Analysis of adaptive immune cell populations and phenotypes in the patients infected by SARS-CoV-2
Authors: Yang et al. Link to paper: https://www.medrxiv.org/content/10.1101/2020.03.23.20040675v2 (posted on 03/04/2020)
Journal/ Pre-Print: MedRxiv
Key Words: Adaptive immunity, COVID-19 patients, FACS, aging
1. Compared to healthy donors (HD), circulating CD8 T cells presented an upregulation of activation marker CD25 in COVID-19 patients. Moreover, both CD4 and CD8 T cells from COVID-19 patients exhibited an upregulation of the exhausted marker PD-1 (highlighting their activation status);
2. Frequencies of circulating B cells, Tfh and GCB-like cells were significantly increased in COVID-19 patients, suggesting a primary B cell response occurred;
3. There was no correlation between age and leucocyte populations as well as age and expression of functional markers on T cell populations, such as CD25.
The article from Yang et al. demonstrated a FACS-characterization of adaptive immune cell populations from blood of 38 COVID-19 patients and 18 healthy donors. They observed that the percentages of CD4 and CD8 T cells were similar, whereas frequencies of circulating B cells, Tfh and GCB-like cells were significantly increased in COVID-19 patients. Looking at functional markers on T cells, they found that specially CD8 T cells exhibited enhanced expression of CD25 and PD-1 in COVID-19 patients. There is no correlation between age and percentages of leucocyte populations and also age and expression of functional markers on T cell populations.
Impact for SARS-CoV2/COVID19 research efforts
Understanding the adaptive immune response to SARS-CoV2/COVID19 is crucial to obtain clear readout to follow a potential efficacy of vaccines for instance. The summarised article describes changes on circulating CD8 T, B, Tfh and GCB-like populations upon SARS-CoV2 infection, providing some useful information about the immune response to SARS-CoV2 for further studies focused on these leucocyte populations (it is required much deeper characterization). Indeed, future work on these populations could be avenues for better understating of immune response to SARS-CoV2 as the characterization of Ig-class of antibody-secreting cell in the blood – IgG and IgM would suggest a primary/early B cell response.
· Clinical study characterizing immune adaptive cell populations from blood of 38 COVID-19 patients and 18 healthy donors using multicolor flow cytometry
Strengths and limitations of the paper
Novelty: Description of the potential role of some leucocytes populations (e.g, such as Tfh and GCB-like cells) in the immune response to SARS-CoV2.
Standing in the field: There are other evidences showing a reduction of circulating lymphocytes in COVID-19 patients in terms of absolute numbers (not percentage) (https://www.medrxiv.org/content/10.1101/2020.02.18.20024364v1.full.pdf), whereas in the summarised paper the authors did not observe a significant decrease of lymphocyte populations frequencies. Moreover, other report described that PD-1 was up-regulated upon SARS-COV 2 infection and this may predict severe disease progression in COVID-19 patients (https://www.nature.com/articles/s41423-020-0401-3).
Appropriate statistics: No. According to the statistical analysis in methods section, the authors pointed out that Student's t-test was performed for analysis of data under either normal distribution or non-normal distribution. Data under non-normal distribution should be analysed using a non-parametric test, such as Mann-Whitney U test.
Viral model used: N/A (analysis on patients infected with SARS-CoV2).
Translatability: This paper is just a preliminary observational study characterising blood-circulating frequency of adaptive immune cells in COVID-19 patients.
1) They did not mention the time point/day post-symptoms of when they performed the analysis. There is no information about the stage of the disease and therefore patients were not stratified by disease severity (most of the patients presented mild symptoms); numbers are relatively small for a study of this nature. Unclear whether the sample of patients is truly represents whole population of COVID-19 patients therefore difficult to draw firm conclusions.
2) Few information about features of the patient and healthy control groups (basically, only age and gender of patients were provided). Not clear whether patients and controls were age- and sex-matched. Patient/control demographics are contained in Table 1, which is absent.
3) FACS-panel description is not accurate and no representative dot plots are shown.
4) Data have been demonstrated just in percentages. Absolute numbers for each cell populations should be addressed to make the findings more robust.
5) Observational study and not mechanistic (no functional assays using isolated cells from COVID-19 patients were performed).