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Review 1: "A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients"

This study investigates the molecular underpinnings of liver dysfunction in deceased patients with severe COVID-19. Reviewers find the study reliable with caution towards the applicability of findings to mild COVID-19 phenotypes and current demographics of vaccinated individuals.

Published onDec 09, 2022
Review 1: "A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients"
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A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients
Description

AbstractThe molecular underpinnings of organ dysfunction in acute COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we performed single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents. We identified hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells. Integrated analysis and comparisons with healthy controls revealed extensive changes in the cellular composition and expression states in COVID-19 liver, reflecting hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis. We also observed Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas, resembling similar responses in liver injury in mice and in sepsis, respectively. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition was dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells. Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.

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Review:

In general, this paper studies the changes in the cellular composition and expression states in COVID-19-infected liver cells by analyzing snRNA-seq and spatial transcriptomic data from the livers of COVID-19 decedents in comparison to healthy controls. The analyses are quite thorough and provide novel insights into liver function changes in COVID-19. The omics data from those patients, if publicly available, will be great resources to the field. The major limitation of the study, as discussed in the paper as well, is the relatively small sample size (17 patients), where the patient heterogeneity (e.g. age) may confound the analysis. Also, since all patients have a severe COVID-19 phenotype, the conclusion cannot be generalized to other phenotypes. Nevertheless, the paper is overall well-written and good for publication with minor revision. Below are my comments regarding computational analysis:

  1. For doublet detection, the authors applied Scrublet. However, since samples are from different donors, it is more reasonable to use Demuxlet (Kang, et al., 2018) to detect doublets via genetic variation, which is considered an approximate ground truth. I suggest the authors apply Demuxlet to remove doublets (replacing Demuxlet but following the same procedure afterward) and see if there are any changes in the conclusion.

  2. Batch effect correction and clustering are important steps that may have a big impact on downstream analyses. The authors used Harmony to remove the batch effect before clustering, which is well-known for its computational efficiency. Still, I am a little bit concerned about the choice of Harmony on downstream analyses. That being said, I suggest the authors try some other methods for batch effect correction before clustering (e.g. scVI, Seurat) to check the robustness of the results.

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