CoChat
Sign Up
Cited by 737COVID-19 Clinical Research StudiesSARS-CoV-2 and COVID-19 ResearchSingle-cell and spatial transcriptomics

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing

Open access
Wen, Wen, Su, Wenru, Tang, Hao, Le, Wenqing, Zhang, Xiaopeng, Zheng, Yingfeng, Liu, Xiuxing, Xie, Lihui, Li, Jianmin, Ye, Jinguo, Dong, Liwei, Cui, Xiuliang, Miao, Yushan, Wang, Depeng, Dong, Jiantao, Xiao, Chuanle, Chen, Wei, Wang, Hongyang·2020·Cell Discovery
DOI 10.1038/s41421-020-0168-9PMC PMC7197635

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

COVID-19, caused by SARS-CoV-2, has recently affected over 1,200,000 people and killed more than 60,000. The key immune cell subsets change and their states during the course of COVID-19 remain unclear. We sought to comprehensively characterize the transcriptional changes in peripheral blood mononuclear cells during the recovery stage of COVID-19 by single-cell RNA sequencing technique. It was found that T cells decreased remarkably, whereas monocytes increased in patients in the early recovery stage (ERS) of COVID-19. There was an increased ratio of classical CD14 ++ monocytes with high inflammatory gene expression as well as a greater abundance of CD14 ++ IL1β + monocytes in the ERS. CD4 + T cells and CD8 + T cells decreased significantly and expressed high levels of inflammatory genes in the ERS. Among the B cells, the plasma cells increased remarkably, whereas the naïve B cells decreased. Several novel B cell-receptor (BCR) changes were identified, such as IGHV3-23 and IGHV3-7, and isotypes (IGHV3-15, IGHV3-30, and IGKV3-11) previously used for virus vaccine development were confirmed. The strongest pairing frequencies, IGHV3-23-IGHJ4, indicated a monoclonal state associated with SARS-CoV-2 specificity, which had not been reported yet. Furthermore, integrated analysis predicted that IL-1β and M-CSF may be novel candidate target genes for inflammatory storm and that TNFSF13, IL-18, IL-2, and IL-4 may be beneficial for the recovery of COVID-19 patients. Our study provides the first evidence of an inflammatory immune signature in the ERS, suggesting COVID-19 patients are still vulnerable after hospital discharge. Identification of novel BCR signaling may lead to the development of vaccines and antibodies for the treatment of COVID-19.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41421-020-0168-9

