Delineating COVID-19 immunological features using single-cell RNA sequencing

•Single-cell RNA sequencing is a powerful biotechnology in COVID-19 research•It reveals multiple features of SARS-CoV-2 pathogenesis and host immune response•Single-cell immune profiling characterizes the adaptive immune response in COVID-19•Analysis of B cell receptors accelerates the neutralizing antibody identification Understanding the molecular mechanisms of coronavirus disease 2019 (COVID-19) pathogenesis and immune response is vital for developing therapies. Single-cell RNA sequencing has been applied to delineate the cellular heterogeneity of the host response toward COVID-19 in multiple tissues and organs. Here, we review the applications and findings from over 80 original COVID-19 single-cell RNA sequencing studies as well as many secondary analysis studies. We describe that single-cell RNA sequencing reveals multiple features of COVID-19 patients with different severity, including cell populations with proportional alteration, COVID-19-induced genes and pathways, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in single cells, and adaptation of immune repertoire. We also collect published single-cell RNA sequencing datasets from original studies. Finally, we discuss the limitations in current studies and perspectives for future advance. Understanding the molecular mechanisms of coronavirus disease 2019 (COVID-19) pathogenesis and immune response is vital for developing therapies. Single-cell RNA sequencing has been applied to delineate the cellular heterogeneity of the host response toward COVID-19 in multiple tissues and organs. Here, we review the applications and findings from over 80 original COVID-19 single-cell RNA sequencing studies as well as many secondary analysis studies. We describe that single-cell RNA sequencing reveals multiple features of COVID-19 patients with different severity, including cell populations with proportional alteration, COVID-19-induced genes and pathways, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in single cells, and adaptation of immune repertoire. We also collect published single-cell RNA sequencing datasets from original studies. Finally, we discuss the limitations in current studies and perspectives for future advance. IntroductionAs of June 11th, 2022, the estimated coronavirus disease 2019 (COVID-19) global caseload and mortality are over 535 million cases and over 6.3 million deaths respectively (https://ourworldindata.org/grapher/cumulative-deaths-and-cases-covid-19). The severity of this global emergency has provided fuel for COVID-19 research. This is reflected in the sharp increase in COVID-19-related publications, with over 100,000 articles estimated in 2020 alone (https://www.nature.com/articles/d41586-020-03564-y), many of these published as preprints.Single-cell RNA sequencing (scRNA-seq) has become one of the most powerful tools to understand the dynamics of gene expression and genomics both within the cell and in the cellular environment. First developed in 2009 to sequence a mouse blastomere, subsequent developments have made this high-resolution methodology readily available and widely applied for dissecting heterogeneity of human tissues and underlying diseases. The clinical symptoms of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection severity varies dramatically, ranging from asymptotic, mild, severe, critical, to even death. Furthermore, viral infection of host cells causes dramatic changes in the immune response.1Bost P. Giladi A. Liu Y. et al.Host-viral infection maps reveal signatures of severe COVID-19 patients.Cell. 2020; 181: 1475-1488.e12Abstract Full Text Full Text PDF PubMed Scopus (238) Google Scholar, 2Delorey T.M. Ziegler C.G.K. Heimberg G. et al.COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets.Nature. 2021; 595: 107-113Crossref PubMed Scopus (170) Google Scholar, 3Grant R.A. Morales-Nebreda L. Markov N.S. et al.NU SCRIPT Study InvestigatorsCircuits between infected macrophages and T cells in SARS-CoV-2 pneumonia.Nature. 