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

Elife. 2021 Aug 5;10:e64653. doi: 10.7554/eLife.64653.


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.

Trial registration: ClinicalTrials.gov NCT02796001.

Keywords: COVID-19; computational biology; cytometry; human; immune monitoring; immunology; inflammation; machine learning; rhinovirus; systems biology.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adolescent
  • Adult
  • Algorithms
  • CD4-Positive T-Lymphocytes / immunology
  • COVID-19 / immunology*
  • Humans
  • Leukemia, Myeloid, Acute / drug therapy
  • Leukemia, Myeloid, Acute / immunology*
  • Melanoma / drug therapy
  • Melanoma / immunology*
  • Neoplasms
  • Picornaviridae Infections / immunology*
  • Rhinovirus / isolation & purification
  • SARS-CoV-2 / isolation & purification
  • Unsupervised Machine Learning*
  • Young Adult

Associated data

  • ClinicalTrials.gov/NCT02796001