scholarly journals A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity

2021 ◽  
Vol 7 (20) ◽  
pp. eabf5835
Author(s):  
Wen Zhang ◽  
Peter G. Hawkins ◽  
Jing He ◽  
Namita T. Gupta ◽  
Jinrui Liu ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Adaptive Immune System ◽  
Cell Receptor ◽  
Immune Monitoring ◽  
Binding Assays ◽  
Antigen Specificity ◽  
Interaction Map ◽  
Antigen Interaction ◽  
Context Specific

T cell receptor (TCR) antigen–specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide–major histocompatibility complex) interactions and a neural network–based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen–specific interactions for basic immunological research and clinical immune monitoring.

scholarly journals Analysis of T-Cell Receptor Repertoire in Transplantation: Fingerprint of T Cell-mediated Alloresponse

2022 ◽  
Vol 12 ◽  
Author(s):  
Guangyao Tian ◽  
Mingqian Li ◽  
Guoyue Lv
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Immune Monitoring ◽  
Antigen Specificity ◽  
Cell Repertoire ◽  
Receptor Repertoire ◽  
Cell Clones ◽  
Alloreactive T Cell

T cells play a key role in determining allograft function by mediating allogeneic immune responses to cause rejection, and recent work pointed their role in mediating tolerance in transplantation. The unique T-cell receptor (TCR) expressed on the surface of each T cell determines the antigen specificity of the cell and can be the specific fingerprint for identifying and monitoring. Next-generation sequencing (NGS) techniques provide powerful tools for deep and high-throughput TCR profiling, and facilitate to depict the entire T cell repertoire profile and trace antigen-specific T cells in circulation and local tissues. Tailing T cell transcriptomes and TCR sequences at the single cell level provides a full landscape of alloreactive T-cell clones development and biofunction in alloresponse. Here, we review the recent advances in TCR sequencing techniques and computational tools, as well as the recent discovery in overall TCR profile and antigen-specific T cells tracking in transplantation. We further discuss the challenges and potential of using TCR sequencing-based assays to profile alloreactive TCR repertoire as the fingerprint for immune monitoring and prediction of rejection and tolerance.

scholarly journals GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hongyi Zhang ◽  
Xiaowei Zhan ◽  
Bo Li
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Large Scale ◽  
Cell Receptor ◽  
Alignment Algorithm ◽  
Computationally Efficient ◽  
Antigen Specificity ◽  
Non Invasive ◽  
Isometric Transformation ◽  
Specific Receptors

AbstractSimilarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

scholarly journals In Vivo Selection of T-Cell Receptor Junctional Region Sequences by HLA-A2 Human T-Cell Lymphotropic Virus Type 1 Tax11-19 Peptide Complexes

2001 ◽  
Vol 75 (2) ◽  
pp. 1065-1071 ◽  
Author(s):  
Mineki Saito ◽  
Graham P. Taylor ◽  
Akiko Saito ◽  
Yoshitaka Furukawa ◽  
Koichiro Usuku ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
Virus Type ◽  
Cell Receptor ◽  
Antigen Specificity ◽  
Human T Cell ◽  
Cdr3 Region

ABSTRACT Using HLA-peptide tetrameric complexes, we isolated human T-cell lymphotrophic virus type 1 Tax peptide-specific CD8+ T cells ex vivo. Antigen-specific amino acid motifs were identified in the T-cell receptor Vβ CDR3 region of clonally expanded CD8+ T cells. This result directly confirms the importance of the CDR3 region in determining the antigen specificity in vivo.

scholarly journals A Framework to Identify Antigen-Expanded T Cell Receptor Clusters Within Complex Repertoires

2021 ◽  
Vol 12 ◽  
Author(s):  
Valentina Ceglia ◽  
Erin J. Kelley ◽  
Annalee S. Boyle ◽  
Sandra Zurawski ◽  
Heather L. Mead ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
T Cell Responses ◽  
Cell Receptor ◽  
Antigen Specificity ◽  
Protein Antigens ◽  
Cell Responses ◽  
Tcr Repertoire ◽  
Receptor Clusters ◽  
Theoretical Sensitivity

Common approaches for monitoring T cell responses are limited in their multiplexity and sensitivity. In contrast, deep sequencing of the T Cell Receptor (TCR) repertoire provides a global view that is limited only in terms of theoretical sensitivity due to the depth of available sampling; however, the assignment of antigen specificities within TCR repertoires has become a bottleneck. This study combines antigen-driven expansion, deep TCR sequencing, and a novel analysis framework to show that homologous ‘Clusters of Expanded TCRs (CETs)’ can be confidently identified without cell isolation, and assigned to antigen against a background of non-specific clones. We show that clonotypes within each CET respond to the same epitope, and that protein antigens stimulate multiple CETs reactive to constituent peptides. Finally, we demonstrate the personalized assignment of antigen-specificity to rare clones within fully-diverse uncultured repertoires. The method presented here may be used to monitor T cell responses to vaccination and immunotherapy with high fidelity.

scholarly journals T cell and non-T cell compartments can independently determine resistance to Leishmania major.