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Study design and analysis of single immune cell profiling in COVID-19 patients. a Schematics of the experimental design for single-cell RNA (sc-RNA) sequencing. Peripheral blood mononuclear cells (PBMCs) were collected from COVID-19 patients and healthy controls (HCs) and then processed via sc-RNA, sc-BCR, and sc-TCR sequencing using the 10x-Based Genomics platform. b The heatmaps show differentially expressed genes (DEGs) upregulated in myeloid cells, NK and T cells, B cells, and other clusters of PBMCs. c t-distributed stochastic neighbor embedding (t-SNE) plot showing myeloid cells (red), NK and T cells (blue), B cells (green), and other clusters (gray) of PBMCs identified using integrated and classification analysis. d t-SNE projection of canonical markers, including CD14, CD1C, and FCGR3A for myeloid cells; CD3E, CD4, CD8A, and NCAM1 for NK and T cells; andCD19 for B cells as indicated in the legend.
Fig. 1 Study design and analysis of single immune cell profiling in COVID-19 patients. a Schematics of the experimental design for single-cell RNA (sc-RNA) sequencing. Peripheral blood mononuclear cells (PBMCs) were collected from COVID-19 patients and healthy controls (HCs) and then processed via sc-RNA, sc-BCR, and sc-TCR sequencing using the 10x-Based Genomics platform. b The heatmaps show differentially expressed genes (DEGs) upregulated in myeloid cells, NK and T cells, B cells, and other clusters of PBMCs. c t-distributed stochastic neighbor embedding (t-SNE) plot showing myeloid cells (red), NK and T cells (blue), B cells (green), and other clusters (gray) of PBMCs identified using integrated and classification analysis. d t-SNE projection of canonical markers, including CD14, CD1C, and FCGR3A for myeloid cells; CD3E, CD4, CD8A, and NCAM1 for NK and T cells; andCD19 for B cells as indicated in the legend.CC BY · PubMed Central
Fig. 2 An overview of NK and T, B, and myeloid cells in the blood of convalescent patients with COVID-19. a The t-SNE plot shows a comparison of the clustering distribution across HCs as well as early recovery stage (ERS) and late recovery stage (LRS) patients with COVID-19. b The bar plot shows the relative contributions of myeloid, NK and T, and B cells by individual samples, including five HCs, five ERS patients, and five LRS patients. c The pie chart shows the percentages of myeloid, NK and T, and B cells across HCs as well as ERS and LRS patients with COVID-19. d The heatmap shows the DEGs of myeloid cells among the HCs and the ERS and LRS COVID-19 patients. e The heatmap shows the DEGs of NK and T cells among the HCs and the ERS and LRS COVID-19 patients. f The heatmap shows the DEGs of B cells among the HCs and the ERS and LRS COVID-19 patients.
Fig. 2 An overview of NK and T, B, and myeloid cells in the blood of convalescent patients with COVID-19. a The t-SNE plot shows a comparison of the clustering distribution across HCs as well as early recovery stage (ERS) and late recovery stage (LRS) patients with COVID-19. b The bar plot shows the relative contributions of myeloid, NK and T, and B cells by individual samples, including five HCs, five ERS patients, and five LRS patients. c The pie chart shows the percentages of myeloid, NK and T, and B cells across HCs as well as ERS and LRS patients with COVID-19. d The heatmap shows the DEGs of myeloid cells among the HCs and the ERS and LRS COVID-19 patients. e The heatmap shows the DEGs of NK and T cells among the HCs and the ERS and LRS COVID-19 patients. f The heatmap shows the DEGs of B cells among the HCs and the ERS and LRS COVID-19 patients.CC BY · PubMed Central
Fig. 3 Myeloid cell subsets and their states in the blood of convalescent patients with COVID-19. a Six clusters of myeloid cells were displayed according to marker gene expression levels. Uniform manifold approximation and projection (UMAP) presentation of the heterogeneous clusters of peripheral myeloid cells. Classical CD14++ monocytes (M1), non-classical CD16++ (FCGR3A) CD14−/+ monocytes (M2), intermediate CD14++ CD16+ monocytes (M3), CD1C+ cDC2 (M4), CLEC9A+ cDC1 (M5), and pDC (CLEC4C+CD123+) (M6). b The UAMP plot shows subtype-specific marker genes of myeloid cells, includingCD14, FCGR3A, CD1C, CLEC9A, CLEC4C, and IL-1β. c Bar chart of the relative frequencies of the six sub-clusters of myeloid cells and three sub-clusters of monocytes across the HCs and the ERS and LRS patients. d The heatmap shows the top DEGs between COVID-19 patients and HCs in CD14++ monocytes. e Volcano plot of fold change between COVID-19 patients and HCs in CD14++ monocytes. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure. f The UAMP plot shows that IL-1β was highly expressed in the ERS patients vs. the LRS patients and HCs in myeloid cells. g GO BP enrichment analysis of the DEGs of CD14++ monocytes upregulated in COVID-19 patients. P value was derived by a hypergeometric test.
