Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

eLife

Alberta GA Paul¹  Lyndsey M Muehling²  Judith A Woodfolk²  William W Kwok³  Sierra M Barone⁴  Jonathan M Irish⁵  Joanne A Lannigan⁶  Ronald B Turner⁷ 

[1] Allergy Division, Department of Medicine, University of Virginia School of Medicine, Charlottesville, United States;Allergy Division, Department of Medicine, University of Virginia School of Medicine, Charlottesville, United States;Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, United States;Benaroya Research Institute at Virginia Mason, Seattle, United States;Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States;Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, United States;Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States;Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, United States;Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, United States;Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, United States;Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, United States;

关键词: machine learning;    rhinovirus;    COVID-19;    immune monitoring;    systems biology;    cytometry;    Human;   

DOI  :  10.7554/eLife.64653

来源: eLife Sciences Publications, Ltd

【 摘 要 】

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.

【 授权许可】

CC BY