Background Heterologous vaccine regimens have been widely discussed as a way to mitigate intermittent supply shortages and to improve immunogenicity and safety of COVID-19 vaccines. We aimed to assess the reactogenicity and immunogenicity of heterologous immunisations with ChAdOx1 nCov-19 (AstraZeneca, Cambridge, UK) and BNT162b2 (Pfizer-BioNtech, Mainz, Germany) compared with homologous BNT162b2 and ChAdOx1 nCov-19 immunisation. Methods This is an interim analysis of a prospective observational cohort study enrolling health-care workers in Berlin (Germany) who received either homologous ChAdOx1 nCov-19 or heterologous ChAdOx1 nCov-19-BNT162b2 vaccination with a 10-12-week vaccine interval or homologous BNT162b2 vaccination with a 3-week vaccine interval. We assessed reactogenicity after the first and second vaccination by use of electronic questionnaires on days 1, 3, 5, and 7. Immunogenicity was measured by the presence of SARS-CoV-2-specific antibodies (full spike-IgG, S1-IgG, and RBD-IgG), by an RBD-ACE2 binding inhibition assay (surrogate SARS-CoV-2 virus neutralisation test), a pseudovirus neutralisation assay against two variants of concerns (alpha [B.1.1.7] and beta [B.1.351]), and anti-S1-IgG avidity. T-cell reactivity was measured by IFN-γ release assay.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

Severe COVID-19 is linked to both dysfunctional immune response and unrestrained immunopathology, and it remains unclear whether T cells contribute to disease pathology. Here, we combined single-cell transcriptomics and single-cell proteomics with mechanistic studies to assess pathogenic T cell functions and inducing signals. We identified highly activated, CD16 + T cells with increased cytotoxic functions in severe COVID-19. CD16 expression enabled immune complex-mediated, T cell receptor-independent degranulation and cytotoxicity not found in other diseases. CD16 + T cells from COVID-19 patients promoted microvascular endothelial cell injury and release of neutrophil and monocyte chemoattractants. CD16 + T cell clones persisted beyond acute disease maintaining their cytotoxic phenotype. Increased generation of C3a in severe COVID-19 induced activated CD16 + cytotoxic T cells. Proportions of activated CD16 + T cells and plasma levels of complement proteins upstream of C3a were associated with fatal outcome of COVID-19, supporting a pathological role of exacerbated cytotoxicity and complement activation in COVID-19.