Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy
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 di...
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Format: | Article |
Language: | English |
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eLife Sciences Publications Ltd
2021-08-01
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Series: | eLife |
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Online Access: | https://elifesciences.org/articles/64653 |
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doaj-21ef1bb6d59e41a698679d22b7e99387 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sierra M Barone Alberta GA Paul Lyndsey M Muehling Joanne A Lannigan William W Kwok Ronald B Turner Judith A Woodfolk Jonathan M Irish |
spellingShingle |
Sierra M Barone Alberta GA Paul Lyndsey M Muehling Joanne A Lannigan William W Kwok Ronald B Turner Judith A Woodfolk Jonathan M Irish Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy eLife machine learning rhinovirus COVID-19 immune monitoring systems biology cytometry |
author_facet |
Sierra M Barone Alberta GA Paul Lyndsey M Muehling Joanne A Lannigan William W Kwok Ronald B Turner Judith A Woodfolk Jonathan M Irish |
author_sort |
Sierra M Barone |
title |
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy |
title_short |
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy |
title_full |
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy |
title_fullStr |
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy |
title_full_unstemmed |
Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy |
title_sort |
unsupervised machine learning reveals key immune cell subsets in covid-19, rhinovirus infection, and cancer therapy |
publisher |
eLife Sciences Publications Ltd |
series |
eLife |
issn |
2050-084X |
publishDate |
2021-08-01 |
description |
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. |
topic |
machine learning rhinovirus COVID-19 immune monitoring systems biology cytometry |
url |
https://elifesciences.org/articles/64653 |
work_keys_str_mv |
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doaj-21ef1bb6d59e41a698679d22b7e993872021-08-17T11:46:04ZengeLife Sciences Publications LtdeLife2050-084X2021-08-011010.7554/eLife.64653Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapySierra M Barone0https://orcid.org/0000-0001-5944-750XAlberta GA Paul1https://orcid.org/0000-0002-9318-3760Lyndsey M Muehling2https://orcid.org/0000-0003-3203-3264Joanne A Lannigan3https://orcid.org/0000-0002-3981-8681William W Kwok4https://orcid.org/0000-0003-4843-4599Ronald B Turner5Judith A Woodfolk6https://orcid.org/0000-0002-8915-4334Jonathan M Irish7https://orcid.org/0000-0001-9428-8866Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States; Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, United StatesAllergy Division, Department of Medicine, University of Virginia School of Medicine, Charlottesville, United StatesAllergy 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 StatesDepartment of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, United StatesBenaroya Research Institute at Virginia Mason, Seattle, United StatesDepartment of Pediatrics, University of Virginia School of Medicine, Charlottesville, United StatesAllergy 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 StatesDepartment 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 StatesFor 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.https://elifesciences.org/articles/64653machine learningrhinovirusCOVID-19immune monitoringsystems biologycytometry |