SARS-CoV-2 infection is associated with a pro-thrombotic platelet phenotype.

Novel coronavirus disease 2019 (COVID-19) is associated with a hypercoagulable state, characterized by abnormal coagulation parameters and by increased incidence of cardiovascular complications. With this study, we aimed to investigate the activation state and the expression of transmembrane proteins in platelets of hospitalized COVID-19 patients. We investigated transmembrane proteins expression with a customized mass cytometry panel of 21 antibodies. Platelets of 8 hospitalized COVID-19 patients not requiring intensive care support and without pre-existing conditions were compared to platelets of healthy controls (11 donors) with and without in vitro stimulation with thrombin receptor-activating peptide (TRAP). Mass cytometry of non-stimulated platelets detected an increased surface expression of activation markers P-Selectin (0.67 vs. 1.87 median signal intensity for controls vs. patients, p = 0.0015) and LAMP-3 (CD63, 0.37 vs. 0.81, p = 0.0004), the GPIIb/IIIa complex (4.58 vs. 5.03, p < 0.0001) and other adhesion molecules involved in platelet activation and platelet-leukocyte interactions. Upon TRAP stimulation, mass cytometry detected a higher expression of P-selectin in COVID-19 samples compared to controls (p < 0.0001). However, we observed a significantly reduced capacity of COVID-19 platelets to increase the expression of activation markers LAMP-3 and P-Selectin upon stimulation with TRAP. We detected a hyperactivated phenotype in platelets during SARS-CoV-2 infection, consisting of highly expressed platelet activation markers, which might contribute to the hypercoagulopathy observed in COVID-19. In addition, several transmembrane proteins were more highly expressed compared to healthy controls. These findings support research projects investigating antithrombotic and antiplatelet treatment regimes in COVID-19 patients, and provide new insights on the phenotypical platelet expression during SARS-CoV-2 infection.

SLAMF7 and IL-6R define distinct cytotoxic versus helper memory CD8+ T cells.

The prevailing 'division of labor' concept in cellular immunity is that CD8+ T cells primarily utilize cytotoxic functions to kill target cells, while CD4+ T cells exert helper/inducer functions. Multiple subsets of CD4+ memory T cells have been characterized by distinct chemokine receptor expression. Here, we demonstrate that analogous CD8+ memory T-cell subsets exist, characterized by identical chemokine receptor expression signatures and controlled by similar generic programs. Among them, Tc2, Tc17 and Tc22 cells, in contrast to Tc1 and Tc17 + 1 cells, express IL-6R but not SLAMF7, completely lack cytotoxicity and instead display helper functions including CD40L expression. CD8+ helper T cells exhibit a unique TCR repertoire, express genes related to skin resident memory T cells (TRM) and are altered in the inflammatory skin disease psoriasis. Our findings reveal that the conventional view of CD4+ and CD8+ T cell capabilities and functions in human health and disease needs to be revised.

Setting Up Mass Cytometry in a Shared Resource Lab Environment.

Core facilities or shared resource laboratories (SRL) ensure fast and direct access to high-end instrumentation that is often expensive and requires technical expertise to be operated and maintained. SRL have the responsibility and capability to ensure the possibility of generating reproducible quality data by implementing best practices, providing expert knowledge, and keeping the necessary instrumentation in good shape. Here we review critical steps involved in the integration of mass cytometry in a shared resource lab environment. Lab requirements and considerations for best practices are discussed, and critical aspects of mass cytometry project discussions as well as strategies for the management and documentation of samples are presented.

Age dependent differences in the kinetics of γδ T cells after influenza vaccination.

Immunosenescence is a hallmark of the aging immune system and is considered the main cause of a reduced vaccine efficacy in the elderly. Although γδ T cells can become activated by recombinant influenza hemagglutinin, their age-related immunocompetence during a virus-induced immune response has so far not been investigated. In this study we evaluate the kinetics of γδ T cells after vaccination with the trivalent 2011/2012 northern hemisphere seasonal influenza vaccine. We applied multi-parametric flow cytometry to a cohort of 21 young (19-30 years) and 23 elderly (53-67 years) healthy individuals. Activated and proliferating γδ T cells, as identified by CD38 and Ki67 expression, were quantified on the days 0, 3, 7, 10, 14, 17, and 21. We observed a significantly lower number of activated and proliferating γδ T cells at baseline and following vaccination in elderly as compared to young individuals. The kinetics changes of activated γδ T cells were much stronger in the young, while corresponding changes in the elderly occurred slower. In addition, we observed an association between day 21 HAI titers of influenza A and the frequencies of Ki67+ γδ T cells at day 7 in the young. In conclusion, aging induces alterations of the γδ T cell response that might have negative implications for vaccination efficacy.

Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination.

Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (<31 years, N = 17) versus elderly (≥50 years, N = 20) donors sero-negative to the newly emerged A(H1N1)pdm09 influenza virus strain and correlated it with the serological response to that specific strain after seasonal influenza vaccination. Extensive multi-chromatic FACS analysis (36 lymphocyte sub-populations measured) was used to quantitatively assess the cellular immune status before vaccination. We identified CD4+ T cells, and amongst them particularly naive CD4+ T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4+ T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4+ counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31-50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count.