scholarly journals Predicting antigen-specificity of single T-cells based on TCR CDR3 regions

2019 ◽  
Author(s):  
David S. Fischer ◽  
Yihan Wu ◽  
Benjamin Schubert ◽  
Fabian J. Theis
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Sequence Data ◽  
Cell Receptor ◽  
Categorical Variables ◽  
Learning Approaches ◽  
Antigen Specificity ◽  
Multiple Sequence ◽  
Large Sets ◽  
Epitope Sequence

It has recently become possible to assay T-cell specificity with respect to large sets of antigens as well as T-cell receptor sequence in high-throughput single-cell experiments. We propose multiple sequence-data specific deep learning approaches to impute TCR to epitope specificity to reduce the complexity of new experiments. We found that models that treat antigens as categorical variables outperform those which model the TCR and epitope sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates.

scholarly journals Single-cell transcriptomics identifies multiple pathways underlying antitumor function of TCR- and CD8αβ-engineered human CD4+ T cells

2020 ◽  
Vol 6 (27) ◽  
pp. eaaz7809 ◽  
Author(s):  
Jan A. Rath ◽  
Gagan Bajwa ◽  
Benoit Carreres ◽  
Elisabeth Hoyer ◽  
Isabelle Gruber ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Function ◽  
Xenograft Model ◽  
Cell Receptor ◽  
Target Cells ◽  
Antigen Specificity ◽  
Molecular Features ◽  
Mouse Xenograft Model

Transgenic coexpression of a class I–restricted tumor antigen–specific T cell receptor (TCR) and CD8αβ (TCR8) redirects antigen specificity of CD4+ T cells. Reinforcement of biophysical properties and early TCR signaling explain how redirected CD4+ T cells recognize target cells, but the transcriptional basis for their acquired antitumor function remains elusive. We, therefore, interrogated redirected human CD4+ and CD8+ T cells by single-cell RNA sequencing and characterized them experimentally in bulk and single-cell assays and a mouse xenograft model. TCR8 expression enhanced CD8+ T cell function and preserved less differentiated CD4+ and CD8+ T cells after tumor challenge. TCR8+CD4+ T cells were most potent by activating multiple transcriptional programs associated with enhanced antitumor function. We found sustained activation of cytotoxicity, costimulation, oxidative phosphorylation– and proliferation-related genes, and simultaneously reduced differentiation and exhaustion. Our study identifies molecular features of TCR8 expression that can guide the development of enhanced immunotherapies.

scholarly journals Dual TCR-alpha expression on MAIT cells as a potential confounder of TCR interpretation

2021 ◽  
Author(s):  
Sara Suliman ◽  
Lars Kjer-Nielsen ◽  
Sarah K. Iwany ◽  
Kattya Lopez Tamara ◽  
Liyen Loh ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
Cell Receptor ◽  
Antigen Specificity ◽  
Mait Cells ◽  
Human T Cells ◽  
V Genes ◽  
Antigen Presenting ◽  
Β Chain

AbstractMucosal-associated invariant T (MAIT) cells are innate-like T cells that are highly abundant in human blood and tissues. Most MAIT cells have an invariant T cell receptor (TCR) α chain that uses TRAV1-2 joined to TRAJ33/20/12 and recognize metabolites from bacterial riboflavin synthesis bound to the antigen-presenting molecule, MR1. Recently, our attempts to identify alternative MR1-presented antigens led to the discovery of rare MR1-restricted T cells with non-TRAV1-2 TCRs. Because altered antigen specificity is likely to lead to altered affinity for the most potent known antigen, 5-(2-oxopropylideneamino)-6-D-ribitylaminouracil (5-OP-RU), we performed bulk TCRα and β chain sequencing, and single cell-based paired TCR sequencing, on T cells that bound the MR1-5-OP-RU tetramer, but with differing intensities. Bulk sequencing showed that use of V genes other than TRAV1-2 was enriched among MR1-5-OP-RU tetramerlow cells. Whereas we initially interpreted these as diverse MR1-restricted TCRs, single cell TCR sequencing revealed that cells expressing atypical TCRα chains also co-expressed an invariant MAIT TCRα chain. Transfection of each non-TRAV1-2 TCRα chain with the TCRβ chain from the same cell demonstrated that the non-TRAV1-2 TCR did not bind the MR1-5-OP-RU tetramer. Thus, dual TCRα chain expression in human T cells and competition for the endogenous β chain explains the existence of some MR1-5-OP-RU tetramerlow T cells. The discovery of simultaneous expression of canonical and non-canonical TCRs on the same T cell means that claims of roles for non-TRAV1-2 TCR in MR1 response must be validated by TCR transfer-based confirmation of antigen specificity.

