Single-nuclei characterization of pervasive transcriptional signatures across organs in response to COVID-19

  1. The COVID Tissue Atlas Consortium
  2. Alejandro A Granados
  3. Simon Bucher
  4. Hanbing Song
  5. Aditi Agrawal
  6. Ann T Chen
  7. Tien Peng
  8. Norma Neff
  9. Angela Oliveira Pisco
  10. Franklin Huang
  11. Bruce Wang

  • Curated by eLife

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    eLife assessment

    This study provides a valuable data resource to study the systemic effects of severe COVID-19. It shows compelling evidence that the transcriptional response to COVID-19 is coordinated across the body, and it highlights cell interactions between macrophages and endothelial cells in COVID-19. This analysis and the associated resource will be valuable to understand the pathogenic mechanism of long-COVID.

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Abstract

Infection by coronavirus SARS-CoV2 is a severe and often deadly disease that has implications for the respiratory system and multiple organs across the human body. While the effects in the lung have been extensively studied, less is known about the impact COVID-19 has across other organs.

Methods:

Here, we contribute a single-nuclei RNA-sequencing atlas comprising six human organs across 20 autopsies where we analyzed the transcriptional changes due to COVID-19 in multiple cell types. The integration of data from multiple organs enabled the identification of systemic transcriptional changes.

Results:

Computational cross-organ analysis for endothelial cells and macrophages identified systemic transcriptional changes in these cell types in COVID-19 samples. In addition, analysis of gene modules showed enrichment of specific signaling pathways across multiple organs in COVID-19 autopsies.

Conclusions:

Altogether, the COVID Tissue Atlas enables the investigation of both cell type-specific and cross-organ transcriptional responses to COVID-19, providing insights into the molecular networks affected by the disease and highlighting novel potential targets for therapies and drug development.

Funding:

The Chan-Zuckerberg Initiative, The Chan-Zuckerberg Biohub.

Article activity feed

  1. Version published to 10.7554/elife.81090 on eLife
  2. eLife

    eLife assessment

    This study provides a valuable data resource to study the systemic effects of severe COVID-19. It shows compelling evidence that the transcriptional response to COVID-19 is coordinated across the body, and it highlights cell interactions between macrophages and endothelial cells in COVID-19. This analysis and the associated resource will be valuable to understand the pathogenic mechanism of long-COVID.

  3. eLife

    Reviewer #1 (Public Review):

    Although COVID-19 primarily causes an inflammatory response in the lungs, there is growing evidence that other organs are also affected by SARS-Cov-2, and that some patients continue to receive long-term effects of the disease sequelae even after treatment. We are not clear at this time about the effects of COVID-19 in organs other than the lungs. In this study, the authors presented the COVID Tissue Atlas (CTA) that comprises scRNA-seq data across six human organs of severe COVID-19. This study provides a valuable data resource to study the systemic effects of severe COVID-19, especially the common and specific transcriptional response to COVID-19 in multiple organs. Specifically, the authors identified dysregulated insulin and HIF signaling and prominent macrophage-endothelial interactions. This study will obviously help us to understand the pathogenesis of long-COVID.

  4. eLife

    Reviewer #2 (Public Review):

    This cell atlassing study used single nuclei RNA-sequencing to profile cell type-specific transcriptional response to COVID-19 across multiple organs. The authors surveyed a cohort of 20 patients including 15 COVID-19 donors and 6 organs including the lung, liver and heart. They then annotated major cell types across these tissues and performed systematic differential gene expression analysis to propose cell type-specific shared transcriptional responses in macrophages and endothelial cells across multiple tissues. Finally, they inferred COVID-19 enriched cell interactions between macrophages and endothelia across multiple organs.

    The strengths of the study include cross organ profiling from COVID-19 patients beyond the lungs, the immediate availability of this snRNAseq dataset as a resource and the systematic gene expression analysis that compares cell type specific disease programs across the body. There are several novel observations including dysregulation of insulin signalling in the liver and the heart. Most notable are the putative receptor-ligand interactions identified between macrophages and endothelial cells, an understudied aspect of COVID-19 tissue pathology.

