New Paper (Other): The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing about covid19-review HOT 1 OPEN

What did they analyze?
- Bronchial lavage fluid from 6 patients (3 mild and 3 severe).

What methods did they use?

• scRNA seq on 10x Genomics platform. Also used TCR-seq to identify RNA for T-cell receptor expressing cell types.

Does this paper study COVID-19, SARS-CoV-2, or a related disease and/or virus?
- COVID-19 patient samples

What is the main finding (or a few main takeaways)?
-Based upon scRNA seq of cells isolated from bronchial lavage, the single cell expression profiles suggest that FCN1+ macrophages become the dominant cell type in severe disease as compared to mild disease that show more FABP4+ alveolar macrophages. These cells may be responsible for the cytokine storm that is observed as lung tissue becomes compromised. Also increased CD8+ T cells in the lung environment of mild symptom patients suggest an adaptive body response to COVID-19 infection. These patient data were compared with 8 publicly available normal lung datasets. Other finding are that there is a higher proportion of T and NK cells and fewer epithelial cells in COVID-19 patients relative to controls. The GO enrichment of gene sets of severe COVID-19 patients (Fig 3F) associated with ER localization and ribonuclotide metabolism are interesting observations suggesting physiological mechanisms that are co-opted by COVID-19. It was nice to see the clinical data in Table 1, although limited it may prove helpful in comparing the current study with others.

What does this paper tell us about the background and/or diagnostics/therapeutics for COVID-19 / SARS-CoV-2?
-Based upon RNA expression differences the enrichement of particular cell types in mild and severe COVID-19 samples may highlight particular cells to target for intervention but perhaps more importantly, combinations of cells to target. Further intracellular processes that maybe altered with infection, inferred from RNA levels, suggest intracellular pathways that might be targeted in therapeutic development. In particular if physiological changes do parallel the observed RNA differences, then the pathway analysis points to physiological outputs that may prove to be useful as quantifiable correlates of therapeutic efficacy in high-throughput screens.

Do you have any concerns about methodology or the interpretation of these results beyond this analysis?
-Too few details about how the cell samples were processed were presented. What volume of lavage, what is cellular density in lavage, etc, all of which might influence the data.
-as a filtering criteria gene number per retained sample ranged from 200-6000. This suggests that some of the cells were unhealthy (200 genes is low) and that there was contamination of some of the cells with RNA from other cells (6000 genes is high for single cell 10xGenomics RNAseq data. This may be problematic in assessing differential gene expression between the samples.
-No demographic information about the healthy controls was provided. It is unclear whether the data was matched for ethnicity, which may be a factor in normal lung immune cell population distribution and may differ with COVID-19 infection.

from covid19-review.