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Authors: P Bost et al.

Link to paper: https://www.sciencedirect.com/science/article/pii/S0092867420305687

Journal/ Pre-Print: Cell

Tags: Cell Biology, Immunology/Immunity, Inflammation, scRNA

Research Highlights 

1. Development of a new unbiased automatic computational tool (Viral-Track) to identify virus-infected vs bystander cells in infected samples

2. Single cell (sc)RNA-seq analysis of Bronchoalveolar-Lavage (BAL) from mild or severe COVID-19 patients reveals severe alterations in the immune landscape

3. Coinfection of SARS-Cov-2 and human Metapneumovirus (hMPV) could explain the severity of one of the patients through its potential role in immunosuppression.

Summary 

In this study the authors developed a new computational tool (Viral-Track) to map scRNA-seq data to the host and viral genomes to define the infectious status of clinical samples. This tool also identified differentially expressed genes in infected versus bystander cells. scRNA-seq of BAL from 9 COVID-19 patients (3 mild; 6 severe) revealed different immune cell profiles depending on disease severity. Severe cases displayed patient-specific increases in neutrophils, monocytes, and macrophages, whilst NK and CD4+ naïve T cells were consistently enriched. Coinfection with hMPV downregulated macrophage type I IFN and MHCII signalling and could explain the severity of one case.

Impact for SARS-CoV2/COVID19 research efforts

Understand the immune response to SARS-CoV2/COVID19

The authors show the extensive changes in the immune landscape of mild vs severe cases and suggest how coinfections with other viruses could explain some of the severity observed in selected cases.

Study Type

· Clinical Cohort study – re-analysis of ex vivo BAL scRNA (from Liao et al, 2020, Nature Medicine)

Strengths and limitations of the paper

Novelty: The bioinformatics tool developed in this paper (albeit building on previous ones) is an important technical advance for investigating host response to viral infections. Importantly for a clinical environment, it does not require previous knowledge of the viral pathogens infecting the patient. Also, it provides information on which cells gets infected and how they specifically respond transcriptionally to the infection and influence bystander cells. The authors also suggest a possible role for virus coinfection in determining the severity of covid-19.

Standing in the field: The study confirms previous findings that show lymphopenia and elevated neutrophil in severe cases, as well as type I IFN signature and elevated chemokines.

Appropriate statistics: Yes

Viral model used: NA – ex vivo analysis of human BAL

Translatability: NA – ex vivo analysis of human BAL

Main limitations:

· Samples are not age-matched and they have had different therapies. Small sample size.

· The Viral-Track in the case of COVID-19 only detect infected cells in severe patients where the viral load is extremely high. Most of the infected cells are probably lost with this virus. The authors acknowledge that Viral-Track is not ideal for some types of viruses, especially the ones without poly(A) tails in their RNA or without a 5’ cap.

· Only one patient was co-infected with hMPV so, even if the study finds a possible mechanism that could cause increased severity, the sample size is too small.

· A limitation of this bioinformatics approach is its reliance on high quality viral genomes for the mapping step. An unknown virus or virus which currently has a subpar quality for its reference genome will not be identified (there is no de novo genome reconstruction which would be required for finding unreferenced viral genomes).

It could have been interesting to correlate the type of cells, ACE2 and TMPRSS2 expression levels, and viral loads to investigate potential links between these variables.