Precise tracking of vaccine-responding T cell clones reveals convergent and personalized response in identical twins

T cell receptor (TCR) repertoire data contain information about infections that could be used in disease diagnostics and vaccine development, but extracting that information remains a major challenge. Here we developed a statistical framework to detect TCR clone proliferation and contraction from lo...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS Vol. 115; no. 50; p. 12704
Main Authors: Pogorelyy, Mikhail V, Minervina, Anastasia A, Touzel, Maximilian Puelma, Sycheva, Anastasiia L, Komech, Ekaterina A, Kovalenko, Elena I, Karganova, Galina G, Egorov, Evgeniy S, Komkov, Alexander Yu, Chudakov, Dmitriy M, Mamedov, Ilgar Z, Mora, Thierry, Walczak, Aleksandra M, Lebedev, Yuri B
Format: Journal Article
Language:English
Published: United States 11.12.2018
Subjects:
Antigens, Viral - immunology
Humans
Immunization - methods
Receptors, Antigen, T-Cell - immunology
T-Lymphocytes - immunology
Tissue Donors
Twins, Monozygotic
Vaccination - methods
Yellow Fever Vaccine - immunology
twins
RepSeq
T cell receptor
high-throughput sequencing
vaccination
ISSN:1091-6490, 1091-6490
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:T cell receptor (TCR) repertoire data contain information about infections that could be used in disease diagnostics and vaccine development, but extracting that information remains a major challenge. Here we developed a statistical framework to detect TCR clone proliferation and contraction from longitudinal repertoire data. We applied this framework to data from three pairs of identical twins immunized with the yellow fever vaccine. We identified 600 to 1,700 responding TCRs in each donor and validated them using three independent assays. While the responding TCRs were mostly private, albeit with higher overlap between twins, they could be well-predicted using a classifier based on sequence similarity. Our method can also be applied to samples obtained postinfection, making it suitable for systematic discovery of new infection-specific TCRs in the clinic.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1091-6490
1091-6490
DOI:10.1073/pnas.1809642115