CovidStanfordV1, Main, Exploration, bibRecord, 000833

A single-cell atlas of the peripheral immune response in patients with severe COVID-19.

Identifieur interne : 000833 ( Main/Exploration ); précédent : 000832; suivant : 000834

A single-cell atlas of the peripheral immune response in patients with severe COVID-19.

Auteurs : Aaron J. Wilk [États-Unis] ; Arjun Rustagi [États-Unis] ; Nancy Q. Zhao [États-Unis] ; Jonasel Roque [États-Unis] ; Giovanny J. Martínez-Col N [États-Unis] ; Julia L. Mckechnie [États-Unis] ; Geoffrey T. Ivison [États-Unis] ; Thanmayi Ranganath [États-Unis] ; Rosemary Vergara [États-Unis] ; Taylor Hollis [États-Unis] ; Laura J. Simpson [États-Unis] ; Philip Grant [États-Unis] ; Aruna Subramanian [États-Unis] ; Angela J. Rogers [États-Unis] ; Catherine A. Blish [États-Unis]

Source :

Nature medicine [ 1546-170X ] ; 2020.

RBID : pubmed:32514174

Descripteurs français

English descriptors

KwdEn :
- Adult (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Betacoronavirus (immunology), COVID-19 (MeSH), Case-Control Studies (MeSH), Coronavirus Infections (genetics), Coronavirus Infections (immunology), Coronavirus Infections (pathology), Cytokines (genetics), Cytokines (metabolism), Female (MeSH), Gene Expression Profiling (methods), Humans (MeSH), Immunity, Cellular (MeSH), Killer Cells, Natural (immunology), Killer Cells, Natural (metabolism), Leukocytes, Mononuclear (immunology), Leukocytes, Mononuclear (metabolism), Leukocytes, Mononuclear (virology), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (genetics), Pneumonia, Viral (immunology), Pneumonia, Viral (pathology), RNA-Seq (methods), SARS-CoV-2 (MeSH), Sequence Analysis, RNA (methods), Severity of Illness Index (MeSH), Single-Cell Analysis (methods), T-Lymphocytes (immunology), T-Lymphocytes (metabolism), Young Adult (MeSH).
MESH :
- chemical , genetics : Cytokines.
- genetics : Coronavirus Infections, Pneumonia, Viral.
- immunology : Betacoronavirus, Coronavirus Infections, Killer Cells, Natural, Leukocytes, Mononuclear, Pneumonia, Viral, T-Lymphocytes.
- chemical , metabolism : Cytokines, Killer Cells, Natural, Leukocytes, Mononuclear, T-Lymphocytes.
- methods : Gene Expression Profiling, RNA-Seq, Sequence Analysis, RNA, Single-Cell Analysis.
- pathology : Coronavirus Infections, Pneumonia, Viral.
- virology : Leukocytes, Mononuclear.
- Adult, Aged, Aged, 80 and over, COVID-19, Case-Control Studies, Female, Humans, Immunity, Cellular, Male, Middle Aged, Pandemics, SARS-CoV-2, Severity of Illness Index, Young Adult.

Abstract

There is an urgent need to better understand the pathophysiology of Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, which has infected more than three million people worldwide¹. Approximately 20% of patients with COVID-19 develop severe disease and 5% of patients require intensive care². Severe disease has been associated with changes in peripheral immune activity, including increased levels of pro-inflammatory cytokines^3,4 that may be produced by a subset of inflammatory monocytes^5,6, lymphopenia^7,8 and T cell exhaustion^9,10. To elucidate pathways in peripheral immune cells that might lead to immunopathology or protective immunity in severe COVID-19, we applied single-cell RNA sequencing (scRNA-seq) to profile peripheral blood mononuclear cells (PBMCs) from seven patients hospitalized for COVID-19, four of whom had acute respiratory distress syndrome, and six healthy controls. We identify reconfiguration of peripheral immune cell phenotype in COVID-19, including a heterogeneous interferon-stimulated gene signature, HLA class II downregulation and a developing neutrophil population that appears closely related to plasmablasts appearing in patients with acute respiratory failure requiring mechanical ventilation. Importantly, we found that peripheral monocytes and lymphocytes do not express substantial amounts of pro-inflammatory cytokines. Collectively, we provide a cell atlas of the peripheral immune response to severe COVID-19.

