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Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.
Barone, Sierra M; Paul, Alberta Ga; Muehling, Lyndsey M; Lannigan, Joanne A; Kwok, William W; Turner, Ronald B; Woodfolk, Judith A; Irish, Jonathan M.
  • Barone SM; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States.
  • Paul AG; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, United States.
  • Muehling LM; Allergy Division, Department of Medicine, University of Virginia School of Medicine, Charlottesville, United States.
  • Lannigan JA; Allergy Division, Department of Medicine, University of Virginia School of Medicine, Charlottesville, United States.
  • Kwok WW; Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, United States.
  • Turner RB; Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, United States.
  • Woodfolk JA; Benaroya Research Institute at Virginia Mason, Seattle, United States.
  • Irish JM; Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, United States.
Elife ; 102021 08 05.
Article in English | MEDLINE | ID: covidwho-1513039
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ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Leukemia, Myeloid, Acute / Picornaviridae Infections / Unsupervised Machine Learning / COVID-19 / Melanoma Type of study: Experimental Studies / Prognostic study Limits: Adolescent / Adult / Humans / Young adult Language: English Year: 2021 Document Type: Article Affiliation country: ELife.64653

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Leukemia, Myeloid, Acute / Picornaviridae Infections / Unsupervised Machine Learning / COVID-19 / Melanoma Type of study: Experimental Studies / Prognostic study Limits: Adolescent / Adult / Humans / Young adult Language: English Year: 2021 Document Type: Article Affiliation country: ELife.64653