COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas
Open accessAbstract
A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes are associated with distinct clinical features, including age, sex, severity, and disease stages of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within virus-positive cells. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis of and developing effective therapeutic strategies for COVID-19.
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SARS-CoV-2 infection strongly activates complement and other innate immune pathways such as TLR4 and the inflammasome, leading to increased IL-1β and IL-18 levels.
“SARS-CoV-2 infection has been documented to strongly activate complement and to activate other innate immune pathways such as TLR4 and the inflammasome, which leads to increased IL-1 β and IL-18 levels. 137-142”
From COVID-19, Myocarditis and Pericarditis (2023) · cites this paper as [142]
The Results section on peripheral cytokine storms reports higher IL1B levels in severe COVID-19 patients, and the epithelial-cell interactions section identifies a possible S100A9/A8–TLR4 inflammatory interaction. Neither passage establishes strong TLR4 activation or shows that it caused the cytokine increase; the paper reports no notable TLR4 difference in PBMCs between severe and moderate patients. The passages found do not support the attributed complement or inflammasome activation, or increased IL-18. Page numbers were unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredOther key innate cytokines elevated during COVID-19 include TNFα and IL-6, which is increased by IL-1β.
“Other key innate cytokines that are elevated during COVID-19 include TNF α and IL-6, which is increased by IL-1 β . 137-142”
From COVID-19, Myocarditis and Pericarditis (2023) · cites this paper as [142]
The Results section, “Megakaryocytes and monocyte subsets as critical peripheral sources of cytokine storms” (page unavailable), reports higher TNF and IL-6 levels in severe COVID-19 patients. It does not establish that IL-1β increases IL-6; the reported elevation of both cytokines does not show that causal relationship. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAmong cytokine profiles of spike-responsive memory T cells, production was dominated by the expression of IL-2 and IFN-γ that were highly expressed by CCR6− subsets.
“Among cytokine profiles of spike-responsive memory T cells, production was dominated by the expression of IL-2 and IFN-γ that were highly expressed by CCR6− subsets.32, 33”
From Safety and immunogenicity of the SARS-CoV-2 ARCoV mRNA vaccine in Chinese adults: a randomised, double-blind, placebo-controlled, phase 1 trial (2022) · cites this paper as [32]
The Results section on cytokine storms reports high IFNG expression in a CD8 T-cell subtype, but does not identify it as spike-responsive or CCR6−. Searches for CCR6, spike-responsive memory T cells, and IL-2/IFN-γ production found no passage establishing the claimed cytokine profile; page numbers were unavailable in our copy. (LLM, conf=0.76)
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Mao, Y., Lin, Y.-Y., Wong, N. K. Y., Volik, S., Sar, F., Collins, C., & Ester, M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae067 · Cited by 31 · see citing works
Guo, Y., Zhang, G., Yang, Q., Xie, X., Lu, Y., Cheng, X., Wang, H., Liang, J., Tang, J., Gao, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39267-x · Cited by 28 · see citing works
“Next, clonotypes were constituted by dominant heavy and light chains according to previous reports 24,25 .”
Self-citationUsing blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification
Zhong, H., Zhou, X., Uhm, H., Jiang, Y., Cao, H., Chen, Y., Mak, T. T. W., Lo, R. M. N., Wong, B. W. Y., Cheng, E. Y. L., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.13691 · Cited by 25 · see citing works
Self-citationTong, R., Luo, L., Zhao, Y., Sun, M., Li, R., Zhong, J., Chen, Y., Hu, L., Li, Z., Shi, J., et al. (2023) · Emerging Microbes & Infections · doi:10.1080/22221751.2023.2187245 · Cited by 21 · see citing works
Self-citationCellDrift: inferring perturbation responses in temporally sampled single-cell data
Jin, K., Schnell, D., Li, G., Salomonis, N., Prasath, V. B. S., Szczesniak, R., & Aronow, B. J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac324 · Cited by 11 · see citing works
Robust identification of perturbed cell types in single-cell RNA-seq data
Nicol, P. B., Paulson, D., Qian, G., Liu, X. S., Irizarry, R., & Sahu, A. D. (2023) · doi:10.1101/2023.05.06.539326 · Cited by 33 · see citing works
Self-citation
- Authors
- Ren, X., Wen, W., Fan, X., Hou, W., Su, B., Cai, P., Li, J., Liu, Y., Tang, F., Zhang, F., Yang, Y., He, J., Ma, W., He, J., Wang, P., Cao, Q., Chen, F., Chen, Y., Cheng, X., Deng, G., Deng, X., Ding, W., Feng, Y., Gan, R., Guo, C., Guo, W., He, S., Jiang, C., Liang, J., Li, Y.-M., Lin, J., Ling, Y., Liu, H., Liu, J., Liu, N., Liu, S.-Q., Luo, M., Ma, Q., Song, Q., Sun, W., Wang, G., Wang, F., Wang, Y., Wen, X., Wu, Q., Xu, G., Xie, X., Xiong, X., Xing, X., Xu, H., Yin, C., Yu, D., Yu, K., Yuan, J., Zhang, B., Zhang, P., Zhang, T., Zhao, J., Zhao, P., Zhou, J., Zhou, W., Zhong, S., Zhong, X., Zhang, S., Zhu, L., Zhu, P., Zou, B., Zou, J., Zuo, Z., Bai, F., Huang, X., Zhou, P., Jiang, Q., Huang, Z., Bei, J.-X., Wei, L., Bian, X.-W., Liu, X., Cheng, T., Li, X., Zhao, P., Wang, F.-S., Wang, H., Su, B., Zhang, Z., Qu, K., Wang, X., Chen, J., Jin, R., & Zhang, Z.