    However, the study presents weaknesses that diminish the impact of the resource. First, tissue profiling depth/coverage is lower than existing resources with relatively few number of cells per tissue and, more importantly, a very coarse grained cellular annotation. Second, the extent of coordinated gene expression changes across different organs is not very clear from the analysis presented in the paper, especially for macrophages. Finally, the comparisons to existing resources are not very strong and it would be more impactful to see the orthogonal (IHC or smFISH) validation of the novel snRNASeq observations in this study (e.g. endothelial-macrophage interactions).

    Major comments:

    1. While multiple organs have been profiled, the overall cell numbers are low (~85k nuclei across six organs) compared to existing studies (Delorey study from broad with ~100k nuclei from lung alone). There is also cell # and type bias towards certain donors - 6 donors (donors 15-20) have significantly more cells than others and majority of certain cell types come from a handful of donors (e.g. fibroblasts in covid lung). There is no analysis or discussion to compare the statistical power of this study to other resources - I expect it is limited in recovering DE genes compared to other resources, especially given patient heterogeneity in COVID-19.

    2. The results on ABI/Transitional AT2 and PATS cells in the lung are not clear. While the increased basal cells are presented as likely ABIs, the label transfer seems to map most of this signature to AT1 cells (Fig 2E). Fig 2F presents gene expression similarities - but it is difficult to see them on the heatmap (there are few cells and this reviewer is color blind). A more quantitative approach or clear visualisation of shared definitive marker gene expression is needed. Regarding PATS, with the limited number of nuclei & patients profiled here, I am not confident in the label transfer based comparison to the Broad study.

    3. More granular annotation of endothelial and macrophage subtypes would improve the utility of the resource. For example, lymphatic vs vascular endothelial cells in the lung show different responses to COVID-19 with the former population increasing in abundance in disease while the latter population diminishes (e.g. Broad delorey study). Such phenotypes cannot be extracted from the current annotation.

    4. The extent of the cross organ coordinated response is not very clear. Fig 5A and Fig 5 sup fig suggest common DEG genes in macrophages and endothelial cells respectively across organs, but Fig 5F and G seem to suggest that DE coordination is close to random or not significant (except endothelial cells). Fig 5B-E correlations also seem limited. Fig 6C-E finds few cell-cell interactions conserved between macrophages and endothelial cells. In addition, endothelial cells change in abundance in opposite directions in the lung vs heart, suggesting divergent responses.

    5. How many STR genes are there and are they conserved across different cell types?

    6. Orthogonal validation of some of the novel findings with IHC or smFISH would confirm the robustness of novel findings and utility as a resource. The validation of hepatocyte insulin dysregulation or the vascular-macrophage cell interactions would add great value.

  5. ScreenIT

    SciScore for 10.1101/2022.05.31.493925: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Reads were aligned to an extended Gencode Reference 30 (GRCh38) genome containing SARS-Cov2 genes (kindly provided by Aviv Regev and Carly Ziegler) using CellRanger version 5.0.1, available from 10x Genomics, with default parameters.
    CellRanger
    suggested: (SCIGA, RRID:SCR_021002)
    Gene set enrichment analysis: To identify gene sets enriched in COVID donors, we selected the top DE genes for each cell type (COVID vs healthy) and used them as input for pathfindR (Ulgen, Ozisik, and Sezerman 2019), a gene-set enrichment algorithm that includes the fold-change along with potential interactions using a protein-protein interaction network.
    Gene set enrichment analysis
    suggested: (Gene Set Enrichment Analysis, RRID:SCR_003199)
    We used 4 different pathway databases as references for our analysis to be comprehensive, KEGG, Reactome, GO, and BioCarta.
    BioCarta
    suggested: (BioCarta Pathways, RRID:SCR_006917)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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

    About SciScore

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  6. Version published to 10.1101/2022.05.31.493925v1 on bioRxiv