TY - JOUR
T1 - Characterization and Monitoring of Antigen-Responsive T Cell Clones Using T Cell Receptor Gene Expression Analysis
AU - Pollastro, Sabrina
AU - de Bourayne, Marie
AU - Balzaretti, Giulia
AU - Jongejan, Aldo
AU - van Schaik, Barbera D. C.
AU - Niewold, Ilse T. G.
AU - van Kampen, Antoine H. C.
AU - Maillère, Bernard
AU - de Vries, Niek
N1 - Funding Information: This work was carried out on the Dutch national e-infrastructure with the support of SURF Foundation (e-infra180005). Publisher Copyright: © Copyright © 2021 Pollastro, de Bourayne, Balzaretti, Jongejan, van Schaik, Niewold, van Kampen, Maillère and de Vries.
PY - 2021/2/19
Y1 - 2021/2/19
N2 - High-throughput T-cell receptor repertoire sequencing constitutes a powerful tool to study T cell responses at the clonal level. However, it does not give information on the functional phenotype of the responding clones and lacks a statistical framework for quantitative evaluation. To overcome this, we combined datasets from different experiments, all starting from the same blood samples. We used a novel, sensitive, UMI-based protocol to perform repertoire analysis on experimental replicates. Applying established bioinformatic routines for transcriptomic expression analysis we explored the dynamics of antigen-induced clonal expansion after in vitro stimulation, identified antigen-responsive clones, and confirmed their activation status using the expression of activation markers upon antigen re-challenge. We demonstrate that the addition of IL-4 after antigen stimulation drives the expansion of T cell clones encoding unique receptor sequences. We show that our approach represents a scalable, high-throughput immunological tool, which can be used to identify and characterize antigen-responsive T cells at clonal level.
AB - High-throughput T-cell receptor repertoire sequencing constitutes a powerful tool to study T cell responses at the clonal level. However, it does not give information on the functional phenotype of the responding clones and lacks a statistical framework for quantitative evaluation. To overcome this, we combined datasets from different experiments, all starting from the same blood samples. We used a novel, sensitive, UMI-based protocol to perform repertoire analysis on experimental replicates. Applying established bioinformatic routines for transcriptomic expression analysis we explored the dynamics of antigen-induced clonal expansion after in vitro stimulation, identified antigen-responsive clones, and confirmed their activation status using the expression of activation markers upon antigen re-challenge. We demonstrate that the addition of IL-4 after antigen stimulation drives the expansion of T cell clones encoding unique receptor sequences. We show that our approach represents a scalable, high-throughput immunological tool, which can be used to identify and characterize antigen-responsive T cells at clonal level.
KW - T cell responses
KW - T-cell receptor
KW - adaptive immune receptor repertoire
KW - bioinformatics
KW - immunoinformatics
KW - next generation sequencing
UR - http://www.scopus.com/inward/record.url?scp=85102315816&partnerID=8YFLogxK
U2 - https://doi.org/10.3389/fimmu.2020.609624
DO - https://doi.org/10.3389/fimmu.2020.609624
M3 - Article
C2 - 33679697
SN - 1664-3224
VL - 11
JO - Frontiers in immunology
JF - Frontiers in immunology
M1 - 609624
ER -