COVID-19 is characterized by a wide spectrum of disease severity, whose indicators and underlying mechanisms need to be identified. The role of extracellular vesicles (EVs) in COVID-19 and their biomarker potential, however, remains largely unknown. Aiming to identify specific EV signatures of patients with mild compared to severe COVID-19, researchers at the University of Duisburg-Essen characterized the EV composition of 20 mild and 26 severe COVID-19 patients along with 16 sex and age-matched healthy donors with a panel of eight different antibodies by imaging flow cytometry (IFCM). They correlated the obtained data with 37 clinical, prerecorded biochemical and immunological parameters. Severe patients’ sera contained increased amounts of CD13+ and CD82+ EVs, which positively correlated with IL-6-producing and circulating myeloid-derived suppressor cells (MDSCs) and with the serum concentration of proinflammatory cytokines, respectively. Sera of mild COVID-19 patients contained more HLA-ABC+ EVs than sera of the healthy donors and more CD24+ EVs than severe COVID-19 patients. Their increased abundance negatively correlated with disease severity and accumulation of MDSCs, being considered as key drivers of immunopathogenesis in COVID-19. Altogether, these results support the potential of serum EVs as powerful biomarkers for COVID-19 severity and pave the way for future investigations aiming to unravel the role of EVs in COVID-19 progression.
Correlation and LEfSe analysis of EVs subtypes data with haematological,
immunological and biochemical parameters of COVID-19
(A) A correlation plot shows correlation between EVs immunological parameters and clinical and immunological parameters of mild (n = 18) and severe (n = 19) COVID-19 patients Spearman rank correlation coefficient was estimated to determine the association between the 37 parameters (clinical and immunological) and 8 immunological EVs parameters, collected from total 37 patients. Only significant comparisons (p < 0.05) between variables being compared are shown with circles size and colour corresponding to Spearmen’s rank coefficient, as indicated. n_, number; Mo- monocytes; pmn- polymorphonucler; inf- inflammatory; clas- classical; trans-transitory. (B) LEfSe analysis plot of biomarkers associated with disease severity. Linear discriminant analysis (LDA) Effect Size (LEfSe) was performed using Galaxy-based LEfSe workflow to discover biomarkers associated with severity of disease. Results are obtained using Kruskall-Wallis test for differentially distributed biomarkers in different classes, “mild” and “severe,” following LEfSe with default parameters. Green color indicates biomarkers enriched in patients with mild disease symptoms, and purple indicates biomarkers associated with severe symptoms. The bar column length represents logarithmic discriminant analysis (LDA) score higher than 2. Only markers able to discriminate between mild and severe conditions are shown. p-percentage; n-number