DOI: 10.1038/s41591-020-0944-y
PubMed: 32514174
PubMed Central: PMC7382903

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000624
to stream Main, to step Curation: 000624

Le document en format XML

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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term>
<term>Aged (MeSH)</term>
<term>Aged, 80 and over (MeSH)</term>
<term>Betacoronavirus (immunology)</term>
<term>COVID-19 (MeSH)</term>
<term>Case-Control Studies (MeSH)</term>
<term>Coronavirus Infections (genetics)</term>
<term>Coronavirus Infections (immunology)</term>
<term>Coronavirus Infections (pathology)</term>
<term>Cytokines (genetics)</term>
<term>Cytokines (metabolism)</term>
<term>Female (MeSH)</term>
<term>Gene Expression Profiling (methods)</term>
<term>Humans (MeSH)</term>
<term>Immunity, Cellular (MeSH)</term>
<term>Killer Cells, Natural (immunology)</term>
<term>Killer Cells, Natural (metabolism)</term>
<term>Leukocytes, Mononuclear (immunology)</term>
<term>Leukocytes, Mononuclear (metabolism)</term>
<term>Leukocytes, Mononuclear (virology)</term>
<term>Male (MeSH)</term>
<term>Middle Aged (MeSH)</term>
<term>Pandemics (MeSH)</term>
<term>Pneumonia, Viral (genetics)</term>
<term>Pneumonia, Viral (immunology)</term>
<term>Pneumonia, Viral (pathology)</term>
<term>RNA-Seq (methods)</term>
<term>SARS-CoV-2 (MeSH)</term>
<term>Sequence Analysis, RNA (methods)</term>
<term>Severity of Illness Index (MeSH)</term>
<term>Single-Cell Analysis (methods)</term>
<term>T-Lymphocytes (immunology)</term>
<term>T-Lymphocytes (metabolism)</term>
<term>Young Adult (MeSH)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term>
<term>Adulte d'âge moyen (MeSH)</term>
<term>Agranulocytes (immunologie)</term>
<term>Agranulocytes (métabolisme)</term>
<term>Agranulocytes (virologie)</term>
<term>Analyse de profil d'expression de gènes (méthodes)</term>
<term>Analyse de séquence d'ARN (méthodes)</term>
<term>Analyse sur cellule unique (méthodes)</term>
<term>Betacoronavirus (immunologie)</term>
<term>Cellules tueuses naturelles (immunologie)</term>
<term>Cellules tueuses naturelles (métabolisme)</term>
<term>Cytokines (génétique)</term>
<term>Cytokines (métabolisme)</term>
<term>Femelle (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Immunité cellulaire (MeSH)</term>
<term>Indice de gravité de la maladie (MeSH)</term>
<term>Infections à coronavirus (anatomopathologie)</term>
<term>Infections à coronavirus (génétique)</term>
<term>Infections à coronavirus (immunologie)</term>
<term>Jeune adulte (MeSH)</term>
<term>Lymphocytes T (immunologie)</term>
<term>Lymphocytes T (métabolisme)</term>
<term>Mâle (MeSH)</term>
<term>Pandémies (MeSH)</term>
<term>Pneumopathie virale (anatomopathologie)</term>
<term>Pneumopathie virale (génétique)</term>
<term>Pneumopathie virale (immunologie)</term>
<term>Sujet âgé (MeSH)</term>
<term>Sujet âgé de 80 ans ou plus (MeSH)</term>
<term>Études cas-témoins (MeSH)</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>Cytokines</term>
</keywords>
<keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Cytokines</term>
<term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Agranulocytes</term>
<term>Betacoronavirus</term>
<term>Cellules tueuses naturelles</term>
<term>Infections à coronavirus</term>
<term>Lymphocytes T</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>Betacoronavirus</term>
<term>Coronavirus Infections</term>
<term>Killer Cells, Natural</term>
<term>Leukocytes, Mononuclear</term>
<term>Pneumonia, Viral</term>
<term>T-Lymphocytes</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Cytokines</term>
<term>Killer Cells, Natural</term>
<term>Leukocytes, Mononuclear</term>
<term>T-Lymphocytes</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Gene Expression Profiling</term>
<term>RNA-Seq</term>
<term>Sequence Analysis, RNA</term>
<term>Single-Cell Analysis</term>
</keywords>
<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Agranulocytes</term>
<term>Cellules tueuses naturelles</term>
<term>Cytokines</term>
<term>Lymphocytes T</term>
</keywords>
<keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Analyse de profil d'expression de gènes</term>
<term>Analyse de séquence d'ARN</term>
<term>Analyse sur cellule unique</term>
</keywords>
<keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Agranulocytes</term>
</keywords>
<keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Leukocytes, Mononuclear</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Adult</term>
<term>Aged</term>
<term>Aged, 80 and over</term>
<term>COVID-19</term>
<term>Case-Control Studies</term>
<term>Female</term>
<term>Humans</term>
<term>Immunity, Cellular</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Pandemics</term>
<term>SARS-CoV-2</term>
<term>Severity of Illness Index</term>
<term>Young Adult</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Femelle</term>
<term>Humains</term>
<term>Immunité cellulaire</term>
<term>Indice de gravité de la maladie</term>
<term>Jeune adulte</term>
<term>Mâle</term>
<term>Pandémies</term>
<term>Sujet âgé</term>
<term>Sujet âgé de 80 ans ou plus</term>
<term>Études cas-témoins</term>
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<front><div type="abstract" xml:lang="en">There is an urgent need to better understand the pathophysiology of Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, which has infected more than three million people worldwide<sup>1</sup>
. Approximately 20% of patients with COVID-19 develop severe disease and 5% of patients require intensive care<sup>2</sup>
. Severe disease has been associated with changes in peripheral immune activity, including increased levels of pro-inflammatory cytokines<sup>3,4</sup>
 that may be produced by a subset of inflammatory monocytes<sup>5,6</sup>
, lymphopenia<sup>7,8</sup>
 and T cell exhaustion<sup>9,10</sup>
. To elucidate pathways in peripheral immune cells that might lead to immunopathology or protective immunity in severe COVID-19, we applied single-cell RNA sequencing (scRNA-seq) to profile peripheral blood mononuclear cells (PBMCs) from seven patients hospitalized for COVID-19, four of whom had acute respiratory distress syndrome, and six healthy controls. We identify reconfiguration of peripheral immune cell phenotype in COVID-19, including a heterogeneous interferon-stimulated gene signature, HLA class II downregulation and a developing neutrophil population that appears closely related to plasmablasts appearing in patients with acute respiratory failure requiring mechanical ventilation. Importantly, we found that peripheral monocytes and lymphocytes do not express substantial amounts of pro-inflammatory cytokines. Collectively, we provide a cell atlas of the peripheral immune response to severe COVID-19.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32514174</PMID>
<DateCompleted><Year>2020</Year>
<Month>07</Month>
<Day>22</Day>
</DateCompleted>
<DateRevised><Year>2021</Year>
<Month>01</Month>
<Day>10</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1546-170X</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>26</Volume>
<Issue>7</Issue>
<PubDate><Year>2020</Year>
<Month>07</Month>
</PubDate>
</JournalIssue>
<Title>Nature medicine</Title>
<ISOAbbreviation>Nat Med</ISOAbbreviation>
</Journal>
<ArticleTitle>A single-cell atlas of the peripheral immune response in patients with severe COVID-19.</ArticleTitle>
<Pagination><MedlinePgn>1070-1076</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.1038/s41591-020-0944-y</ELocationID>
<Abstract><AbstractText>There is an urgent need to better understand the pathophysiology of Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, which has infected more than three million people worldwide<sup>1</sup>
. Approximately 20% of patients with COVID-19 develop severe disease and 5% of patients require intensive care<sup>2</sup>
. Severe disease has been associated with changes in peripheral immune activity, including increased levels of pro-inflammatory cytokines<sup>3,4</sup>
 that may be produced by a subset of inflammatory monocytes<sup>5,6</sup>
, lymphopenia<sup>7,8</sup>
 and T cell exhaustion<sup>9,10</sup>
