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Authors:Keiko Taniguchi-Ponciano et al. 

Journal/ Pre-Print:Researchsquare 

TagsBioinformatics, Immunology/Immunity, Molecular biology 

Research Highlights

  1. Circulating immune cell profile of severe COVID-19 patients is skewed towards immature myeloid populations. 

Summary 

Circulating immune cells from 5 severe COVID-19 patients undergoing mechanical ventilation were used for single cell RNAseq. The cells were compared to an unspecified cohort of healthy controls. The cells were devided into different immune populations according to published markers. The authors see an increase in myeloid populations in COVID-19 patients compared to control, in particular immature myeloid cells such as band neutrophils, metamyelocytes and promyelocytes-myelocytes. Due to the poor resolution of the figures it is difficult to confirm the author’s statements.  

Impact for SARS-CoV2/COVID19 research efforts  

Understand the immune response to  SARS-CoV2/COVID19  

The authors show an increase in immature myelocyte populations in severe cases 

Study Type  

  • bioinformatics study 

  • Cohort study  

Strengths and limitations of the paper 

Novelty: This is not the first study of this type. The authors confirm the skew towards myeloid cells compared to lymphoid cells in COVID-19 patients and also show how the myeloid cells have an immature phenotype.  

Standing in the field:the results confirm previous findings and it is known that severe viral infection can drive increase of immature myeloid cell populations  

Appropriate statistics:none 

Viral model used:N/A 

Translatability:Remote 

Main limitations:  

  • The resolution of the figures does not allow the comprehension of the content 

  • The figure legend is non-existent 

  • There is no information about the control patients 

  • It is not clear how the pooling/barcoding has been done (before or after several patients were pooled) 

  • Small number of patients with different pre-existing conditions and one with another bacterial infection 

  • No information about statistical methods