2021; 590: 635-641Crossref PubMed Scopus (230) Google Scholar, 4Wauters E. Van Mol P. Garg A.D. et al.Discriminating mild from critical COVID-19 by innate and adaptive immune single-cell profiling of bronchoalveolar lavages.Cell Res. 2021; 31: 272-290Crossref PubMed Scopus (100) Google Scholar scRNA-seq offers a high-resolution view into the cell and cellular environment. Thus, it has been an important tool for studying the molecular mechanisms of COVID-19, including the dynamic cellular changes in response to viral infection.Accordingly, we have seen over 200 scRNA-seq publications for COVID-19 since the spring 2020. Here, we systematically summarize our search of these studies, the related datasets, as well as their discoveries. We first present a summary of the literature and their classifications. In addition, we describe the published datasets and their features by categories. We then discuss the standards of and definitions of COVID-19 infection severity in these studies. Next, the findings from these studies regarding immune cell subpopulations and differential gene expression are compared and discussed. In addition, the pathways of interest and their functions in COVID-19 infection severity are reviewed. Furthermore, we highlight some important findings in cellular communication, cell trajectory inference, and other applications such as using novel variable, diversity and joining (VDJ) sequencing to identify COVID-19-specific T and B cell responses. Finally, we review the applications of these datasets, their limitations, and potential improvements to be made in future. We further discuss the gaps in knowledge in the field and its current direction.Rapid adoption of SCRNA-seq in COVID-19 researchSince early spring 2020, over 80 original studies using scRNA-seq in COVID-19 research and over 60 bioinformatic re-analysis studies of those original datasets have been published. We retrieved a collection of articles about this topic from PubMed with query "(single-cell) AND (sequencing OR seq) AND (COVID OR SARS-CoV-2)" on October 4th, 2021. After removing duplicates, replacing published preprint articles, and filtering out irrelevant articles, we obtained a total of 262 articles (Table S1). We classified articles according to experimental methods, species, and whether subjects were infected with COVID-19. Among these articles, nearly half of the studies performed scRNA-seq on infected or uninfected subjects, while another half performed bioinformatic analysis on published datasets but did not perform original sequencing (Figure 1A ).Next, we analyzed the trend of COVID-19 scRNA-seq studies by looking into the online publication date for these articles. For the studies performing scRNA-seq on infected subjects, there is a gradual increase in the number of article since the first publication by Wen et al. in May 2020 (Figure 1B).5Wen W. Su W. Tang H. et al.Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing.Cell Discov. 2020; 6: 41Crossref PubMed Scopus (17) Google Scholar There are also 28 studies performing scRNA-seq on uninfected subjects and analyzing potential implications of SARS-CoV-2 infection (labeled "non-COVID-19 infection" in Figure 1A). They quantified the expression of ACE2 and TMPRSS2 in certain organs, tissues, or cell types to predict their susceptibility to SARS-CoV-2 infection.6Hoffmann M. Kleine-Weber H. Schroeder S. et al.SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor.Cell. 2020; 181: 271-280.e8Abstract Full Text Full Text PDF PubMed Scopus (10278) Google Scholar scRNA-seq studies using vaccinated human or animal subjects to study vaccine efficacy and vaccine-induced immune responses started to appear in 2021. Since June 2021, there has been a trend of more articles focusing on analyzing COVID-19 scRNA-seq datasets than non-COVID-19 datasets. This is likely due to the accumulating number of COVID-19 scRNA-seq studies that shared data in the past year. With the ongoing global pandemic, we expect that more scRNA-seq and bioinformatic analysis studies will appear soon.Data sharing is a critical issue in biomedical research. As the datasets generated