Single-cell atlas of a non-human primate reveals new pathogenic mechanisms of COVID-19

  1. Lei Han
  2. Xiaoyu Wei
  3. Chuanyu Liu
  4. Giacomo Volpe
  5. Zhifeng Wang
  6. Taotao Pan
  7. Yue Yuan
  8. Ying Lei
  9. Yiwei Lai
  10. Carl Ward
  11. Yeya Yu
  12. Mingyue Wang
  13. Quan Shi
  14. Tao Wu
  15. Liang Wu
  16. Ya Liu
  17. Chunqing Wang
  18. Yuanhang Zhang
  19. Haixi Sun
  20. Hao Yu
  21. Zhenkun Zhuang
  22. Tingting Tang
  23. Yunting Huang
  24. Haorong Lu
  25. Liqin Xu
  26. Jiangshan Xu
  27. Mengnan Cheng
  28. Yang Liu
  29. Chi Wai Wong
  30. Tao Tan
  31. Weizhi Ji
  32. Patrick H. Maxwell
  33. Huanming Yang
  34. Jian Wang
  35. Shida Zhu
  36. Shiping Liu
  37. Xun Xu
  38. Yong Hou
  39. Miguel A. Esteban
  40. Longqi Liu
  41. South China Greater Bay Area-Single Cell Consortium (SC-GBA-C)

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Abstract

Stopping COVID-19 is a priority worldwide. Understanding which cell types are targeted by SARS-CoV-2 virus, whether interspecies differences exist, and how variations in cell state influence viral entry is fundamental for accelerating therapeutic and preventative approaches. In this endeavor, we profiled the transcriptome of nine tissues from a Macaca fascicularis monkey at single-cell resolution. The distribution of SARS-CoV-2 facilitators, ACE2 and TMRPSS2, in different cell subtypes showed substantial heterogeneity across lung, kidney, and liver. Through co-expression analysis, we identified immunomodulatory proteins such as IDO2 and ANPEP as potential SARS-CoV-2 targets responsible for immune cell exhaustion. Furthermore, single-cell chromatin accessibility analysis of the kidney unveiled a plausible link between IL6-mediated innate immune responses aiming to protect tissue and enhanced ACE2 expression that could promote viral entry. Our work constitutes a unique resource for understanding the physiology and pathophysiology of two phylogenetically close species, which might guide in the development of therapeutic approaches in humans.

Bullet points

  • We generated a single-cell transcriptome atlas of 9 monkey tissues to study COVID-19.

  • ACE2 + TMPRSS2 + epithelial cells of lung, kidney and liver are targets for SARS-CoV-2.

  • ACE2 correlation analysis shows IDO2 and ANPEP as potential therapeutic opportunities.

  • We unveil a link between IL6, STAT transcription factors and boosted SARS-CoV-2 entry.

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      SciScore for 10.1101/2020.04.10.022103: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      Institutional Review Board StatementThis study was approved by the Institutional Review Board on Ethics Committee of BGI ( permit no. BGI-IRB19125) .Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableFor this study, we used a six-year-old female monkey in which we profiled nine different organs (Fig. 1a).

      Table 2: Resources

      Antibodies
      SentencesResources
      We focused on IL6 because a recent clinical trial has been started that uses anti-IL6 receptor ( IL6R ) antibodies in the treatment of COVID-19 ( http://www.chictr.org.cn/showprojen.aspx ? proj=49409)
      anti-IL6 receptor ( IL6R
      suggested: None
      IL6 expression , which is higher in elderly patients and those with inflammatory conditions , is effectively targeted by anti-IL6R monoclonal antibodies leading to a more favourable disease course.
      anti-IL6R
      suggested: None
      Software and Algorithms
      SentencesResources
      Raw sequencing reads from DIPSEQ-T1 were filtered and demultiplexed using PISA ( version 0.2 ) ( https://github.com/shiquan/PISA).
      PISA
      suggested: (PISA, SCR_015749)
      Reads were aligned to Macaca_fascicularis_5.0 genome using STAR ( version 2.7.4a)46 and sorted by sambamba ( version 0.7.0 ) 47
      STAR
      suggested: (STAR, SCR_015899)
      Clustering analysis of the complete cynomolgus monkey tissue dataset was performed using Scanpy ( version 1.4)48 in a Python environment .
      Python
      suggested: (IPython, SCR_001658)
      Each tissue dataset was portrayed using the Seurat package ( version 3.1.1)49 in R environment by default parameters for filtering , data normalization , dimensionality reduction , clustering , and gene differential expression analysis .
      Seurat
      suggested: (SEURAT, SCR_007322)
      To infer the biological function of highly correlated genes ( cor > 0.6 and adjusted P value < 0.001) , we performed gene set enrichment analysis using Metascape (
      Metascape
      suggested: (Metascape, SCR_016620)

      Results from OddPub: Thank you for sharing your data.

      About SciScore

      SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

    2. Version published to 10.1101/2020.04.10.022103v2 on bioRxiv
    3. Version published to 10.1101/2020.04.10.022103v1 on bioRxiv