High dimensional profiling identifies specific immune types along the recovery trajectories of critically ill COVID19 patients.


Journal

Cellular and molecular life sciences : CMLS
ISSN: 1420-9071
Titre abrégé: Cell Mol Life Sci
Pays: Switzerland
ID NLM: 9705402

Informations de publication

Date de publication:
Apr 2021
Historique:
received: 28 10 2020
accepted: 03 03 2021
revised: 27 01 2021
pubmed: 15 3 2021
medline: 14 5 2021
entrez: 14 3 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 33715015
doi: 10.1007/s00018-021-03808-8
pii: 10.1007/s00018-021-03808-8
pmc: PMC7955698
doi:

Substances chimiques

Acute-Phase Proteins 0
Antigens, CD 0
Cytokines 0
HLA-DR Antigens 0

Types de publication

Journal Article Observational Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

3987-4002

Subventions

Organisme : KULeuven
ID : C14/17/084

Investigateurs

Michael Casaer (M)
Dieter Dauwe (D)
Jan Gunst (J)
Greet Hermans (G)
Stephanie Humblet-Baron (S)
Diether Lambrechts (D)
Adrian Liston (A)
Natalie Lorent (N)
Philippe Meersseman (P)
Johan Neyts (J)
Paul Proost (P)
Jeroen Raes (J)
Stephen Rex (S)
Sabine Tejpar (S)
Karin Thevissen (K)
Thomas Tousseyn (T)
Birgit Weynand (B)
Alexander Wilmer (A)
Carine Wouters (C)

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Auteurs

P A Penttilä (PA)

KU Leuven Flow and Mass Cytometry Facility, KU Leuven, Leuven, Belgium.

S Van Gassen (S)

Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.

D Panovska (D)

Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.

L Vanderbeke (L)

Laboratory of Clinical Bacteriology and Mycology, Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.

Y Van Herck (Y)

Laboratory of Experimental Oncology, Department of Oncology,, KU Leuven, Leuven, Belgium.

K Quintelier (K)

Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.

A Emmaneel (A)

Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.

J Filtjens (J)

Laboratory of Immunobiology, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.

B Malengier-Devlies (B)

Laboratory of Immunobiology, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.

K Ahmadzadeh (K)

Laboratory of Immunobiology, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.

P Van Mol (P)

Laboratory of Translational Genetics, Department of Human Genetics, VIB-KU Leuven, Leuven, Belgium.

D M Borràs (DM)

Laboratory for Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine (CMM), KU Leuven, Leuven, Belgium.

A Antoranz (A)

Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.

F M Bosisio (FM)

Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.

E Wauters (E)

Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium.

K Martinod (K)

Center for Molecular and Vascular Biology, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.

P Matthys (P)

Laboratory of Immunobiology, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.

Y Saeys (Y)

Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.

A D Garg (AD)

Laboratory for Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine (CMM), KU Leuven, Leuven, Belgium.

J Wauters (J)

Laboratory for Clinical Infectious and Inflammatory Disorders, Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.

F De Smet (F)

Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium. frederik.desmet@kuleuven.be.

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