- Affiliations
- Peking University (CN)
- Second Military Medical University (CN)
- Chinese Academy of Sciences (CN)
- Shenzhen Bay Laboratory (CN)
- Shanghai Jiao Tong University (CN)
- University of Science and Technology of China (CN)
- Southern University of Science and Technology (CN)
- Guangzhou Regenerative Medicine and Health Guangdong Laboratory (CN)
- Harbin Institute of Technology (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Capital Medical University (CN)
- Yue Bei People's Hospital (CN)
- Sun Yat-sen University Cancer Center (CN)
- Shanghai Public Health Clinical Center (CN)
- Army Medical University (CN)
- Renmin Hospital of Wuhan University (CN)
- Sun Yat-sen University (CN)
- Beijing Normal University (CN)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- Tongji Hospital (CN)
- Shanghai Medical College of Fudan University (CN)
- Corresponding author
- Zhou, Penghui
- Jiang, Qinghua
- Huang, Zhiwei
- Bei, Jin-Xin
- Wei, Lai
- Bian, Xiu-Wu
- Liu, Xindong
- Cheng, Tao
- Li, Xiangpan
- Zhao, Pingsen
- Wang, Fu-Sheng
- Wang, Hongyang
- Su, Bing
- Zhang, Zheng
- Qu, Kun
- Wang, Xiaoqun
- Chen, Jiekai
- Jin, Ronghua
- Zhang, Zemin
- ORCID
- Ren, Xianwen
- Wen, Wen
- Fan, Xiaoying
- Su, Bin
- Cai, Pengfei
- Li, Jiesheng
- Liu, Yang
- Yang, Yu
- He, Jiangping
- He, Jingjing
- Wang, Pingping
- Cao, Qiqi
- Gan, Rui
- Guo, Chuang
- He, Shuai
- Ling, Yun
- Liu, Haofei
- Liu, Jianwei
- Liu, Nianping
- Luo, Meng
- Ma, Qiang
- Wang, Ying
- Wen, Xiaofeng
- Wu, Qian
- Xu, Gang
- Xiong, Xinxin
- Yu, Kezhuo
- Zhang, Biao
- Zhang, Peipei
- Zhang, Tong
- Zhao, Jincun
- Zhou, Jianfeng
- Zhang, Shuye
- Zhu, Ping
- Zou, Bin
- Bai, Fan
- Huang, Xi
- Zhou, Penghui
- Jiang, Qinghua
- Huang, Zhiwei
- Bei, Jin-Xin
- Wei, Lai
- Bian, Xiu-Wu
- Liu, Xindong
- Cheng, Tao
- Li, Xiangpan
- Zhao, Pingsen
- Wang, Fu-Sheng
- Wang, Hongyang
- Zhang, Zheng
- Qu, Kun
- Wang, Xiaoqun
- Chen, Jiekai
- Jin, Ronghua
- Zhang, Zemin
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 184
- Issue
- 7
- Pages
- 1895-1913.e19
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC7857060
- OpenAlex
- W3128961456
- Cited by
- 865
- References
- 56
- Citations by year
- 2026: 86 · 2025: 114 · 2024: 192 · 2023: 170 · 2022: 185 · 2021: 117 · 2020: 1
- FWCI
- 54.23
- Type
- journal article
- Language
- en
- Update notices
- erratum (2021-11-11)
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Ministry of Science and Technology of the People's Republic of China
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- COVID-19 pandemic, SARS-CoV-2, immune response, peripheral immune cells, immune cell subtypes, disease severity, S100A8/A9, cytokine storm, megakaryocytes, monocytes, epithelial cells, clinical features
- MeSH terms
- COVID-19, SARS-CoV-2, Adolescent, Adult, Aged, Aged, 80 and over, Child, China, Female, Humans, Male, Megakaryocytes, Middle Aged, Monocytes, RNA, Viral, Cohort Studies, Cytokines, Young Adult, Single-Cell Analysis, Transcriptome
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