Evidence map›Paper›PMID 33715015›Full record

Observational studyCellular and molecular life sciences : CMLS2021

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

P A Penttilä, S Van Gassen, D Panovska, L Vanderbeke, Y Van Herck, K Quintelier, A Emmaneel, J Filtjens, B Malengier-Devlies, K Ahmadzadeh and 12 more

Abstract readObservational Study
In one paragraph

Observational study in Cellular and molecular life sciences : CMLS, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

P A Penttilä *KU Leuven Flow and Mass Cytometry Facility, KU Leuven, Leuven, Belgium.
S Van Gassen *Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
D Panovska *Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
L VanderbekeLaboratory of Clinical Bacteriology and Mycology, Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
Y Van HerckLaboratory of Experimental Oncology, Department of Oncology,, KU Leuven, Leuven, Belgium.
K QuintelierDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
A EmmaneelDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
J FiltjensLaboratory of Immunobiology, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
B Malengier-DevliesLaboratory of Immunobiology, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
K AhmadzadehLaboratory of Immunobiology, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
P Van MolLaboratory of Translational Genetics, Department of Human Genetics, VIB-KU Leuven, Leuven, Belgium.
D M BorràsLaboratory for Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine (CMM), KU Leuven, Leuven, Belgium.
A AntoranzLaboratory for Precision Cancer Medicine, Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
F M BosisioTranslational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
E WautersLaboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium.
K MartinodCenter for Molecular and Vascular Biology, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
P MatthysLaboratory of Immunobiology, Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.
Y SaeysData Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.
A D GargLaboratory for Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine (CMM), KU Leuven, Leuven, Belgium.
J Wauters *Laboratory for Clinical Infectious and Inflammatory Disorders, Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
F De Smet *Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium. frederik.desmet@kuleuven.be.ORCID http://orcid.org/0000-0002-6669-3335
CONTAGIOUS consortium

Funding

KULeuven C14/17/084
6 · The paper itself

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.

Indexed as

Critical IllnessLeukocyte CountSARS-CoV-2Acute-Phase ProteinsAntigens, CDConvalescenceCOVID-19CytokinesFemaleFollow-Up StudiesHLA-DR AntigensHumansIntensive Care UnitsLength of StayLymphocyte CountLymphocyte SubsetsAcute-Phase ProteinsAntigens, CDCytokinesHLA-DR AntigensCOVID-19CyTOFCytokine multiplexingICU patientsImmune profilingNeutrophil-to-lymphocyte ratioRecovering immune populations

Identifiers

PMID33715015
PMCPMC7955698

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.