High dimensional profiling identifies specific immune types along the recovery trajectories of critically ill COVID19 patients
Male
0301 basic medicine
Neutrophils
Critical Illness
COVID-19
Convalescence
HLA-DR Antigens
Length of Stay
Middle Aged
Lymphocyte Subsets
Monocytes
3. Good health
Intensive Care Units
Leukocyte Count
03 medical and health sciences
Antigens, CD
Cytokines
Humans
Original Article
Female
Lymphocyte Count
Pandemics
Acute-Phase Proteins
Follow-Up Studies
DOI:
10.1007/s00018-021-03808-8
Publication Date:
2021-03-13T17:02:52Z
AUTHORS (22)
ABSTRACT
The COVID-19 pandemic poses a major burden on healthcare and economic systems across the globe. Even though a majority of the population develops only minor symptoms upon SARS-CoV-2 infection, a significant number are hospitalized at intensive care units (ICU) requiring critical care. While insights into the early stages of the disease are rapidly expanding, the dynamic immunological processes occurring in critically ill patients throughout their recovery at ICU are far less understood. Here, we have analysed whole blood samples serially collected from 40 surviving COVID-19 patients throughout their recovery in ICU using high-dimensional cytometry by time-of-flight (CyTOF) and cytokine multiplexing. Based on the neutrophil-to-lymphocyte ratio (NLR), we defined four sequential immunotypes during recovery that correlated to various clinical parameters, including the level of respiratory support at concomitant sampling times. We identified classical monocytes as the first immune cell type to recover by restoration of HLA-DR-positivity and the reduction of immunosuppressive CD163 + monocytes, followed by the recovery of CD8 + and CD4 + T cell and non-classical monocyte populations. The identified immunotypes also correlated to aberrant cytokine and acute-phase reactant levels. Finally, integrative analysis of cytokines and immune cell profiles showed a shift from an initially dysregulated immune response to a more coordinated immunogenic interplay, highlighting the importance of longitudinal sampling to understand the pathophysiology underlying recovery from severe COVID-19.
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CITATIONS (14)
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