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For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease-specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders EXpanding (T-REX) was created to identify changes in both rare and common cells across human immune monitoring settings. T-REX identified cells with highly similar phenotypes that localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized MHCII tetramer reagents that mark rhinovirus-specific CD4+ cells were left out during analysis and then used to test whether T-REX identified biologically significant cells. T-REX identified rhinovirus-specific CD4+ T cells based on phenotypically homogeneous cells expanding by ≥95% following infection. T-REX successfully identified hotspots of virus-specific T cells by comparing infection (day 7) to either pre-infection (day 0) or post-infection (day 28) samples. Plotting the direction and degree of change for each individual donor provided a useful summary view and revealed patterns of immune system behavior across immune monitoring settings. For example, the magnitude and direction of change in some COVID-19 patients was comparable to blast crisis acute myeloid leukemia patients undergoing a complete response to chemotherapy. Other COVID-19 patients instead displayed an immune trajectory like that seen in rhinovirus infection or checkpoint inhibitor therapy for melanoma. The T-REX algorithm thus rapidly identifies and characterizes mechanistically significant cells and places emerging diseases into a systems immunology context for comparison to well-studied immune changes.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4 cells, MHCII tetramers, were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4 T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

A better understanding of the bifurcation of human B cell differentiation into memory B cells (MBC) and antibody-secreting cells (ASC) and identification of MBC and ASC precursors is crucial to optimize vaccination strategies or block undesired antibody responses. To unravel the dynamics of antigen-induced B cell responses, we compared circulating B cells reactive to SARS-CoV-2 (Spike, RBD and Nucleocapsid) in COVID-19 convalescent individuals to B cells specific to Influenza-HA, RSV-F and TT, induced much longer ago. High-dimensional spectral flow cytometry indicated that the decision point between ASC- and MBC-formation lies in the CD43+CD71+IgG+ Activated B cell compartment, showing properties indicative of recent germinal center activity and recent antigen encounter. Within this Activated B cells compartment, CD86+ B cells exhibited close phenotypical similarity with ASC, while CD86− B cells were closely related to IgG+ MBCs. Additionally, different activation stages of the IgG+ MBC compartment could be further elucidated. The expression of CD73 and CD24, regulators of survival and cellular metabolic quiescence, discerned activated MBCs from resting MBCs. Activated MBCs (CD73- CD24lo) exhibited phenotypical similarities with CD86− IgG+ Activated B cells and were restricted to SARS-CoV-2 specificities, contrasting with the resting MBC compartment (CD73-/CD24hi) that exclusively encompassed antigen-specific B cells established long ago. Overall, these findings identify novel stages for IgG+ MBC and ASC formation and bring us closer in defining the decision point for MBC or ASC differentiation.Competing Interest StatementThe authors have declared no competing interest.