1995 ◽  
Vol 181 (3) ◽  
pp. 845-855 ◽  
Author(s):  
A H Shankar ◽  
R G Titus
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
Leishmania Major ◽  
Cell Receptor ◽  
Disease Outcome ◽  
Chimeric Mice ◽  
Antigen Specificity ◽  
Ifn Gamma ◽  
Cell Compartments

In experimental murine cutaneous leishmaniasis caused by Leishmania major (Lm), the cellular determinants governing development of protective or exacerbative T cells are not well understood. We, therefore, attempted to determine the influence of T cell and non-T cell compartments on disease outcome. To this end, T cell chimeric mice were constructed using adult thymectomized lethally irradiated, bone marrow-reconstituted (ATXBM) animals of genetically resistant, C57BL/6, or susceptible, BALB/c, backgrounds. These hosts were engrafted with naive T cell populations from H-2-congenic susceptible, BALB.B6-H-2b, or resistant, C57BL/6.C-H-2d, animals, respectively. Chimeric mice were then infected with Lm, and disease outcome was monitored. BALB/c T cell chimeric mice, BALB/c ATXBM hosts given naive C57BL/6.C-H-2d T cells, resolved their infections as indicated by reductions in both lesion size and parasite numbers. Furthermore, the mice developed typical Th1 (interferon[IFN]-gamma hiinterleukin[IL]-4lo) cytokine patterns. In contrast, both sham chimeric, BALB/c ATXBM hosts given naive BALB/c T cells, and control irradiated euthymic mice succumbed to infection, producing Th2 profiles (IFN-gamma loIL-4hiIL-10hi). C57BL/6 T cell chimeras, C57BL/6 ATXBM hosts given naive BALB.B6-H-2b T cells, resolved their infections as did C57BL/6 sham chimeras and euthymic controls. Interestingly, whereas C57BL/6 control animals produced Th1 cytokines, chimeric animals progressed from Th0 (IFN-gamma hiIL-4hiIL-10hi) to Th2 (IFN-gamma loIL-4hiIL-10hi) cytokine profiles as cure ensued. Both reconstitution and chimeric status of all mice were confirmed by flow cytometry. In addition, T cell receptor V beta usage of Lm-specific blasts was determined. In all cases, V beta use was multiclonal, involving primarily V beta 2, 4, 6, 8.1, 8.2, 8.3, 10, and 14, with relative V beta frequencies differing between H-2b and H-2d animals. Most importantly, however, these differences did not segregate between cure and noncure outcomes. These findings indicate that: (a) genetic traits determining cure in Lm infection can direct disease outcome from both T cell and non-T cell compartments; (b) the presence of the curing genotype in only one compartment is sufficient to confer cure; (c) curing genotype T cells autonomously assume a Th1 cytokine profile-mediating cure; (d) noncuring genotype T cells can mediate cure in a curing environment, despite the onset of Th2 cytokine production; and lastly, (e) antigen specificity of responding T cells, as assessed by V beta T cell receptor diversity, is not a critical determinant of disease outcome.

scholarly journals Generation of diversity in T cell receptor repertoire specific for pigeon cytochrome c.

1987 ◽  
Vol 165 (2) ◽  
pp. 279-301 ◽  
Author(s):  
S B Sorger ◽  
S M Hedrick ◽  
P J Fink ◽  
M A Bookman ◽  
L A Matis
Keyword(s):  
T Cell ◽  
Cytochrome C ◽  
T Cell Receptor ◽  
Cell Lines ◽  
Dna Sequences ◽  
Cell Receptor ◽  
T Cell Response ◽  
Beta Chain ◽  
Antigen Specificity ◽  
T Cell Lines

