COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas

Ren, Xianwen; Wen, Wen; Fan, Xiaoying; Hou, Wei; Su, Bin; Cai, Pengfei; Li, Jiesheng; Liu, Yang; Tang, Fei; Zhang, Fan; Yu, Yang; He, Jun; Ma, Wei; He, Jifeng; Wang, Pingping; Cao, Qiqi; Chen, Fangjin; Chen, Yuqing; Cheng, Xiaoyan; Deng, Guohong; Deng, Xianwen; Ding, Wei; Feng, Yingmei; Gan, Rui; Guo, Chunyu; Guo, Wenbin; He, Shaoming; Jiang, Chen; Liang, Juanran; Li, Yimin; Ji, Lin; Ling, Yun; Liu, Haofei; Liu, Jianwei; Liu, Nianping; Liu, Shu-Qiang; Luo, Meng; Ma, Qiang; Song, Qibing; Sun, Wujianan; Wang, Gaoxiang; Wang, Feng; Wang, Ying; Wen, Xintian; Qian, Wei; Xu, Gang; Xie, Xuhui; Xiong, Xinxin; Xing, Xudong; Xu, Hao; Yin, Chonghai; Yu, Daquan; Yu, Kezhuo; Yuan, Jian; Zhang, Biao; Zhang, Peipei; Zhang, Tong; Zhao, Jincun; Zhao, Peidong; Zhou, Jianfeng; Zhou, Wei; Zhong, Shengwen; Zhong, Xiaosong; Zhang, Shuye; Zhu, Lin; Zhu, Ping; Zhou, Bin; Zou, Jiahua; Zuo, Zengtao; Fan, Bin; 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, Fusheng; Wang, Hongyang; Shi, Bing; Zhang, Zheng; Qu, Kun; Wang, Xiaoqun; Chen, Jiekai; Jin, Ronghua; Zhang, Zemin

doi:10.1016/j.cell.2021.10.023

Cited by 149 publications

(242 citation statements)

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“…5F). Interestingly, given the important role of type I interferon (IFN-I) in COVID-19 [18], we also found decreased expression of multiple IFN-I-inducible genes including MX1, OAS3, and OASL (Fig. 5K-M).…”

Section: Whole Blood Rna Sequencing Reveals Significant Perturbations...mentioning

confidence: 72%

“…Single-cell RNA sequencing (scRNA-Seq) of peripheral blood mononuclear cells (PMBC) from a small (n = 10) cohort of patients that were 7-14 days post-recovery also found an increased ratio of classical CD14 + monocytes with high inflammatory gene expression, decreased CD4 + and CD8 + T cells, and significantly increased plasma B cells [17]. scRNA-Seq profiling of PBMC gene expression in a larger cohort of recovering individuals (n = 95) found those with severe disease (n = 36) had decreased plasmacytoid dendritic cells (pDCs) and increased levels of proliferative effector memory CD8 + T cells, relative to healthy controls [18]. A potential limitation of this study, however, was that samples from recovered individuals were not collected at uniform timepoints during recovery, instead samples were collected between 9 and 126 days post-infection (on average 44.5 days).…”

Section: Introductionmentioning

confidence: 89%

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Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection

Ryan

Hope

Masavuli

et al. 2022

BMC Med

199

196

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Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious respiratory virus which is responsible for the coronavirus disease 2019 (COVID-19) pandemic. It is increasingly clear that recovered individuals, even those who had mild COVID-19, can suffer from persistent symptoms for many months after infection, a condition referred to as “long COVID”, post-acute sequelae of COVID-19 (PASC), post-acute COVID-19 syndrome, or post COVID-19 condition. However, despite the plethora of research on COVID-19, relatively little is known about the molecular underpinnings of these long-term effects. Methods We have undertaken an integrated analysis of immune responses in blood at a transcriptional, cellular, and serological level at 12, 16, and 24 weeks post-infection (wpi) in 69 patients recovering from mild, moderate, severe, or critical COVID-19 in comparison to healthy uninfected controls. Twenty-one of these patients were referred to a long COVID clinic and > 50% reported ongoing symptoms more than 6 months post-infection. Results Anti-Spike and anti-RBD IgG responses were largely stable up to 24 wpi and correlated with disease severity. Deep immunophenotyping revealed significant differences in multiple innate (NK cells, LD neutrophils, CXCR3+ monocytes) and adaptive immune populations (T helper, T follicular helper, and regulatory T cells) in convalescent individuals compared to healthy controls, which were most strongly evident at 12 and 16 wpi. RNA sequencing revealed significant perturbations to gene expression in COVID-19 convalescents until at least 6 months post-infection. We also uncovered significant differences in the transcriptome at 24 wpi of convalescents who were referred to a long COVID clinic compared to those who were not. Conclusions Variation in the rate of recovery from infection at a cellular and transcriptional level may explain the persistence of symptoms associated with long COVID in some individuals.