Fig. 3 Myeloid cell subsets and their states in the blood of convalescent patients with COVID-19. a Six clusters of myeloid cells were displayed according to marker gene expression levels. Uniform manifold approximation and projection (UMAP) presentation of the heterogeneous clusters of peripheral myeloid cells. Classical CD14++ monocytes (M1), non-classical CD16++ (FCGR3A) CD14−/+ monocytes (M2), intermediate CD14++ CD16+ monocytes (M3), CD1C+ cDC2 (M4), CLEC9A+ cDC1 (M5), and pDC (CLEC4C+CD123+) (M6). b The UAMP plot shows subtype-specific marker genes of myeloid cells, includingCD14, FCGR3A, CD1C, CLEC9A, CLEC4C, and IL-1β. c Bar chart of the relative frequencies of the six sub-clusters of myeloid cells and three sub-clusters of monocytes across the HCs and the ERS and LRS patients. d The heatmap shows the top DEGs between COVID-19 patients and HCs in CD14++ monocytes. e Volcano plot of fold change between COVID-19 patients and HCs in CD14++ monocytes. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure. f The UAMP plot shows that IL-1β was highly expressed in the ERS patients vs. the LRS patients and HCs in myeloid cells. g GO BP enrichment analysis of the DEGs of CD14++ monocytes upregulated in COVID-19 patients. P value was derived by a hypergeometric test.CC BY · PubMed Central
Fig. 4 Characterization of T and NK cell responses in the blood of recovered COVID-19 patients. a Ten sub-clusters of NK and T lymphocytes were identified. The UMAP plot shows the clustering of T and NK cells. CD56+CD16-NK cells (NK1), C56-CD16+ NK cells (NK2), naïve CD4+ T cells (T1), central memory CD4+ T cells (T2), effector memory CD4+ T cells (T3), regulatory T cells (T4), naïve CD8+ T cells (T5), effector memory CD8+ T cells (T6), cytotoxic CD8+ T cells (T7), and proliferating T cells (T8). b UAMP plot showing subtype-specific marker genes of NK and T cells including CD4, CD8A, NCAM1, CCR7, GZMK, GNLY, MKI67, FCGR3A, and IL-1β. c The bar plot shows the percentages of four sub-clusters of NK and T cells, four sub-clusters of CD4+ T cells, and three sub-clusters of CD4+ T cells among the HCs and the ERS and LRS patients. d Heatmap of CD4+ T cells showing the DEGs between the COVID-19 patients and HCs.e The volcano plot shows the DEGs of CD4+ T cells between the COVID-19 patients and HCs. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure. f GO BP enrichment analysis of the DEGs of CD4+ T cells upregulated in the COVID-19 patients. P value was derived by a hypergeometric test. g The pie plot shows the TCR clone differences across the HCs and the ERS and LRS patients. h UAMP shows expanded TCR clones (n ≥ 2) in the ERS and LRS patients. i The volcano plot shows the DEGs of CD8+ CTLs between the COVID-19 ERS group and HCs. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure.
Fig. 4 Characterization of T and NK cell responses in the blood of recovered COVID-19 patients. a Ten sub-clusters of NK and T lymphocytes were identified. The UMAP plot shows the clustering of T and NK cells. CD56+CD16-NK cells (NK1), C56-CD16+ NK cells (NK2), naïve CD4+ T cells (T1), central memory CD4+ T cells (T2), effector memory CD4+ T cells (T3), regulatory T cells (T4), naïve CD8+ T cells (T5), effector memory CD8+ T cells (T6), cytotoxic CD8+ T cells (T7), and proliferating T cells (T8). b UAMP plot showing subtype-specific marker genes of NK and T cells including CD4, CD8A, NCAM1, CCR7, GZMK, GNLY, MKI67, FCGR3A, and IL-1β. c The bar plot shows the percentages of four sub-clusters of NK and T cells, four sub-clusters of CD4+ T cells, and three sub-clusters of CD4+ T cells among the HCs and the ERS and LRS patients. d Heatmap of CD4+ T cells showing the DEGs between the COVID-19 patients and HCs.e The volcano plot shows the DEGs of CD4+ T cells between the COVID-19 patients and HCs. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure. f GO BP enrichment analysis of the DEGs of CD4+ T cells upregulated in the COVID-19 patients. P value was derived by a hypergeometric test. g The pie plot shows the TCR clone differences across the HCs and the ERS and LRS patients. h UAMP shows expanded TCR clones (n ≥ 2) in the ERS and LRS patients. i The volcano plot shows the DEGs of CD8+ CTLs between the COVID-19 ERS group and HCs. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure.CC BY · PubMed Central
Fig. 5 Characterization of single-cell B cells in COVID-19 patients. a Four clusters of B cells were identified. The UMAP plot shows the clustering of B cells. Naïve B cells (B1), memory B cells (B2), immature B cells (B3), and plasma cells (B4). b UAMP plot showing subtype-specific marker genes of B cells, including MME, IL4R, CD38, CD27, MZB1, and IGHA1. c The bar plot shows the percentages of B clusters across the HCs and the ERS and LRS patients. d The volcano plot shows the DEGs of MPB cells between the COVID-19 patients and HCs. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure. e The violin plot shows thatMZB1, IGHG1, andIGHA1 were highly expressed in COVID-19 patients vs. the HCs in the B cell sub-clusters. f GO BP enrichment analysis of the DEGs of MPB cells between the COVID-19 patients vs. the HCs. P value was derived by a hypergeometric test.g The bar plot shows the relative percentage of each isotype by individual sample.h The bar plot shows the ratio of (IgA+IgG+IgE) to (IgD+IgM) among the HCs and the ERS and LRS patients. Statistical analysis used One-Way ANOVA test. Values are mean ± SD. *P < 0.05, **P < 0.01.