in COVID-19 scRNA-seq studies can be re-analyzed by others, we evaluate data sharing of these studies (Figure 1C). About two-thirds of the studies share both raw (FASTQ) and processed data, or at least share the processed data. However, there are also studies that did not share their generated scRNA-seq datasets on any public databases or websites. In addition, several studies claim they uploaded their datasets into specific data repository, but there are issues preventing access, including invalid accession numbers and long-term delayed sharing. This issue is unexpected, as most journals now require data sharing for peer review. To evaluate the effect of data sharing, we counted the frequency of published COVID-19 scRNA-seq datasets that were re-analyzed in bioinformatic analysis articles. The top three datasets7Liao M. Liu Y. Yuan J. et al.Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19.Nat. Med. 2020; 26: 842-844Crossref PubMed Scopus (1190) Google Scholar, 8Wilk A.J. Rustagi A. Zhao N.Q. et al.A single-cell atlas of the peripheral immune response in patients with severe COVID-19.Nat. Med. 2020; 26: 1070-1076Crossref PubMed Scopus (651) Google Scholar, 9Lee J.S. Park S. Jeong H.W. et al.Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19.Sci. Immunol. 2020; 5: eabd1554Crossref PubMed Scopus (377) Google Scholar are all publicly accessible datasets deposited to the Gene Expression Omnibus (GEO) database, and most of the re-analyzed datasets have publicly accessible processed data (Figure 1D). It is apparent that data sharing promoted follow-up studies in COVID-19 research. We conclude that data sharing is necessary and strongly encouraged for ongoing and future COVID-19 studies.To facilitate further COVID-19 research, we curated a collection of COVID-19 scRNA-seq datasets (Table S2). We made notes of the sampling tissues/organs, number of recruited subjects, sequencing protocols, and accessibility of these datasets (Figure 2). Most datasets are generated using peripheral blood mononuclear cells (PBMCs) from subjects. Other common tissue or organ samples include bronchoalveolar lavage fluid (BALF), nasopharyngeal swab, cerebrospinal fluid (CSF), and lung. Most studies recruit up to 20 COVID-19 patients and 20 healthy subjects as controls, but there are a few studies with over 50 COVID-19 patients.10Ren X. Wen W. Fan X. et al.COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.Cell. 2021; 184: 5838-1913e19Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar, 11Stephenson E. Reynolds G. Botting R.A. et al.Single-cell multi-omics analysis of the immune response in COVID-19.Nat. Med. 2021; 27: 904-916Crossref PubMed Scopus (132) Google Scholar, 12Su Y. Chen D. Yuan D. et al.Multi-omics resolves a sharp disease-state shift between mild and moderate COVID-19.Cell. 2020; 183: 1479-1495.e20Abstract Full Text Full Text PDF PubMed Scopus (202) Google Scholar, 13van der Wijst M.G.P. Vazquez S.E. Hartoularos G.C. et al.UCSF COMET consortiumType I interferon autoantibodies are associated with systemic immune alterations in patients with COVID-19.Sci. Transl. Med. 2021; 13: eabh2624Crossref PubMed Scopus (52) Google Scholar For library preparation and sequencing protocols, most studies use the 10x Genomics platform. The 5′ gene expression library, including VDJ library (total 38), is preferred compared with the 3′ library (total 28). Most datasets share processed data like count matrices or R/Python objects, and 36 out of the 65 datasets share raw sequencing data.Figure 2A detailed view of 65 COVID-19 scRNA-seq datasetsShow full captionOn the top, it shows several categories: tissue/organ, number of subjects, sequencing protocol, accessibility of data format (raw or processed). On the left, it shows the datasets by the first author and publication month/year. Top bar plots show the numbers of datasets belonging to each category. For number of subjects, a blank cell indicates zero subject in this category. The top histogram shows the distribution of subjects in each category among datasets. Datasets with zero subjects indicate that