Deep immune profiling of COVID-19 patients reveals patient heterogeneity and distinct immunotypes with implications for therapeutic interventions
bioinformatics clinical immunology/immunity inflammation
Authors: Mathew et al.
Link to paper: https://doi.org/10.1101/2020.05.20.106401
Journal/ Pre-Print: bioRxiv
Tags: Bioinformatics, Clinical, Immunology/Immunity, Inflammation
1. Deep immune profiling of T and B cell subsets in the peripheral blood of COVID19 patients revealed an increase in frequency of specific memory T cell subsets and activated Tfh cells within the T cell compartment, in addition to decreased frequency of class switched and not class switched B cells and elevated frequency of CD27+CD38+ plasmablasts within the B cell compartment. Analysis revealed broad T and B cell activation with increased frequencies of CD39+, PD-1+, Ki-67+ and HLADR+CD38+ cells.
2. Follow-up analysis of PBMCs of some COVID-19 patients at d7 showed stable activated/proliferating populations in the CD8 and CD4 T cell subset as well as stable plasma blast frequencies
3. COVID-19 patients were stratified into 8 clinical severity groups according to the NIH ordinal scale, higher disease severity was associated with lower frequencies of CD8 and CD4 T cells, further analysis determined 3 immunotypes in hospitalised COVID-19 patients: Immunotype 1 characterised by activation and proliferation of CD4+ T cells and a signature of T-bet+ plasmablasts; immunotype 2 characterised by CD8+ effector memory and EMRA subsets, CD8+ T cell activation, T-bet+ memory B cells and CD138+ plasmablasts; and immunotype 3 characterised by low or no T cell and B cell activation. Immunotype 1 was linked to increased severity score.
The authors used high dimensional flow cytometry to perform deep immune profiling of T and B cell subsets in the peripheral blood of a cohort of 71 active COVID19 patients alongside 25 recovered COVID19 patients and 37 healthy controls. By combining this analysis with clinical data they demonstrate the heterogeneity of the immune response in patients hospitalised with COVID19 and identify three immune signatures that link to distinct clinical outcomes.
Impact for SARS-CoV2/COVID19 research efforts
Understand the immune response to SARS-CoV2/COVID19
Clinical symptoms and pathogenesis of SARS-Cov2/COVID19
· Clinical cohort study
Strengths and limitations of the paper
Novelty: Large COVID-19 patient cohort study for deep immune profiling of blood PBMCs
Standing in the field: One of the first studies performing deep immune profiling on a large COVID-19 patient cohort
Appropriate statistics: Yes
Viral model used: Peripheral blood of SARS-CoV2 positive patients
Translatability: Not yet, study will need to be repeated in another clinical cohort, to see if data on patient subtypes is robust, which would allow prediction of clinical course and tailored clinical management
· Suggestions for improvement of figure readability:
o inclusion of median bar for the different groups
o change of colour palette (to accommodate red-green colour blind readers)
o clearer distinction of colours in Figure 6 for the different disease severity groups
o label of x-axis
· FACS gating strategy of CD8 and CD4 is hard to judge, as events are depicted in contour plot (e.g. gate setting of CD27+ CD45RA+ gate in CD8 population; CCR7 gate settings in CD4 gating strategy isn’t consistent)
· Not clear which patients are included in the Luminex analysis (would have been nice to include patients who show high T cell activation levels vs low T cell activation levels)
· It is not clear how many patients are included in the follow up study on day 7
· Figure 5F appears to have the FACS plots switched around for the “stable” and “down” group at d7
· Unclear on the numbers of the 71 COVID patients stratified into each of the 8 groups based on disease severity score – should be included in Figure 6A
· Not clear how long after recovery from COVID patients were sampled
· Figure 1C (assessment of clinical correlates) is underpowered to detect any true associations