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COMPASS identifies T-cell subsets correlated with clinical outcomes

Advances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by thes... Full description

Journal Title: Nature biotechnology 2015-06, Vol.33 (6), p.610-616
Main Author: Lin, Lin
Other Authors: Finak, Greg , Ushey, Kevin , Seshadri, Chetan , Hawn, Thomas R , Frahm, Nicole , Scriba, Thomas J , Mahomed, Hassan , Hanekom, Willem , Bart, Pierre-Alexandre , Pantaleo, Giuseppe , Tomaras, Georgia D , Rerks-Ngarm, Supachai , Kaewkungwal, Jaranit , Nitayaphan, Sorachai , Pitisuttithum, Punnee , Michael, Nelson L , Kim, Jerome H , Robb, Merlin L , O'Connell, Robert J , Karasavvas, Nicos , Gilbert, Peter , C De Rosa, Stephen , McElrath, M Juliana , Gottardo, Raphael
Format: Electronic Article Electronic Article
Language: English
Subjects:
HIV
Publisher: United States: Nature Publishing Group
ID: ISSN: 1087-0156
Link: https://www.ncbi.nlm.nih.gov/pubmed/26006008
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recordid: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4569006
title: COMPASS identifies T-cell subsets correlated with clinical outcomes
format: Article
creator:
  • Lin, Lin
  • Finak, Greg
  • Ushey, Kevin
  • Seshadri, Chetan
  • Hawn, Thomas R
  • Frahm, Nicole
  • Scriba, Thomas J
  • Mahomed, Hassan
  • Hanekom, Willem
  • Bart, Pierre-Alexandre
  • Pantaleo, Giuseppe
  • Tomaras, Georgia D
  • Rerks-Ngarm, Supachai
  • Kaewkungwal, Jaranit
  • Nitayaphan, Sorachai
  • Pitisuttithum, Punnee
  • Michael, Nelson L
  • Kim, Jerome H
  • Robb, Merlin L
  • O'Connell, Robert J
  • Karasavvas, Nicos
  • Gilbert, Peter
  • C De Rosa, Stephen
  • McElrath, M Juliana
  • Gottardo, Raphael
subjects:
  • AIDS Vaccines - administration & dosage
  • AIDS Vaccines - immunology
  • Article
  • Case-Control Studies
  • Cytokines - biosynthesis
  • Cytokines - blood
  • Cytokines - immunology
  • Female
  • Flow Cytometry
  • Gene Products, env - immunology
  • Gene Products, env - therapeutic use
  • Health aspects
  • Healthy Volunteers
  • HIV
  • HIV Infections - blood
  • HIV Infections - drug therapy
  • HIV Infections - immunology
  • HIV Infections - virology
  • HIV-1 - immunology
  • HIV-1 - pathogenicity
  • Human immunodeficiency virus
  • Humans
  • Immunity, Cellular
  • Immunoglobulin A - blood
  • Male
  • Methods
  • Single-Cell Analysis
  • T cells
  • T-Lymphocyte Subsets - immunology
  • Treatment Outcome
  • Vaccines
ispartof: Nature biotechnology, 2015-06, Vol.33 (6), p.610-616
description: Advances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by these technologies are lacking. Here, we present a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS). COMPASS uses a Bayesian hierarchical framework to model all observed cell subsets and select those most likely to have antigen-specific responses. Cell-subset responses are quantified by posterior probabilities, and human subject-level responses are quantified by two summary statistics that describe the quality of an individual's polyfunctional response and can be correlated directly with clinical outcome. Using three clinical data sets of cytokine production, we demonstrate how COMPASS improves characterization of antigen-specific T cells and reveals cellular 'correlates of protection/immunity' in the RV144 HIV vaccine efficacy trial that are missed by other methods. COMPASS is available as open-source software.
language: eng
source:
identifier: ISSN: 1087-0156
fulltext: no_fulltext
issn:
  • 1087-0156
  • 1546-1696
url: Link


