Circulating Platelet-Derived Extracellular Vesicles Are a Hallmark of Sars-Cov-2 Infection

Abstract

Sars-Cov-2 infection causes fever and cough that may rapidly lead to acute respiratory distress syndrome (ARDS). Few biomarkers have been identified but, unfortunately, these are individually poorly specific, and novel biomarkers are needed to better predict patient outcome. The aim of this study was to evaluate the diagnostic performance of circulating platelets (PLT)-derived extracellular vesicles (EVs) as biomarkers for Sars-Cov-2 infection, by setting a rapid and reliable test on unmanipulated blood samples. PLT-EVs were quantified by flow cytometry on two independent cohorts of Sars-CoV-2+ (n = 69), Sars-Cov-2- (n = 62) hospitalized patients, and healthy controls. Diagnostic performance of PLT-EVs was evaluated by receiver operating characteristic (ROC) curve. PLT-EVs count were higher in Sars-Cov-2+ compared to Sars-Cov-2- patients or HC. ROC analysis of the combined cohorts showed an AUC = 0.79 and an optimal cut-off value of 1472 EVs/μL, with 75% sensitivity and 74% specificity. These data suggest that PLT-EVs might be an interesting biomarker deserving further investigations to test their predictive power.

Keywords: COVID-19; Sars-Cov-2; circulating biomarker; extracellular vesicles; platelets.

Figure 1

Working principle and gating strategy. The customized kit (BD) uses an APC emitting lipophilic cationic dye (LCD) (red), which diffuses into double layer structures assisted by membrane potential, thus staining both particles and cells, and Phalloidin-FITC that binds the cytoskeleton protein actin, only in particles having damaged membrane. The gating strategy is shown in the middle panel. To draw the correct EVs area, the PLT was identified from all events according their high intensity for CD31 and CD41a markers. Then, the EVs area was drawn by excluding the PLTs as shown in the second plot (LCD vs FSC). Damaged EVs were discriminated in the third plot according to the positivity to phalloidin (green) (LCD vs Phalloidin). All the LCD+/phalloidin- particles were then stained with anti-CD31 and anti–CD41a antibodies to detect platelets-derived EVs (CD41a/CD31). The lower dot plots show the negative controls; the sample was treated with a solution of Triton X-100, and then re-acquired in order to confirm the specificity of the LCD. Fluorescent minus one (FMOs) for the CD31 and CD41 are also shown. Each sample was fixed in PBS and paraformaldehyde (PFA) 2%, to avoid any further release of EVs from cells, and acquired with BD FACSymphony™ A5 and FC data were analyzed using FACSDiva software (BD, NJ, USA).

Figure 2

PLT-EVs count are higher in Sars-Cov-2+ patients and are an independent diagnostic predictor biomarker of Sars-Cov-2 infection. (A) Counts of PLT-EVs in Sars-Cov2+, Sars-Cov2− patients and HC (grey rectangle, n = 10) enrolled during the 1st wave and 2nd wave. The box plot summarizes the results from the combined cohorts. The count of EVs/μL was obtained using the following formula: EVs/μL = (N° of EV events for a given population*dilution factor)/(Acquired volume); (B) Combined receiver operating characteristic curve (ROC) evaluating the accuracy of PLT-EVs in Sars-Cov-2+ and Sars-Cov-2− patients. The area under the curve (AUC) was 0.79 (95% CI 0.7074–0.8648; p < 0.0001), the optimal cut-off was 1472 EVs/μL with 75% sensitivity and 74% specificity. Both cohorts, analyzed independently, gave overlapping results. For statistical analyses, D’Agostino and Pearson normality test was used before to perform Kruskal-Wallis with Dunn’s post-hoc test. All statistical analysis was performed using GraphPad Instat software (GraphPad Software). ** p < 0.01, *** p < 0.001, **** p < 0.0001.