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Section: Whole Blood Rna Sequencing Reveals Significant Perturbations...mentioning

confidence: 72%

Section: Introductionmentioning

confidence: 89%

Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection

Ryan

Hope

Masavuli

et al. 2022

BMC Med

199

196

View full text Add to dashboard Cite

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“…The accumulating lines of evidence [29, 54] have suggested that subsets of monocytes and megakaryocytes might be the major resources of inflammatory storm. We sought to examine whether CCR1 + CD16+monocytes and ABO + megakaryocytes play more important roles in cytokine storm among severe patients.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, we downloaded four independent scRNA-seq datasets on COVID-19 in PBMC and BALF from the ArrayExpress database (Dataset #1, the accession number is E-MTAB-9357 from Su et al study [10]), and the Gene Expression Omnibus (GEO) database (Dataset #2, the accession number is GSE149689 from Lee et al study [18], Dataset #3, the accession number is GSE150861 from Guo et al study [11], and Dataset #4, the accession number is GSE158055 [29]). The first dataset contained 270 peripheral blood samples including 254 samples with different COVID-19 severity (i.e., mild N = 109, moderate N = 102, and severe N = 50) and 16 healthy controls for scRNA-seq analysis.…”

Section: Methodsmentioning

confidence: 99%

“…We leveraged cell state scores to assess the immunological degree of each immune cell type expressed a pre-curated expression gene set [11, 14, 29]. The cell state scores (CTS) were calculated based on the average expression of genes from the pre-curated gene set in the respective cell with the following formula: CTS k ( m ) = average(RE(GS k, m )) - average(RE(GS n , m)), where GS k is a pre-defined gene set k in a given cell m , and GS n is a control gene set that was randomly chosen on the basis of aggregate expression levels bins, which obtain a comparable distribution of expression levels and over size to that of the pre-curated gene set.…”

Section: Methodsmentioning

confidence: 99%

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Single cell sequencing analysis uncovers genetics-influenced CD16+monocytes and memory CD8+T cells involved in severe COVID-19

Qiu

Deng

et al. 2022

Preprint

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BackgroundUnderstanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown.MethodsWe constructed a powerful computational framework to characterize the host genetics-influenced immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets (N = 606,534 cells).ResultsWe found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly-expressed genetics-risk genes increased with the severity of COVID-19. Three cell-subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were specifically expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significant up-regulated genes including S100A12, S100A8, S100A9, and IFITM1 confer higher risk to the cytokine storms among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality of multiple immunologic features, including elevation of proliferation, migration, and chemotaxis. Moreover, we observed a prominent increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues, and elevated interactions with epithelial cells could contribute to enhance the resident to lung airway for against COVID-19 infection.ConclusionsWe uncover a major genetics-modulated immunological shift between mild and severe infection, including an increase in up-regulated genetic-risk genes, excessive secreted inflammatory cytokines, and functional immune cell subsets contributing high risk to severity, which provides novel insights in parsing the host genetics-influenced immune cells for severe COVID-19.

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Single‐cell RNA sequencing technologies and applications: A brief overview

Jovic

Liang

Zeng

et al. 2022

Clinical & Translational Med

402

204

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Single‐cell RNA sequencing (scRNA‐seq) technology has become the state‐of‐the‐art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA‐seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA‐seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA‐seq can provide unique information for better understanding health and diseases. One important application of the scRNA‐seq technology is to build a better and high‐resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.

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COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas

Cited by 149 publications

References 0 publications

Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection

Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection

Single cell sequencing analysis uncovers genetics-influenced CD16+monocytes and memory CD8+T cells involved in severe COVID-19

Single‐cell RNA sequencing technologies and applications: A brief overview

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