they are generated from cell culture. For data format columns, green indicates the dataset being publicly accessible, while red indicates not (including dataset not shared, controlled access, and invalid accession ID). See Table S2 for details. BALF, bronchoalveolar lavage fluid; CSF, cerebrospinal fluid; NS, nasopharyngeal swab; PBMC, peripheral blood mononuclear cell.View Large Image Figure ViewerDownload Hi-res image Download (PPT)COVID-19 infection severityOne of the major goals of COVID-19 scRNA-seq research has been uncovering the genetic and cellular factors that drive COVID-19 disease severity. However, how do we measure COVID-19 infection severity? The spectrum of symptoms seen in COVID-19 infections is broad. While some patients do not display any signs of infection (asymptomatic), there are others that suffer critical or fatal infections. Infection severity can be classified into different groups, such as mild, moderate, and critical or severe infection based on symptoms. In this section, we present some of the standards used for classifying infection severity in COVID-19 scRNA-seq research.The World Health Organization (WHO) developed a COVID-19 infection severity classification scale for clinical management14WHOTherapeutics and COVID-19. WHO, 2021https://apps.who.int/iris/bitstream/handle/10665/342368/WHO-2019-nCoV-therapeutics-2021.2-eng.pdfGoogle Scholar and a nine-point WHO ordinal scale (WOS) for diagnosing COVID-19 infection severity in clinical trials.15WHOR&D Blueprint: Novel Coronavirus: COVID-19 Therapeutic Trial Synopsis. WHO, 2020https://www.who.int/docs/default-source/blue-print/covid-19-therapeutic-trial-synopsis.pdfGoogle Scholar Under the clinical guidelines, there are three distinct groups: non-severe (also called moderate), severe, and critical COVID-19. The nine-point scale categorizes the patient state as uninfected, ambulatory, mild, severe, and dead based on a score from 0 to 8, with 0 being uninfected and 8 being dead. The score is assigned according to the description of the patient (Table 1). Another infection severity standard is provided by the National Health Commission of China (NHCC).16NHCCDiagnosis and treatment protocol for novel coronavirus pneumonia (Trial Version 7).Chin. Med. J. (Engl.). 2020; 133: 1087-1095PubMed Google Scholar The NHCC standard defines four groups: mild, moderate, severe, and critical (Table 1). Our review indicates that the NHCC's standard is used the most among those COVID-19 scRNA-seq studies, followed by the WHO's standard. Interestingly, some researchers used multiple datasets where both standards of severity were used. In addition to the two most popular standards from the WHO and NHCC, other criteria, such as intensive care unit (ICU) admission and mechanical ventilation, a modified Murray score, or the National Early Warning Score (NEWS), were adopted. The modified Murray score is a four-point scale based on five clinical criteria. The NEWS is used in the UK to determine the degree of illness and need for critical care (https://www.mdcalc.com/national-early-warning-score-news).Table 1Summary of infection severity definition and number of COVID-19 studies in each definitionDefinitionaIncluding the studies using the datasets generated by other studies.WHOWOSNHCCOtherMildAbsence of any signs of severe or critical COVID-19Score 3: hospitalized, no oxygen therapyScore 4: oxygen by mask or nasal prongsMild symptoms with no sign of pneumonia on imagingOther criteriaModerateFever and respiratory symptoms with radiological findings of pneumoniaSevereAny of:(1) oxygen saturation <90% on room air(2) signs of severe respiratory distress in adults; i.e., respiratory rate >30 breaths per minute(3) presence of danger signs in children such as cyanosisScore 5: non-invasive ventilation or high-flow oxygenScore 6: intubation and mechanical ventilationScore 7: ventilation + additional organ support: pressors, RRT, ECMOAny of:(1) respiratory distress (defined by as >30 breaths per minute)(2) oxygen saturation <93% at rest(3) PaO2/FiO2 < 300 mmHg(4) over 50% progression of lung lesions within 