ArticleFrontiers in immunology2017
Specificity, Privacy, and Degeneracy in the CD4 T Cell Receptor Repertoire Following Immunization.
Article in Frontiers in immunology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 50 citations in OpenAlex.
- Single-cell RNA sequencing reveals clonally expanded CD4+ tissue-resident memory T cells in histidyl-tRNA synthetase-induced myositis.JCI insight · 2026Article
- The T-cell receptor repertoire of wild mice.Discovery immunology · 2026Article
- HIV-1 adapts to HLA class II-associated selection pressure exerted by CD4Science advances · 2025Article
- Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.Nucleic acids research · 2025Article
- Dynamics of The Γδtcr Repertoires During The Dedifferentiation Process and Pilot Implications for Immunotherapy of Thyroid Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- DeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention strategy on motifs.Frontiers in immunology · 2024Article
- Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm.Communications biology · 2023Article
- Article
- Anti-tumor memory CD4 and CD8 T-cells quantified by bulk T-cell receptor (TCR) clonal analysis.Frontiers in immunology · 2023Article
- simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods.GigaScience · 2022Article
- Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification.GigaScience · 2022Article
- Genomic and TCR profiling data reveal the distinct molecular traits in epithelial ovarian cancer histotypes.Oncogene · 2022Article
- Article
- DeepLION: Deep Multi-Instance Learning Improves the Prediction of Cancer-Associated T Cell Receptors for Accurate Cancer Detection.Frontiers in genetics · 2022Article
- Machine Learning Approaches to TCR Repertoire Analysis.Frontiers in immunology · 2022Review
- Comparative Study of Repertoire Classification Methods Reveals Data Efficiency ofFrontiers in immunology · 2022Article
- The clonal structure and dynamics of the human T cell response to an organic chemical hapten.eLife · 2021Article
- Increased Immune-Regulatory Receptor Expression on Effector T Cells as Early Indicators of Relapse Following Autologous Stem Cell Transplantation for Multiple Myeloma.Frontiers in immunology · 2021Article
- Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls.Frontiers in immunology · 2021Article
- MHC-II alleles shape the CDR3 repertoires of conventional and regulatory naïve CD4Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
T cells recognize antigen using a large and diverse set of antigen-specific receptors created by a complex process of imprecise somatic cell gene rearrangements. In response to antigen-/receptor-binding-specific T cells then divide to form memory and effector populations. We apply high-throughput sequencing to investigate the global changes in T cell receptor sequences following immunization with ovalbumin (OVA) and adjuvant, to understand how adaptive immunity achieves specificity. Each immunized mouse contained a predominantly private but related set of expanded CDR3β sequences. We used machine learning to identify common patterns which distinguished repertoires from mice immunized with adjuvant with and without OVA. The CDR3β sequences were deconstructed into sets of overlapping contiguous amino acid triplets. The frequencies of these motifs were used to train the linear programming boosting (LPBoost) algorithm LPBoost to classify between TCR repertoires. LPBoost could distinguish between the two classes of repertoire with accuracies above 80%, using a small subset of triplet sequences present at defined positions along the CDR3. The results suggest a model in which such motifs confer degenerate antigen specificity in the context of a highly diverse and largely private set of T cell receptors.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.