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T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps

T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness...

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Autores principales: Andreatta, Massimo, Gueguen, Paul, Borcherding, Nicholas, Carmona, Santiago J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bio-Protocol 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450729/
https://www.ncbi.nlm.nih.gov/pubmed/37638293
http://dx.doi.org/10.21769/BioProtoc.4735
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author Andreatta, Massimo
Gueguen, Paul
Borcherding, Nicholas
Carmona, Santiago J.
author_facet Andreatta, Massimo
Gueguen, Paul
Borcherding, Nicholas
Carmona, Santiago J.
author_sort Andreatta, Massimo
collection PubMed
description T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user. Key features Computational analysis of paired scRNA-seq and scTCR-seq data Characterizing T-cell functional state by reference-based analysis using ProjecTILs Exploring T-cell clonal structure using scRepertoire Linking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotype Graphical overview [Image: see text]
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spelling pubmed-104507292023-08-26 T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps Andreatta, Massimo Gueguen, Paul Borcherding, Nicholas Carmona, Santiago J. Bio Protoc Methods Article T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user. Key features Computational analysis of paired scRNA-seq and scTCR-seq data Characterizing T-cell functional state by reference-based analysis using ProjecTILs Exploring T-cell clonal structure using scRepertoire Linking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotype Graphical overview [Image: see text] Bio-Protocol 2023-08-20 /pmc/articles/PMC10450729/ /pubmed/37638293 http://dx.doi.org/10.21769/BioProtoc.4735 Text en ©Copyright : © 2023 The Authors; This is an open access article under the CC BY license https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Methods Article
Andreatta, Massimo
Gueguen, Paul
Borcherding, Nicholas
Carmona, Santiago J.
T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps
title T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps
title_full T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps
title_fullStr T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps
title_full_unstemmed T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps
title_short T Cell Clonal Analysis Using Single-cell RNA Sequencing and Reference Maps
title_sort t cell clonal analysis using single-cell rna sequencing and reference maps
topic Methods Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450729/
https://www.ncbi.nlm.nih.gov/pubmed/37638293
http://dx.doi.org/10.21769/BioProtoc.4735
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