TY - JOUR TI - Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy AU - Barone, Sierra M AU - Paul, Alberta GA AU - Muehling, Lyndsey M AU - Lannigan, Joanne A AU - Kwok, William W AU - Turner, Ronald B AU - Woodfolk, Judith A AU - Irish, Jonathan M A2 - Altan-Bonnet, Grégoire A2 - Walczak, Aleksandra M A2 - De Rosa, Stephen A2 - Mahfouz, Ahmed VL - 10 PY - 2021 DA - 2021/08/05 SP - e64653 C1 - eLife 2021;10:e64653 DO - 10.7554/eLife.64653 UR - https://doi.org/10.7554/eLife.64653 AB - 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. KW - machine learning KW - rhinovirus KW - COVID-19 KW - immune monitoring KW - systems biology KW - cytometry JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -