The SARS-CoV-2 virus infection presents a range of clinical manifestations, ranging from mild or no symptoms to moderate to severe respiratory illnesses, such as ARDS (acute respiratory-distress syndrome) and multiorgan failure, to death. The innate immune defense mounted by the body involves pattern recognition receptors (PAR), which activate an array of transcription factors through a signal transduction pathway, ultimately resulting in the secretion of different interferons and chemokines. Adaptive immune responses follow, leading to protective, or if uncontrolled, severe forms of infection with increased release of proinflammatory cytokines (cytokine storm). The nature of these immune responses and why they are different between individuals is still not well understood.

The deep immune profiling technique provides a thorough understanding of the nature of immune responses in individuals and could be used to unravel any differences in individual responses and to correlate this diversity with clinical patterns, severity and progression of disease. High-dimensional flow cytometry is a powerful technique with which to obtain deeper insights on the exact nature and diversity of immune responses.

In the publication, “Deep Immune Profiling of COVID-19 Patients Reveals Distinct Immunotypes with Therapeutic Implications,” Mathew et al. use high-dimensional flow cytometry to understand the general distribution of immune profiles of COVID-19 patients (n = ~125) and investigated the immune signatures of each of these immune profiles. From this observational study, they detected several patterns:

• Compared to healthy donors (HD) and recovered donors (RD), COVID-19 patients showed a significant decrease in B cell and CD3 T cell frequencies. A preferential loss of CD8 T cells compared to CD4 T cells was observed.
  
• High-dimensional flow cytometric analysis revealed differences in lymphocyte activation and differentiation.
  
• In a subset of COVID-19 patients, CD8 T cell activation was triggered with a significant increase in KI67+ and HLA-DR+CD38+ non naïve CD8 T cells.
  
• The infection also resulted in diverse CD4 T cell responses. High-dimensional tSNE analysis identified distinct CD4 populations that were activated in COVID-19 patients.
  
• A difference in cytokine levels (for example, CXCL10) was also observed between COVID-19 in a subset of patients, while others did not show any increase.
  
• Distinct B cell populations were observed in COVID-19 patients, which were phenotypically different from healthy and recovered donors in terms of several surface marker expression.
  
• Naïve B cell population frequency was comparable between COVID-19 patients and of HD and RD populations, whereas class-switched (IgD-CD27+) and not-class-switched (IgD+CD27+). Memory B cell populations showed reduced frequency; in contrast, CD27-IgD- B cells and CD27+CD38+ plasmablasts showed marked increase.
  
• Plasmablast responses were also different among COVID-19 patients.
  
• A stable temporal relationship was observed in immune responses of non-naïve CD8 T cells, non-naïve CD4 T cells and B cells, with disease progression.
  
• Computational analysis revealed distinct immune populations that could be related to disease severity trajectory.
  
• A robust CD4 T cell activation, reduction or absence of cT_FH cells and proliferating effector or exhausted CD8 T cells and T-bet+ plasmablast was correlated with more severe disease.
  
• A more traditional response with effector CD8 T cells, reduced CD4 T cell activation and proliferating plasmablast and memory B cells was correlated with patients who failed to mount a robust T cell and B cell response.

Read the paper for further details.