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

BioRxiv : the Preprint Server for Biology
S. M. BaroneJonathan M. Irish

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 very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. MHC tetramers were not used during unsupervised analysis and instead 'left out' to serve as a test of whether T-REX identifies biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by ≥95% following infection. T-REX successfully identified hotspots with virus-specific T cells using pairs of samples comparing Day 7 of infecti...Continue Reading

Citations

Feb 16, 2021·Frontiers in Molecular Biosciences·Diana L BonillaEdmond Chua

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Methods Mentioned

BETA
flow cytometry
fluorescence activated cell sorting
FACS
MDS

Clinical Trials Mentioned

NCT02796001

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