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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Autores principales:	Sierra M Barone, Alberta GA Paul, Lyndsey M Muehling, Joanne A Lannigan, William W Kwok, Ronald B Turner, Judith A Woodfolk, Jonathan M Irish
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	machine learning rhinovirus COVID-19 immune monitoring systems biology cytometry Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/21ef1bb6d59e41a698679d22b7e99387
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spelling	oai:doaj.org-article:21ef1bb6d59e41a698679d22b7e993872021-11-15T07:23:15ZUnsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy10.7554/eLife.646532050-084Xe64653https://doaj.org/article/21ef1bb6d59e41a698679d22b7e993872021-08-01T00:00:00Zhttps://elifesciences.org/articles/64653https://doaj.org/toc/2050-084XFor 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.Sierra M BaroneAlberta GA PaulLyndsey M MuehlingJoanne A LanniganWilliam W KwokRonald B TurnerJudith A WoodfolkJonathan M IrisheLife Sciences Publications Ltdarticlemachine learningrhinovirusCOVID-19immune monitoringsystems biologycytometryMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning rhinovirus COVID-19 immune monitoring systems biology cytometry Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	machine learning rhinovirus COVID-19 immune monitoring systems biology cytometry Medicine R Science Q Biology (General) QH301-705.5 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
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.
format	article
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
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
publishDate	2021
url	https://doaj.org/article/21ef1bb6d59e41a698679d22b7e99387
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_version_	1718428481115652096

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

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