Binding of phosphatidylserine-positive microparticles by PBMCs classifies disease severity in COVID-19 patients.
Lisa RauschKonstantin LutzMartina SchiffererElena WinheimRudi GruberElina F OesterhausLinus RinkeJohannes C HellmuthClemens SchererMaximilian MuenchhoffChristopher MandelMichael Bergwelt-BaildonMikael SimonsTobias StraubAnne B KrugJan KranichThomas BrockerPublished in: Journal of extracellular vesicles (2021)
Infection with SARS-CoV-2 is associated with thromboinflammation, involving thrombotic and inflammatory responses, in many COVID-19 patients. In addition, immune dysfunction occurs in patients characterised by T cell exhaustion and severe lymphopenia. We investigated the distribution of phosphatidylserine (PS), a marker of dying cells, activated platelets and platelet-derived microparticles (PMP), during the clinical course of COVID-19. We found an unexpectedly high amount of blood cells loaded with PS + PMPs for weeks after the initial COVID-19 diagnosis. Elevated frequencies of PS + PMP + PBMCs correlated strongly with increasing disease severity. As a marker, PS outperformed established laboratory markers for inflammation, leucocyte composition and coagulation, currently used for COVID-19 clinical scoring. PS + PMPs preferentially bound to CD8 + T cells with gene expression signatures of proliferating effector rather than memory T cells. As PS + PMPs carried programmed death-ligand 1 (PD-L1), they may affect T cell expansion or function. Our data provide a novel marker for disease severity and show that PS, which can trigger the blood coagulation cascade, the complement system, and inflammation, resides on activated immune cells. Therefore, PS may serve as a beacon to attract thromboinflammatory processes towards lymphocytes and cause immune dysfunction in COVID-19.
Keyphrases
- sars cov
- coronavirus disease
- oxidative stress
- gene expression
- respiratory syndrome coronavirus
- induced apoptosis
- end stage renal disease
- chronic kidney disease
- dna methylation
- palliative care
- newly diagnosed
- dendritic cells
- prognostic factors
- drug delivery
- mass spectrometry
- deep learning
- immune response
- machine learning
- patient reported outcomes
- binding protein
- wound healing
- cancer therapy
- genome wide
- artificial intelligence