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.
【저자키워드】 COVID-19, machine learning, Human, Systems biology, Immune monitoring, rhinovirus, Cytometry, 【초록키워드】 immunology, therapy, knowledge, Infection, immune system, virus, immune, Chemotherapy, Disease progression, cells, Algorithm, Patient, Melanoma, phenotype, trajectory, Single Cell, emerging disease, inhibitor, Other, SARS-CoV-2 infections, leukemia, CD4+ T cell, Analysis, COVID-19 patient, Post-infection, clinical response, immune changes, reagent, hotspot, MHCII, Complete, CD4+ cell, repeated, Cell, identify, example, provided, changes in, homogeneous, magnitude, comparable, virus-specific T cell, individual donor, 【제목키워드】 Infection, Cancer therapy, Immune cell, reveal, subset,