24–48 h (diagnosed by lung imaging)CriticalAny of:(1) ARDS(2) sepsis(3) septic shock(4) requiring any ventilation support such as mechanical ventilation (any kind) and/or vasopressor therapyAny of:(1) respiratory failure and invasive mechanical ventilation(2) shock(3) multi-organ dysfunction requiring ICU admission and monitoringNo. of studies31217bOne study that used both WHO and NHCC standards is not included here.14ARDS, acute respiratory distress syndrome; ECMO, extracorporeal membrane oxygenation; NHCC, National Health Commission of China; ICU, intensive care unit; PaO2/FiO2, ratio of arterial oxygen partial pressure to fraction of inspired air; RRT, renal replacement therapy; WHO World Health Organization; WOS, WHO ordinal scales.a Including the studies using the datasets generated by other studies.b One study that used both WHO and NHCC standards is not included here. Open table in a new tab Our comparison shows no consensus agreement on which standard to use in a study. For example, Ren et al. categorized the patients based on the WHO clinical guidelines to develop a large single-cell transcriptomic atlas.10Ren X. Wen W. Fan X. et al.COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.Cell. 2021; 184: 5838-1913e19Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar They found that megakaryocytes and monocytes might contribute to the cytokine storms observed in severe infections. On the other hand, Ziegler et al. used the WHO's nine-point scale to classify scRNA-seq data from nasopharyngeal swabs. They reported that, despite a similar viral load, the epithelial cells expressed antiviral genes in mild infections, while the antiviral responses in nasal epithelia were impaired in severe infections.17Ziegler C.G.K. Miao V.N. Owings A.H. et al.Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19.Cell. 2021; 184: 4713-4733.e22Abstract Full Text Full Text PDF PubMed Scopus (67) Google ScholarIn May 2020, Liao et al. published the first COVID-19 lung tissue scRNA-seq study in Nature Medicine.7Liao M. Liu Y. Yuan J. et al.Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19.Nat. Med. 2020; 26: 842-844Crossref PubMed Scopus (1190) Google Scholar They developed a single-cell transcriptomic atlas directly from BALF tissue.7Liao M. Liu Y. Yuan J. et al.Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19.Nat. Med. 2020; 26: 842-844Crossref PubMed Scopus (1190) Google Scholar They used the NHCC infection severity classification. Liu et al. linked immune response variation to disease severity over time by performing single-cell analysis of PBMCs.18Liu C. Martins A.J. Lau W.W. et al.Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19.Cell. 2021; 184: 1836-1857.e22Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar They found that severe patients exhibited low levels of cellular inflammation early in their hospitalization, but, at 17 to 23 days after symptom onset, the inflammatory responses were significantly elevated. Hasan et al. classified infections by defining ICU admission and mechanical ventilation treatment as severe infections.19Hasan M.Z. Islam S. Matsumoto K. Kawai T. Meta-analysis of single-cell RNA-seq data reveals phenotypic switching of immune cells in severe COVID-19 patients.Comput. Biol. Med. 2021; 137: 104792Crossref PubMed Scopus (5) Google Scholar Lam et al. stratified patients by measuring lung injury with a modified Murray score that assigns a score between 0 and 4 based on five criteria.20Lam M.T.Y. Duttke S.H. Odish M.F. et al.Profiling transcription initiation in peripheral leukocytes reveals severity-associated cis-regulatory elements in critical COVID-19.bioRxiv. 2021; (Preprint at)https://doi.org/10.1101/2021.08.24.457187Crossref Scopus (0) Google Scholar Lee et al. used NEWS standard to stratify the disease status and found that type I interferons (IFNs) exacerbate inflammation in severe infections.9Lee J.S. Park S. Jeong H.W. et al.Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19.Sci. Immunol. 