tetano
Editor, Senior Moderator
Cell
. 2021 Feb 3;S0092-8674(21)00148-3.
doi: 10.1016/j.cell.2021.01.053. Online ahead of print.
COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas
Xianwen Ren[SUP] 1 [/SUP], Wen Wen[SUP] 2 [/SUP], Xiaoying Fan[SUP] 3 [/SUP], Wenhong Hou[SUP] 4 [/SUP], Bin Su[SUP] 5 [/SUP], Pengfei Cai[SUP] 6 [/SUP], Jiesheng Li[SUP] 1 [/SUP], Yang Liu[SUP] 7 [/SUP], Fei Tang[SUP] 1 [/SUP], Fan Zhang[SUP] 8 [/SUP], Yu Yang[SUP] 1 [/SUP], Jiangping He[SUP] 9 [/SUP], Wenji Ma[SUP] 10 [/SUP], Jingjing He[SUP] 11 [/SUP], Pingping Wang[SUP] 12 [/SUP], Qiqi Cao[SUP] 2 [/SUP], Fangjin Chen[SUP] 13 [/SUP], Yuqing Chen[SUP] 1 [/SUP], Xuelian Cheng[SUP] 14 [/SUP], Guohong Deng[SUP] 15 [/SUP], Xilong Deng[SUP] 16 [/SUP], Wenyu Ding[SUP] 17 [/SUP], Yingmei Feng[SUP] 5 [/SUP], Rui Gan[SUP] 8 [/SUP], Chuang Guo[SUP] 6 [/SUP], Weiqiang Guo[SUP] 18 [/SUP], Shuai He[SUP] 11 [/SUP], Chen Jiang[SUP] 6 [/SUP], Juanran Liang[SUP] 19 [/SUP], Yi-Min Li[SUP] 20 [/SUP], Jun Lin[SUP] 6 [/SUP], Yun Ling[SUP] 21 [/SUP], Haofei Liu[SUP] 22 [/SUP], Jianwei Liu[SUP] 9 [/SUP], Nianping Liu[SUP] 6 [/SUP], Shu-Qiang Liu[SUP] 11 [/SUP], Meng Luo[SUP] 12 [/SUP], Qiang Ma[SUP] 10 [/SUP], Qibing Song[SUP] 23 [/SUP], Wujianan Sun[SUP] 6 [/SUP], GaoXiang Wang[SUP] 24 [/SUP], Feng Wang[SUP] 25 [/SUP], Ying Wang[SUP] 25 [/SUP], Xiaofeng Wen[SUP] 19 [/SUP], Qian Wu[SUP] 26 [/SUP], Gang Xu[SUP] 7 [/SUP], Xiaowei Xie[SUP] 14 [/SUP], Xinxin Xiong[SUP] 11 [/SUP], Xudong Xing[SUP] 27 [/SUP], Hao Xu[SUP] 6 [/SUP], Chonghai Yin[SUP] 10 [/SUP], Dongdong Yu[SUP] 23 [/SUP], Kezhuo Yu[SUP] 1 [/SUP], Jin Yuan[SUP] 7 [/SUP], Biao Zhang[SUP] 14 [/SUP], Peipei Zhang[SUP] 28 [/SUP], Tong Zhang[SUP] 5 [/SUP], Jincun Zhao[SUP] 20 [/SUP], Peidong Zhao[SUP] 29 [/SUP], Jianfeng Zhou[SUP] 24 [/SUP], Wei Zhou[SUP] 9 [/SUP], Sujuan Zhong[SUP] 26 [/SUP], Xiaosong Zhong[SUP] 30 [/SUP], Shuye Zhang[SUP] 31 [/SUP], Lin Zhu[SUP] 6 [/SUP], Ping Zhu[SUP] 14 [/SUP], Bin Zou[SUP] 19 [/SUP], Jiahua Zou[SUP] 32 [/SUP], Zengtao Zuo[SUP] 10 [/SUP], Fan Bai[SUP] 1 [/SUP], Xi Huang[SUP] 33 [/SUP], Penghui Zhou[SUP] 34 [/SUP], Qinghua Jiang[SUP] 35 [/SUP], Zhiwei Huang[SUP] 36 [/SUP], Jin-Xin Bei[SUP] 37 [/SUP], Lai Wei[SUP] 38 [/SUP], Xiu-Wu Bian[SUP] 39 [/SUP], Xindong Liu[SUP] 40 [/SUP], Tao Cheng[SUP] 41 [/SUP], Xiangpan Li[SUP] 42 [/SUP], Pingsen Zhao[SUP] 43 [/SUP], Fu-Sheng Wang[SUP] 44 [/SUP], Hongyang Wang[SUP] 45 [/SUP], Bing Su[SUP] 46 [/SUP], Zheng Zhang[SUP] 47 [/SUP], Kun Qu[SUP] 48 [/SUP], Xiaoqun Wang[SUP] 49 [/SUP], Jiekai Chen[SUP] 50 [/SUP], Ronghua Jin[SUP] 51 [/SUP], Zemin Zhang[SUP] 52 [/SUP]
Affiliations
Abstract
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.
Keywords: B cell receptor sequencing; COVID-19; SARS-CoV-2; T cell receptor sequencing; cell-cell interaction; cytokine storm; host cell range; ligand-receptor interaction; single-cell RNA-seq; single-cell transcriptomics.