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creatorLin, Lin ; Finak, Greg ; Ushey, Kevin ; Seshadri, Chetan ; Hawn, Thomas R ; Frahm, Nicole ; Scriba, Thomas J ; Mahomed, Hassan ; Hanekom, Willem ; Bart, Pierre-Alexandre ; Pantaleo, Giuseppe ; Tomaras, Georgia D ; Rerks-Ngarm, Supachai ; Kaewkungwal, Jaranit ; Nitayaphan, Sorachai ; Pitisuttithum, Punnee ; Michael, Nelson L ; Kim, Jerome H ; Robb, Merlin L ; O'Connell, Robert J ; Karasavvas, Nicos ; Gilbert, Peter ; C De Rosa, Stephen ; McElrath, M Juliana ; Gottardo, Raphael
creatorcontribLin, Lin ; Finak, Greg ; Ushey, Kevin ; Seshadri, Chetan ; Hawn, Thomas R ; Frahm, Nicole ; Scriba, Thomas J ; Mahomed, Hassan ; Hanekom, Willem ; Bart, Pierre-Alexandre ; Pantaleo, Giuseppe ; Tomaras, Georgia D ; Rerks-Ngarm, Supachai ; Kaewkungwal, Jaranit ; Nitayaphan, Sorachai ; Pitisuttithum, Punnee ; Michael, Nelson L ; Kim, Jerome H ; Robb, Merlin L ; O'Connell, Robert J ; Karasavvas, Nicos ; Gilbert, Peter ; C De Rosa, Stephen ; McElrath, M Juliana ; Gottardo, Raphael
descriptionAdvances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by these technologies are lacking. Here, we present a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS). COMPASS uses a Bayesian hierarchical framework to model all observed cell subsets and select those most likely to have antigen-specific responses. Cell-subset responses are quantified by posterior probabilities, and human subject-level responses are quantified by two summary statistics that describe the quality of an individual's polyfunctional response and can be correlated directly with clinical outcome. Using three clinical data sets of cytokine production, we demonstrate how COMPASS improves characterization of antigen-specific T cells and reveals cellular 'correlates of protection/immunity' in the RV144 HIV vaccine efficacy trial that are missed by other methods. COMPASS is available as open-source software.
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subjectAIDS Vaccines - administration & dosage ; AIDS Vaccines - immunology ; Article ; Case-Control Studies ; Cytokines - biosynthesis ; Cytokines - blood ; Cytokines - immunology ; Female ; Flow Cytometry ; Gene Products, env - immunology ; Gene Products, env - therapeutic use ; Health aspects ; Healthy Volunteers ; HIV ; HIV Infections - blood ; HIV Infections - drug therapy ; HIV Infections - immunology ; HIV Infections - virology ; HIV-1 - immunology ; HIV-1 - pathogenicity ; Human immunodeficiency virus ; Humans ; Immunity, Cellular ; Immunoglobulin A - blood ; Male ; Methods ; Single-Cell Analysis ; T cells ; T-Lymphocyte Subsets - immunology ; Treatment Outcome ; Vaccines
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descriptionAdvances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by these technologies are lacking. Here, we present a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS). COMPASS uses a Bayesian hierarchical framework to model all observed cell subsets and select those most likely to have antigen-specific responses. Cell-subset responses are quantified by posterior probabilities, and human subject-level responses are quantified by two summary statistics that describe the quality of an individual's polyfunctional response and can be correlated directly with clinical outcome. Using three clinical data sets of cytokine production, we demonstrate how COMPASS improves characterization of antigen-specific T cells and reveals cellular 'correlates of protection/immunity' in the RV144 HIV vaccine efficacy trial that are missed by other methods. COMPASS is available as open-source software.
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titleCOMPASS identifies T-cell subsets correlated with clinical outcomes
authorLin, Lin ; Finak, Greg ; Ushey, Kevin ; Seshadri, Chetan ; Hawn, Thomas R ; Frahm, Nicole ; Scriba, Thomas J ; Mahomed, Hassan ; Hanekom, Willem ; Bart, Pierre-Alexandre ; Pantaleo, Giuseppe ; Tomaras, Georgia D ; Rerks-Ngarm, Supachai ; Kaewkungwal, Jaranit ; Nitayaphan, Sorachai ; Pitisuttithum, Punnee ; Michael, Nelson L ; Kim, Jerome H ; Robb, Merlin L ; O'Connell, Robert J ; Karasavvas, Nicos ; Gilbert, Peter ; C De Rosa, Stephen ; McElrath, M Juliana ; Gottardo, Raphael
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abstractAdvances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by these technologies are lacking. Here, we present a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS). COMPASS uses a Bayesian hierarchical framework to model all observed cell subsets and select those most likely to have antigen-specific responses. Cell-subset responses are quantified by posterior probabilities, and human subject-level responses are quantified by two summary statistics that describe the quality of an individual's polyfunctional response and can be correlated directly with clinical outcome. Using three clinical data sets of cytokine production, we demonstrate how COMPASS improves characterization of antigen-specific T cells and reveals cellular 'correlates of protection/immunity' in the RV144 HIV vaccine efficacy trial that are missed by other methods. COMPASS is available as open-source software.
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