2020; 5: eabd1554Crossref PubMed Scopus (377) Google ScholarSince the classification systems from the WHO and NHCC have similar guidelines, we expect the results to be comparable between similar infection severities. However, the extent of the difference in the results remains unclear and warrants some future investigation. We believe a universal standard would be beneficial as standardization is important for comparison between cohorts. However, we also acknowledge that various factors such as country of origin for data collection may determine which standard is used.Altered cell proportions in COVID-19scRNA-seq enables the identification of cell populations in samples and comparison of cell proportions in different conditions. When comparing cell proportions from samples in different conditions, a higher proportion usually implies stronger local cell proliferation, transition from other cell types, or recruitment of cells from adjacent tissues like blood. On the contrary, a lower proportion usually implies greater cell death, transition to other cell types, or migration outward. Many COVID-19 scRNA-seq studies analyzed cell proportion alteration in COVID-19 patients of different severities compared with healthy controls or controls with other diseases (Figure 3; Table S3).Figure 3Major cell populations with significantly altered proportions in COVID-19Show full captionRed and blue arrows indicate cell populations with significantly altered proportions in six different tissues from moderate and severe COVID-19 patients. Double arrows indicate that the proportion of the cell populations in severe patients is significantly higher or lower than both healthy controls and moderate patients. BALF, bronchoalveolar lavage fluid; BMMC, bone marrow mononuclear cell; cDC, conventional dendritic cell; CLP, common lymphoid progenitor cell; EP, erythrocyte progenitor cell; HSC/MPP, hematopoietic stem cell/multipotent progenitor cell; LMPP, lymphoid-primed multipotent progenitor; moDC, monocyte-derived dendritic cell; moMφ, monocyte-derived macrophage; nrMφ, non-resident macrophage; PBMC, peripheral blood mononuclear cell; pDC, plasmacytoid dendritic cell; rMφ, resident macrophage.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Most studies used PBMC samples.8Wilk A.J. Rustagi A. Zhao N.Q. et al.A single-cell atlas of the peripheral immune response in patients with severe COVID-19.Nat. Med. 2020; 26: 1070-1076Crossref PubMed Scopus (651) Google Scholar, 9Lee J.S. Park S. Jeong H.W. et al.Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19.Sci. Immunol. 2020; 5: eabd1554Crossref PubMed Scopus (377) Google Scholar, 10Ren X. Wen W. Fan X. et al.COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.Cell. 2021; 184: 5838-1913e19Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar, 11Stephenson E. Reynolds G. Botting R.A. et al.Single-cell multi-omics analysis of the immune response in COVID-19.Nat. Med. 2021; 27: 904-916Crossref PubMed Scopus (132) Google Scholar,21Bernardes J.P. Mishra N. Tran F. et al.HCA Lung Biological NetworkDeutsche COVID-19 Omics Initiative DeCOILongitudinal multi-omics analyses identify responses of megakaryocytes, erythroid cells, and plasmablasts as hallmarks of severe COVID-19.Immunity. 2020; 53: 1296-1314.e9Abstract Full Text Full Text PDF PubMed Scopus (137) Google Scholar, 22Combes A.J. Courau T. Kuhn N.F. et al.Global absence and targeting of protective immune states in severe COVID-19.Nature. 2021; 591: 124-130Crossref PubMed Scopus (96) Google Scholar, 23de Cevins C. Luka M. Smith N. et al.A monocyte/dendritic cell molecular signature of SARS-CoV-2-related multisystem inflammatory syndrome in children with severe myocarditis.Med. 2021; 2: 1072-1092.e7Abstract Full Text Full Text PDF Scopus (17) Google Scholar, 24Flament H. Rouland M. Beaudoin L. et al.Outcome of SARS-CoV-2 infection is linked to MAIT cell activation and cytotoxicity.Nat. Immunol. 2021; 22: 322-335Crossref PubMed Scopus (75) Google Scholar, 25Krämer B. Knoll R. Bonaguro L. et al.Early IFN-alpha signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19.Immunity. 