Screening of cell-virus, cell-cell, gene-gene cross-talks among kingdoms of life at single cell resolution

  1. Dongsheng Chen
  2. Zhihua Ou
  3. Jiacheng Zhu
  4. Peiwen Ding
  5. Haoyu Wang
  6. Lihua Luo
  7. Xiangning Ding
  8. Tianming Lan
  9. Weiying Wu
  10. Yuting Yuan
  11. Wendi Wu
  12. Jiaying Qiu
  13. Yixin Zhu
  14. Yi Jia
  15. Yanan Wei
  16. Qiuyu Qin
  17. Runchu Li
  18. Chengcheng Sun
  19. Wandong Zhao
  20. Zhiyuan Lv
  21. Mingyi Pu
  22. Shangchen Yang
  23. Ashley Chang
  24. Xiaofeng Wei
  25. Fengzhen Chen
  26. Tao Yang
  27. Zhenyong Wei
  28. Fan Yang
  29. Yuejiao Li
  30. Yan Hua
  31. Huan Liu

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Abstract

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) issued a significant and urgent threat to global health. The exact animal origin of SARS-CoV-2 remains obscure and understanding its host range is vital for preventing interspecies transmission. Previously, we have assessed the target cell profiles of SARS-CoV-2 in pets, livestock, poultry and wild animals. Herein, we expand this investigation to a wider range of animal species and viruses to provide a comprehensive source for large-scale screening of potential virus hosts. Single cell atlas for several mammalian species (alpaca, hamster, hedgehog, chinchilla etc.), as well as comparative atlas for lung, brain and peripheral blood mononuclear cells (PBMC) for various lineages of animals were constructed, from which we systemically analyzed the virus entry factors for 113 viruses over 20 species from mammalians, birds, reptiles, amphibians and invertebrates. Conserved cellular connectomes and regulomes were also identified, revealing the fundamental cell-cell and gene-gene cross-talks between these species. Overall, our study could help identify the potential host range and tissue tropism of SARS-CoV-2 and a diverse set of viruses and reveal the host-virus co-evolution footprints.

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  1. ScreenIT

    SciScore for 10.1101/2021.08.13.456190: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Differential expression analysis: Differentially expressed genes (DEGs) were identified by “FindAllMarkers” function in Seurat. For DEGs of each cluster, we applied clusterProfiler Package for GO enrichment analysis.
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    The expression of receptors in all cell types were displayed in dot plot using R package ggplot2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    However, there are several limitations present in our study and the results should be interpreted cautiously. Firstly, we were only able to obtain scRNA data for tissues from 1-3 organs of each animal species in this study, due to difficulty in sampling and experiments. A more comprehensive characterization of the disease related organs may facilitate the interpretation of clinical symptoms induced by infections of various viruses in the animals. Secondly, the dataset of each organ for each species lacked biological nor technical replicates. Our results might be influenced by sampling bias. Thirdly, the expression profiles of the receptors were characterized based on the RNA sequencing data, not in their mature protein form with appropriate post-translational modifications. Some viruses, such as influenza A viruses, utilize sialic acid receptors and the sialyl linkage types to determine the differential binding specificity of avian and human adapted strains12. Although the transcriptome data could reveal the presence of the proteins associated with the receptors, the exact expression profiles of such kind of receptors could not be told by this data. Fourthly, we have only characterized the known receptor genes, other potential alternative receptors of the viruses were not included, but their distribution could also affect the tissue tropism of the viruses. Fifthly, both the expression profiles and the binding affinity between key cellular receptors and viral proteins are esse...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    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 checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.08.13.456190v1 on bioRxiv