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Authors: Bouadma et al.

Link to paper: https://www.medrxiv.org/content/10.1101/2020.05.01.20087239v1

Journal/ Pre-Print: MedRxiv

 Tags: Clinical, Immunology/Immunity, Inflammation

Research Highlights

1. Longitudinal analysis of PBMCs and serum from one COVID-19 patient (day 14 after symptom onset until death on day 24 from multi-organ failure).

2. Although numbers of naïve CD4+ and CD8+ T cells, were reduced in number compared to healthy controls, numbers of effector memory T cells were increased.

3. γδ T-cells, proliferating NK cells and antibody-secreting cells in the patient with COVID19 as compared to 5 healthy controls. Many proinflammatory soluble mediators were also upregulated in the serum.

Summary

Lila Bouadma et al., analysed in a longitudinal study PBMCs (flow cytometry) and serum (Luminex/ELISAs) from an 80-year-old male with severe symptoms of COVID-19 from day 14 after symptoms onset until patient death. Data were compared to 5 healthy controls. A persistent lymphopenia with most T cells expressing high levels of PD1 (exhaustion) and CD57 (senescence) was shown. Antibody-secreting cells, γδ T-cells and proliferating NK cells were enriched in contrast to a decrease in CD14+CD16- monocytes. All 72 serological analytes measured except for Eotaxin, EGF and chemerin were elevated. Analysis at early stage and further technical and biological replicates are needed to validate these observations.

Impact for SARS-CoV2/COVID19 research efforts

Understand the immune response to SARS-CoV2/COVID19 in a longitudinal manner. Aimed to uncover underlying pathological mechanisms and therapeutic targets for COVID19.

Study Type

· Patient Case study (ex vivo analysis of PBMCs and serum)

Strengths and limitations of the paper

Novelty: Longitudinal analysis of immune cells and soluble mediators in blood from a patient with COVID19

Standing in the field: Many other papers have performed similar immune-phenotyping across different disease severities with multiple patients. Although this paper lacks any statistics, longitudinal sampling is involved and it confirms data from others (e.g. lymphopenia and increased IL-6)

Appropriate statistics: Statistical analysis not appropriate as this is a case study.

Translatability: Detailed analysis of the course of disease in this patient may help to guide clinical management of similar patients.

Main limitations:

· Samples only from 1 COVID19 patient and not until day 14 after symptom onset (patient transferred to ICU on day 5)

· Lack of SARS-CoV-specific T cell responses data

· Description of methods relate to general cohort but not specific to the patient and healthy controls since they report assessments of viral load but not specific to the subjects of the study

· Data shown alongside that for 5 “healthy controls”, but there is no mention of matching

· Analysis performed solely on PBMCs and serum. Would have been good to look at granulocytes as well. BAL/tissue samples would also have been powerful although these are understandably very difficult to obtain

· No statistics at all. Would have been good to perform technical replicates for flow cytometry and serum analysis to show reproducibility

· No always clwear what the percentages refer to.

· Only show a percentage not the total numbers of cells

· CD38 and HLA-DR shown as T cell activation markers, but why not others like CD25/CD69?

· Approx. 90% of CD3+ T cells are apparently gamma-delta+ T cells on d14; it would be informative to show all T cell subsets as a % of CD3 for comparison

· No viral quantification to compare to other studies, but SARS-CoV-2 was apparently detected in blood and nasopharyngeal samples throughout.

· Report “standardised expression of serum analytes” giving no indication of concentrations. Serum was apparently heat-inactivated but no discussion of whether this effects expression levels. If soluble mediators were out of range of the standard curve, the hight or lowest extrapolated concentration was used.