. To elucidate pathways in peripheral immune cells that might lead to immunopathology or protective immunity in severe COVID-19, we applied single-cell RNA sequencing (scRNA-seq) to profile peripheral blood mononuclear cells (PBMCs) from seven patients hospitalized for COVID-19, four of whom had acute respiratory distress syndrome, and six healthy controls. We identify reconfiguration of peripheral immune cell phenotype in COVID-19, including a heterogeneous interferon-stimulated gene signature, HLA class II downregulation and a developing neutrophil population that appears closely related to plasmablasts appearing in patients with acute respiratory failure requiring mechanical ventilation. Importantly, we found that peripheral monocytes and lymphocytes do not express substantial amounts of pro-inflammatory cytokines. Collectively, we provide a cell atlas of the peripheral immune response to severe COVID-19.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wilk</LastName>
<ForeName>Aaron J</ForeName>
<Initials>AJ</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0003-1430-5852</Identifier>
<AffiliationInfo><Affiliation>Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Rustagi</LastName>
<ForeName>Arjun</ForeName>
<Initials>A</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0002-6921-1012</Identifier>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zhao</LastName>
<ForeName>Nancy Q</ForeName>
<Initials>NQ</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0001-6217-854X</Identifier>
<AffiliationInfo><Affiliation>Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Roque</LastName>
<ForeName>Jonasel</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Martínez-Colón</LastName>
<ForeName>Giovanny J</ForeName>
<Initials>GJ</Initials>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>McKechnie</LastName>
<ForeName>Julia L</ForeName>
<Initials>JL</Initials>
<AffiliationInfo><Affiliation>Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ivison</LastName>
<ForeName>Geoffrey T</ForeName>
<Initials>GT</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0002-3612-0069</Identifier>
<AffiliationInfo><Affiliation>Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ranganath</LastName>
<ForeName>Thanmayi</ForeName>
<Initials>T</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0001-9865-2119</Identifier>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Vergara</LastName>
<ForeName>Rosemary</ForeName>
<Initials>R</Initials>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Hollis</LastName>
<ForeName>Taylor</ForeName>
<Initials>T</Initials>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Simpson</LastName>
<ForeName>Laura J</ForeName>
<Initials>LJ</Initials>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Grant</LastName>
<ForeName>Philip</ForeName>
<Initials>P</Initials>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Subramanian</LastName>
<ForeName>Aruna</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Rogers</LastName>
<ForeName>Angela J</ForeName>
<Initials>AJ</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0001-6969-6200</Identifier>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. ajrogers@stanford.edu.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Blish</LastName>
<ForeName>Catherine A</ForeName>
<Initials>CA</Initials>
<Identifier Source="ORCID">http://orcid.org/0000-0001-6946-7627</Identifier>
<AffiliationInfo><Affiliation>Stanford Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA. cblish@stanford.edu.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. cblish@stanford.edu.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Chan Zuckerberg Biohub, San Francisco, CA, USA. cblish@stanford.edu.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
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<Agency>National Science Foundation (NSF)</Agency>
<Country>International</Country>
</Grant>
<Grant><GrantID>DP1 DA04608902</GrantID>
<Agency>U.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse (NIDA)</Agency>
<Country>International</Country>
</Grant>
<Grant><GrantID>T32 GM007365</GrantID>
<Acronym>GM</Acronym>
<Agency>NIGMS NIH HHS</Agency>
<Country>United States</Country>
</Grant>
<Grant><GrantID>DP1 DA046089</GrantID>
<Acronym>DA</Acronym>
<Agency>NIDA NIH HHS</Agency>
<Country>United States</Country>
</Grant>
<Grant><GrantID>AI007290-35</GrantID>
<Agency>U.S. Department of Health & Human Services | National Institutes of Health (NIH)</Agency>
<Country>International</Country>
</Grant>
<Grant><GrantID>1016687</GrantID>
<Agency>Burroughs Wellcome Fund (BWF)</Agency>
<Country>International</Country>
</Grant>
<Grant><GrantID>T32 AI007502</GrantID>
<Acronym>AI</Acronym>
<Agency>NIAID NIH HHS</Agency>
<Country>United States</Country>
</Grant>
<Grant><GrantID>T32 AI007290</GrantID>
<Acronym>AI</Acronym>
<Agency>NIAID NIH HHS</Agency>
<Country>United States</Country>
</Grant>
<Grant><GrantID>T32 GM007365-44</GrantID>
<Agency>U.S. Department of Health & Human Services | National Institutes of Health (NIH)</Agency>
<Country>International</Country>
</Grant>
<Grant><GrantID>K23 HL125663</GrantID>
<Acronym>HL</Acronym>
<Agency>NHLBI NIH HHS</Agency>
<Country>United States</Country>
</Grant>
<Grant><GrantID>T32 AI007502-23</GrantID>
<Agency>U.S. Department of Health & Human Services | National Institutes of Health (NIH)</Agency>
<Country>International</Country>
</Grant>
<Grant><Acronym>WT_</Acronym>
<Agency>Wellcome Trust</Agency>
<Country>United Kingdom</Country>
</Grant>
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<PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
<PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType>
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<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>06</Month>
<Day>08</Day>
</ArticleDate>
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<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>Nat Med</MedlineTA>
<NlmUniqueID>9502015</NlmUniqueID>
<ISSNLinking>1078-8956</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList><Chemical><RegistryNumber>0</RegistryNumber>
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</Chemical>
</ChemicalList>
<CitationSubset>IM</CitationSubset>
<CommentsCorrectionsList><CommentsCorrections RefType="UpdateOf"><RefSource>medRxiv. 2020 Apr 23;:</RefSource>
<PMID Version="1">32511639</PMID>
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<MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName>
<QualifierName UI="Q000276" MajorTopicYN="Y">immunology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016022" MajorTopicYN="N">Case-Control Studies</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018352" MajorTopicYN="Y">Coronavirus Infections</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
<QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName>
<QualifierName UI="Q000473" MajorTopicYN="N">pathology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016207" MajorTopicYN="N">Cytokines</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
<QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D020869" MajorTopicYN="N">Gene Expression Profiling</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007111" MajorTopicYN="Y">Immunity, Cellular</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007694" MajorTopicYN="N">Killer Cells, Natural</DescriptorName>
<QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName>
<QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007963" MajorTopicYN="Y">Leukocytes, Mononuclear</DescriptorName>
<QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName>
<QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName>
<QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011024" MajorTopicYN="Y">Pneumonia, Viral</DescriptorName>
<QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName>
<QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName>
<QualifierName UI="Q000473" MajorTopicYN="N">pathology</QualifierName>
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</MeshHeading>
<MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="N">SARS-CoV-2</DescriptorName>
</MeshHeading>
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<QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012720" MajorTopicYN="N">Severity of Illness Index</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D059010" MajorTopicYN="N">Single-Cell Analysis</DescriptorName>
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</MeshHeading>
<MeshHeading><DescriptorName UI="D013601" MajorTopicYN="N">T-Lymphocytes</DescriptorName>
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<QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName>
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A single-cell atlas of the peripheral immune response in patients with severe COVID-19.

A single-cell atlas of the peripheral immune response in patients with severe COVID-19.

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