Fig. 5 Characterization of single-cell B cells in COVID-19 patients. a Four clusters of B cells were identified. The UMAP plot shows the clustering of B cells. Naïve B cells (B1), memory B cells (B2), immature B cells (B3), and plasma cells (B4). b UAMP plot showing subtype-specific marker genes of B cells, including MME, IL4R, CD38, CD27, MZB1, and IGHA1. c The bar plot shows the percentages of B clusters across the HCs and the ERS and LRS patients. d The volcano plot shows the DEGs of MPB cells between the COVID-19 patients and HCs. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure. e The violin plot shows thatMZB1, IGHG1, andIGHA1 were highly expressed in COVID-19 patients vs. the HCs in the B cell sub-clusters. f GO BP enrichment analysis of the DEGs of MPB cells between the COVID-19 patients vs. the HCs. P value was derived by a hypergeometric test.g The bar plot shows the relative percentage of each isotype by individual sample.h The bar plot shows the ratio of (IgA+IgG+IgE) to (IgD+IgM) among the HCs and the ERS and LRS patients. Statistical analysis used One-Way ANOVA test. Values are mean ± SD. *P < 0.05, **P < 0.01.CC BY · PubMed Central
Fig. 6 Expanded BCR clones and biased usage of VDJ genes observed in the COVID-19 patients. a The UMAP plot shows the B cell expansion status in the HCs and the ERS and LRS COVID-19 patients. b The bar plots show the clonal expansion status of B cells in peripheral blood from each individual sample. The number of color blocks represents the complexity of the clonal states. c Separate analysis of HC, ERS and LRS group by percentages of maximum clones revealed an enrichment of highly expanded clones (defined as comprising 10% or more of all BCR sequences; indicated by dotted line) in each group. None of healthy subjects had a highly expanded clone, versus four out of five patients in ERS, one out of five patients in LRS. Values are mean ± SD. d The volcano plot shows the DEGs of expanded vs. non-expanded B cells in ERS and LRS patients. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure. e The bar plots show specific IGHV, IGKV, IGLV usage in the HCs and the ERS and LRS COVID-19 patients. f Heatmap showing IGH rearrangements in peripheral blood samples from ERS group.
Fig. 6 Expanded BCR clones and biased usage of VDJ genes observed in the COVID-19 patients. a The UMAP plot shows the B cell expansion status in the HCs and the ERS and LRS COVID-19 patients. b The bar plots show the clonal expansion status of B cells in peripheral blood from each individual sample. The number of color blocks represents the complexity of the clonal states. c Separate analysis of HC, ERS and LRS group by percentages of maximum clones revealed an enrichment of highly expanded clones (defined as comprising 10% or more of all BCR sequences; indicated by dotted line) in each group. None of healthy subjects had a highly expanded clone, versus four out of five patients in ERS, one out of five patients in LRS. Values are mean ± SD. d The volcano plot shows the DEGs of expanded vs. non-expanded B cells in ERS and LRS patients. P values were calculated using a paired, two-sided Wilcoxon test and FDR corrected using the Benjamini–Hochberg procedure. e The bar plots show specific IGHV, IGKV, IGLV usage in the HCs and the ERS and LRS COVID-19 patients. f Heatmap showing IGH rearrangements in peripheral blood samples from ERS group.CC BY · PubMed Central
Fig. 7 Cell-to-cell communication among immune cells in the COVID-19 patients. a T cell-monocyte interactions, B cell-monocyte interactions, B cell-T cell interactions, and monocyte-T cell interactions in the ERS COVID-19 patients. b DC-T cell interactions, DC-B cell interactions, and T cell-B cell interactions in the LRS COVID-19 patients. c, d Schematics illustrating the key innate and adaptive immune cell functional alterations and main differences in cell-cell communications in the ERS (c) and LRS (d) COVID-19 patients.
Fig. 7 Cell-to-cell communication among immune cells in the COVID-19 patients. a T cell-monocyte interactions, B cell-monocyte interactions, B cell-T cell interactions, and monocyte-T cell interactions in the ERS COVID-19 patients. b DC-T cell interactions, DC-B cell interactions, and T cell-B cell interactions in the LRS COVID-19 patients. c, d Schematics illustrating the key innate and adaptive immune cell functional alterations and main differences in cell-cell communications in the ERS (c) and LRS (d) COVID-19 patients.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 49 references

