findings suggest that detecting circulating viral antigen-expressing EVs may provide crucial predictive information on infection-associated disease courses in situations of a future viral pandemic.

Original languageEnglish
Number of pages14
JournalJournal of Virology
Volume99
Issue number10
DOIs
Publication statusPublished - Oct 2025

Keywords

  • SARS-CoV2
  • COVID-19
  • extracellular vesicles
  • virosome
  • flow cytometry
  • progress prediction

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