17 T cell clones and 3 T cell lines, specific for pigeon cytochrome c, were analyzed for fine specificity and rearranged T cell receptor (TCR) gene elements. Clones of similar fine specificities were grouped into one of four phenotypes, and correlations between phenotype differences and gene usage could be made. All the lines and clones rearranged a member of the V alpha 2B4 gene family to a limited number of J alpha regions. The beta chain was made up of one of three non-cross-hybridizing V beta regions, each rearranging to only one or two J beta s. The use of alternate V beta regions could be correlated with phenotype differences, which were manifested either as MHC- or MHC and antigen-specificity changes. In addition, the presence of alloreactivity, which defined a phenotype difference, could be correlated solely with the use of an alternate J alpha region. These observations were substantiated by prospective analyses of pigeon cytochrome c-specific T cell lines that were selected for alternate MHC specificity or alloreactivity and were found to express the correlated alpha and beta chain rearrangements. Previously, the TCR DNA sequences from two clones, each representing a variant of one phenotype, showed sequence differences only in the N regions of their TCR genes. Since only these two variants, using identical V alpha-J alpha and V beta-J beta gene elements, were repeatedly observed in this study, we would predict that the junctional diversity differences are selectable. In this T cell response, all the gene elements involved in the generation of diversity appear to be selected, and may therefore be important in the determination of TCR specificity. This high degree of receptor gene selection represents a fundamental difference from the diversity seen in several extensively analyzed antibody responses.

THE FATE OF DONOR T-CELL RECEPTOR TRANSGENIC T CELLS WITH KNOWN HOST ANTIGEN SPECIFICITY IN A GRAFT-VERSUS-HOST DISEASE MODEL1

1999 ◽  
Vol 68 (1) ◽  
pp. 141-149 ◽  
Author(s):  
Bimalangshu Dey ◽  
Yong-Guang Yang ◽  
Frederic Preffer ◽  
Akira Shimizu ◽  
Kirsten Swenson ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
Graft Versus Host Disease ◽  
Cell Receptor ◽  
Antigen Specificity ◽  
Host Disease ◽  
Graft Versus Host

scholarly journals CD4+ T cell subsets present stable relationships in their T cell receptor repertoires

2020 ◽  
Author(s):  
Shiyu Wang ◽  
Longlong Wang ◽  
Ya Liu
Keyword(s):  
T Cells ◽  
Cell Differentiation ◽  
T Cell ◽  
T Cell Receptor ◽  
Cd4 T Cells ◽  
Cell Receptor ◽  
T Cell Subsets ◽  
Cd4 T Cell ◽  
Antigen Specificity ◽  
Sequence Composition

AbstractCD4+ T cells are key components of adaptive immunity. The cell differentiation equips CD4+ T cells with new functions. However, the effect of cell differentiation on T cell receptor (TCR) repertoire is not investigated. Here, we examined the features of TCR beta (TCRB) repertoire of the top clones within naïve, memory and regular T cell (Treg) subsets: repertoire structure, gene usage, length distribution and sequence composition. First, we found that memory subsets and Treg would be discriminated from naïve by the features of TCRB repertoire. Second, we found that the correlations between the features of memory subsets and naïve were positively related to differentiation levels of memory subsets. Third, we found that public clones presented a reduced proportion and a skewed sequence composition in differentiated subsets. Furthermore, we found that public clones led naïve to recognize a broader spectrum of antigens than other subsets. Our findings suggest that TCRB repertoire of CD4+ T cell subsets is skewed in a differentiation-depended manner. Our findings show that the variations of public clones contribute to these changes. Our findings indicate that the reduce of public clones in differentiation trim the antigen specificity of CD4+ T cells. The study unveils the physiological effect of memory formation and facilitates the selection of proper CD4+ subset for cellular therapy.

scholarly journals TCR3d: The T cell receptor structural repertoire database

2019 ◽  
Vol 35 (24) ◽  
pp. 5323-5325 ◽  
Author(s):  
Ragul Gowthaman ◽  
Brian G Pierce
Keyword(s):  
T Cell ◽  
T Cell Receptors ◽  
Adaptive Immune System ◽  
Cell Receptor ◽  
Binding Mode ◽  
Computational Techniques ◽  
Antigen Specificity ◽  
Cell Receptors ◽  
Gene Usage ◽  
Mechanistic Basis

Abstract Summary T cell receptors (TCRs) are critical molecules of the adaptive immune system, capable of recognizing diverse antigens, including peptides, lipids and small molecules, and represent a rapidly growing class of therapeutics. Determining the structural and mechanistic basis of TCR targeting of antigens is a major challenge, as each individual has a vast and diverse repertoire of TCRs. Despite shared general recognition modes, diversity in TCR sequence and recognition represents a challenge to predictive modeling and computational techniques being developed to predict antigen specificity and mechanistic basis of TCR targeting. To this end, we have developed the TCR3d database, a resource containing all known TCR structures, with a particular focus on antigen recognition. TCR3d provides key information on antigen binding mode, interface features, loop sequences and germline gene usage. Users can interactively view TCR complex structures, search sequences of interest against known structures and sequences, and download curated datasets of structurally characterized TCR complexes. This database is updated on a weekly basis, and can serve the community as a centralized resource for those studying T cell receptors and their recognition. Availability and implementation The TCR3d database is available at https://tcr3d.ibbr.umd.edu/.

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