. 2021 Feb 3;S0092-8674(21)00148-3.
doi: 10.1016/j.cell.2021.01.053. Online ahead of print.
COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas
Xianwen Ren[SUP] 1 [/SUP], Wen Wen[SUP] 2 [/SUP], Xiaoying Fan[SUP] 3 [/SUP], Wenhong Hou[SUP] 4 [/SUP], Bin Su[SUP] 5 [/SUP], Pengfei Cai[SUP] 6 [/SUP], Jiesheng Li[SUP] 1 [/SUP], Yang Liu[SUP] 7 [/SUP], Fei Tang[SUP] 1 [/SUP], Fan Zhang[SUP] 8 [/SUP], Yu Yang[SUP] 1 [/SUP], Jiangping He[SUP] 9 [/SUP], Wenji Ma[SUP] 10 [/SUP], Jingjing He[SUP] 11 [/SUP], Pingping Wang[SUP] 12 [/SUP], Qiqi Cao[SUP] 2 [/SUP], Fangjin Chen[SUP] 13 [/SUP], Yuqing Chen[SUP] 1 [/SUP], Xuelian Cheng[SUP] 14 [/SUP], Guohong Deng[SUP] 15 [/SUP], Xilong Deng[SUP] 16 [/SUP], Wenyu Ding[SUP] 17 [/SUP], Yingmei Feng[SUP] 5 [/SUP], Rui Gan[SUP] 8 [/SUP], Chuang Guo[SUP] 6 [/SUP], Weiqiang Guo[SUP] 18 [/SUP], Shuai He[SUP] 11 [/SUP], Chen Jiang[SUP] 6 [/SUP], Juanran Liang[SUP] 19 [/SUP], Yi-Min Li[SUP] 20 [/SUP], Jun Lin[SUP] 6 [/SUP], Yun Ling[SUP] 21 [/SUP], Haofei Liu[SUP] 22 [/SUP], Jianwei Liu[SUP] 9 [/SUP], Nianping Liu[SUP] 6 [/SUP], Shu-Qiang Liu[SUP] 11 [/SUP], Meng Luo[SUP] 12 [/SUP], Qiang Ma[SUP] 10 [/SUP], Qibing Song[SUP] 23 [/SUP], Wujianan Sun[SUP] 6 [/SUP], GaoXiang Wang[SUP] 24 [/SUP], Feng Wang[SUP] 25 [/SUP], Ying Wang[SUP] 25 [/SUP], Xiaofeng Wen[SUP] 19 [/SUP], Qian Wu[SUP] 26 [/SUP], Gang Xu[SUP] 7 [/SUP], Xiaowei Xie[SUP] 14 [/SUP], Xinxin Xiong[SUP] 11 [/SUP], Xudong Xing[SUP] 27 [/SUP], Hao Xu[SUP] 6 [/SUP], Chonghai Yin[SUP] 10 [/SUP], Dongdong Yu[SUP] 23 [/SUP], Kezhuo Yu[SUP] 1 [/SUP], Jin Yuan[SUP] 7 [/SUP], Biao Zhang[SUP] 14 [/SUP], Peipei Zhang[SUP] 28 [/SUP], Tong Zhang[SUP] 5 [/SUP], Jincun Zhao[SUP] 20 [/SUP], Peidong Zhao[SUP] 29 [/SUP], Jianfeng Zhou[SUP] 24 [/SUP], Wei Zhou[SUP] 9 [/SUP], Sujuan Zhong[SUP] 26 [/SUP], Xiaosong Zhong[SUP] 30 [/SUP], Shuye Zhang[SUP] 31 [/SUP], Lin Zhu[SUP] 6 [/SUP], Ping Zhu[SUP] 14 [/SUP], Bin Zou[SUP] 19 [/SUP], Jiahua Zou[SUP] 32 [/SUP], Zengtao Zuo[SUP] 10 [/SUP], Fan Bai[SUP] 1 [/SUP], Xi Huang[SUP] 33 [/SUP], Penghui Zhou[SUP] 34 [/SUP], Qinghua Jiang[SUP] 35 [/SUP], Zhiwei Huang[SUP] 36 [/SUP], Jin-Xin Bei[SUP] 37 [/SUP], Lai Wei[SUP] 38 [/SUP], Xiu-Wu Bian[SUP] 39 [/SUP], Xindong Liu[SUP] 40 [/SUP], Tao Cheng[SUP] 41 [/SUP], Xiangpan Li[SUP] 42 [/SUP], Pingsen Zhao[SUP] 43 [/SUP], Fu-Sheng Wang[SUP] 44 [/SUP], Hongyang Wang[SUP] 45 [/SUP], Bing Su[SUP] 46 [/SUP], Zheng Zhang[SUP] 47 [/SUP], Kun Qu[SUP] 48 [/SUP], Xiaoqun Wang[SUP] 49 [/SUP], Jiekai Chen[SUP] 50 [/SUP], Ronghua Jin[SUP] 51 [/SUP], Zemin Zhang[SUP] 52 [/SUP]
Affiliations
- PMID: 33657410
- PMCID: PMC7857060
- DOI: 10.1016/j.cell.2021.01.053
Abstract
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.
Keywords: B cell receptor sequencing; COVID-19; SARS-CoV-2; T cell receptor sequencing; cell-cell interaction; cytokine storm; host cell range; ligand-receptor interaction; single-cell RNA-seq; single-cell transcriptomics.