2021; 54: 2650-2669.e14Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar, 26Meckiff B.J. Ramírez-Suástegui C. Fajardo V. et al.Imbalance of regulatory and cytotoxic SARS-CoV-2-reactive CD4(+) T cells in COVID-19.Cell. 2020; 183: 1340-1353.e16Abstract Full Text Full Text PDF PubMed Scopus (216) Google Scholar, 27Schulte-Schrepping J. Reusch N. Paclik D. et al.Deutsche COVID-19 OMICS Initiative DeCOISevere COVID-19 Is marked by a dysregulated myeloid cell compartment.Cell. 2020; 182: 1419-1440.e23Abstract Full Text Full Text PDF PubMed Scopus (588) Google Scholar, 28Silvin A. Chapuis N. Dunsmore G. et al.Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19.Cell. 2020; 182: 1401-1418.e18Abstract Full Text Full Text PDF PubMed Scopus (368) Google Scholar, 29Szabo P.A. Dogra P. Gray J.I. et al.Longitudinal profiling of respiratory and systemic immune responses reveals myeloid cell-driven lung inflammation in severe COVID-19.Immunity. 2021; 54: 797-814.e6Abstract Full Text Full Text PDF PubMed Scopus (125) Google Scholar, 30Yang B. Fan J. Huang J. et al.Clinical and molecular characteristics of COVID-19 patients with persistent SARS-CoV-2 infection.Nat. Commun. 2021; 12: 3501Crossref PubMed Scopus (20) Google Scholar, 31Zhu L. Yang P. Zhao Y. et al.Single-cell sequencing of peripheral mononuclear cells reveals distinct immune response landscapes of COVID-19 and influenza patients.Immunity. 2020; 53: 685-696.e3Abstract Full Text Full Text PDF PubMed Scopus (150) Google Scholar, 32Bost P. De Sanctis F. Canè S. et al.Deciphering the state of immune silence in fatal COVID-19 patients.Nat. Commun. 2021; 12: 1428Crossref PubMed Scopus (53) Google Scholar, 33Xu G. Qi F. Li H. et al.The differential immune responses to COVID-19 in peripheral and lung revealed by single-cell RNA sequencing.Cell Discov. 2020; 6: 73Crossref PubMed Scopus (94) Google Scholar In general, these studies reported that neutrophils, plasma cells, and classical monocytes had significantly increased proportions in the blood of COVID-19 patients. Higher proportions of these cells were also found in severe patients than in moderate patients.7Liao M. Liu Y. Yuan J. et al.Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19.Nat. Med. 2020; 26: 842-844Crossref PubMed Scopus (1190) Google Scholar, 8Wilk A.J. Rustagi A. Zhao N.Q. et al.A single-cell atlas of the peripheral immune response in patients with severe COVID-19.Nat. Med. 2020; 26: 1070-1076Crossref PubMed Scopus (651) Google Scholar, 9Lee J.S. Park S. Jeong H.W. et al.Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19.Sci. Immunol. 2020; 5: eabd1554Crossref PubMed Scopus (377) Google Scholar,22Combes A.J. Courau T. Kuhn N.F. et al.Global absence and targeting of protective immune states in severe COVID-19.Nature. 2021; 591: 124-130Crossref PubMed Scopus (96) Google Scholar,28Silvin A. Chapuis N. Dunsmore G. et al.Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19.Cell. 2020; 182: 1401-1418.e18Abstract Full Text Full Text PDF PubMed Scopus (368) Google Scholar,29Szabo P.A. Dogra P. Gray J.I. et al.Longitudinal profiling of respiratory and systemic immune responses reveals myeloid cell-driven lung inflammation in severe COVID-19.Immunity. 2021; 54: 797-814.e6Abstract Full Text Full Text PDF PubMed Scopus (125) Google Scholar,31Zhu L. Yang P. Zhao Y. et al.Single-cell sequencing of peripheral mononuclear cells reveals distinct immune response landscapes of COVID-19 and influenza patients.Immunity. 2020; 53: 685-696.e3Abstract Full Text Full Text PDF PubMed Scopus (150) Google Scholar,32Bost P. De Sanctis F. Canè S. et al.Deciphering the state of immune silence in fatal COVID-19 patients.Nat. Commun. 2021; 12: 1428Crossref PubMed Scopus (53) Google Scholar Cell populations with a decreased proportion in the blood of COVID-19 patients included the overall T cells, natural killer (NK) cells, dendritic cells (DCs), and non-classical monocytes. Severe patients had even lower proportions of these cells than moderate patients.8Wilk A.J. Rustagi A. Zhao N.Q. et al.A single-cell atlas of the peripheral immune response in patients with severe COVID-19.Nat. Med. 2020; 26: 1070-1076Crossref PubMed Scopus (651)