Cited by

Sorted by citations

Items 201–300 of 737

Related

See all

Metadata

Authors
Wen, W., Su, W., Tang, H., Le, W., Zhang, X., Zheng, Y., Liu, X., Xie, L., Li, J., Ye, J., Dong, L., Cui, X., Miao, Y., Wang, D., Dong, J., Xiao, C., Chen, W., & Wang, H.
Affiliations
  • Second Military Medical University (CN)
  • Sun Yat-sen University (CN)
  • Wuhan Hankou Hospital (CN)
Corresponding author
  • Chen, Wei
  • Wang, Hongyang
ORCID
Venue
Cell Discovery
Publisher
Springer Science and Business Media LLC
Published
2020-05-04
Volume
6
Issue
1
Pages
31-31
ISSN
2056-5968 (ISSN-L)
DOI
10.1038/s41421-020-0168-9
PMC
PMC7197635
OpenAlex
W3021575042
Cited by
730
References
49
Citations by year
2026: 14 · 2025: 37 · 2024: 53 · 2023: 72 · 2022: 138 · 2021: 222 · 2020: 194
FWCI
20.71
Type
journal article
Language
en
Update notices
correction (2020-06-20)
Open access
Licence
Locations
  • Cell Discovery (publishedVersion, cc-by)
  • medRxiv (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Natural Science Foundation of China (81830054, 81988101,91859205, 81722034, 81670015, 81530028,81721003)
  • Ministry of Science and Technology of the People's Republic of China (2018ZX09101002, 2017ZX100203205)
  • Shanghai Municipal Human Resources and Social Security Bureau (2019PJD059)
  • Natural Science Foundation of Guangdong Province (2016A030306006, 2020B1515020057)
Topics
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
COVID-19 pandemic, SARS-CoV-2, immune profiling, recovery stages, peripheral blood mononuclear cells, T cell depletion, monocytes, CD14+ monocytes, IL-1β, B cells, plasma cells, B cell receptor, IGHV3-23, inflammatory storm, macrophage colony-stimulating factor, cytokines, vaccine development

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review