Characterization of low-density granulocytes in COVID-19.
Luz E CabreraPirkka T PekkarinenMaria AlanderKirsten H A NowlanNgoc Anh NguyenSuvi Tuulia JokirantaSuvi KuivanenAnu PatjasSointu MeroSari H PakkanenSanttu HeinonenAnu KanteleOlli VapalahtiEliisa KekäläinenTomas M StrandinPublished in: PLoS pathogens (2021)
Severe COVID-19 is characterized by extensive pulmonary complications, to which host immune responses are believed to play a role. As the major arm of innate immunity, neutrophils are one of the first cells recruited to the site of infection where their excessive activation can contribute to lung pathology. Low-density granulocytes (LDGs) are circulating neutrophils, whose numbers increase in some autoimmune diseases and cancer, but are poorly characterized in acute viral infections. Using flow cytometry, we detected a significant increase of LDGs in the blood of acute COVID-19 patients, compared to healthy controls. Based on their surface marker expression, COVID-19-related LDGs exhibit four different populations, which display distinctive stages of granulocytic development and most likely reflect emergency myelopoiesis. Moreover, COVID-19 LDGs show a link with an elevated recruitment and activation of neutrophils. Functional assays demonstrated the immunosuppressive capacities of these cells, which might contribute to impaired lymphocyte responses during acute disease. Taken together, our data confirms a significant granulocyte activation during COVID-19 and suggests that granulocytes of lower density play a role in disease progression.
Keyphrases
- sars cov
- coronavirus disease
- liver failure
- induced apoptosis
- respiratory failure
- flow cytometry
- drug induced
- immune response
- respiratory syndrome coronavirus
- emergency department
- poor prognosis
- healthcare
- squamous cell carcinoma
- high throughput
- hepatitis b virus
- risk factors
- young adults
- machine learning
- inflammatory response
- long non coding rna
- extracorporeal membrane oxygenation
- electronic health record
- lymph node metastasis
- deep learning
- genetic diversity