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 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 Mapping the immune environment in clear cell renal carcinoma by single-cell genomics

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Nicholas Borcherding ◽  
Ajaykumar Vishwakarma ◽  
Andrew P. Voigt ◽  
Andrew Bellizzi ◽  
Jacob Kaplan ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Immune Cells ◽  
Clear Cell ◽  
Cell Receptor ◽  
Renal Tissue ◽  
High Response Rate ◽  
Cell Renal Cell Carcinoma ◽  
Tumor Mutational Burden ◽  
Macrophage Populations

AbstractClear cell renal cell carcinoma (ccRCC) is one of the most immunologically distinct tumor types due to high response rate to immunotherapies, despite low tumor mutational burden. To characterize the tumor immune microenvironment of ccRCC, we applied single-cell-RNA sequencing (SCRS) along with T-cell-receptor (TCR) sequencing to map the transcriptomic heterogeneity of 25,688 individual CD45+ lymphoid and myeloid cells in matched tumor and blood from three patients with ccRCC. We also included 11,367 immune cells from four other individuals derived from the kidney and peripheral blood to facilitate the identification and assessment of ccRCC-specific differences. There is an overall increase in CD8+ T-cell and macrophage populations in tumor-infiltrated immune cells compared to normal renal tissue. We further demonstrate the divergent cell transcriptional states for tumor-infiltrating CD8+ T cells and identify a MKI67 + proliferative subpopulation being a potential culprit for the progression of ccRCC. Using the SCRS gene expression, we found preferential prediction of clinical outcomes and pathological diseases by subcluster assignment. With further characterization and functional validation, our findings may reveal certain subpopulations of immune cells amenable to therapeutic intervention.

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 clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences

2021 ◽  
Author(s):  
Sebastiaan Valkiers ◽  
Max Van Houcke ◽  
Kris Laukens ◽  
Pieter Meysman
Keyword(s):  
T Cell ◽  
Large Data ◽  
Cell Receptor ◽  
Amino Acid Sequences ◽  
Large Data Sets ◽  
Data Sets ◽  
Clustering Methods ◽  
Link Type ◽  
Large Sets ◽  
Similar Accuracy

The T-cell receptor (TCR) determines the specificity of a T-cell towards an epitope. As of yet, the rules for antigen recognition remain largely undetermined. Current methods for grouping TCRs according to their epitope specificity remain limited in performance and scalability. Multiple methodologies have been developed, but all of them fail to efficiently cluster large data sets exceeding 1 million sequences. To account for this limitation, we developed clusTCR, a rapid TCR clustering alternative that efficiently scales up to millions of CDR3 amino acid sequences. Benchmarking comparisons revealed similar accuracy of clusTCR with other TCR clustering methods. clusTCR offers a drastic improvement in clustering speed, which allows clustering of millions of TCR sequences in just a few minutes through efficient similarity searching and sequence hashing.clusTCR was written in Python 3. It is available as an anaconda package (https://anaconda.org/svalkiers/clustcr) and on github (https://github.com/svalkiers/clusTCR).

scholarly journals 192 Pooled T cell receptor screening (POTS) provides unbiased, high-throughput method for TCR discovery

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A204-A204
Author(s):  
Jack Reid ◽  
Shihong Zhang ◽  
Ariunaa Munkhbat ◽  
Matyas Ecsedi ◽  
Megan McAfee ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
High Throughput ◽  
Ex Vivo ◽  
Cell Receptor ◽  
High Avidity ◽  
Reporter System ◽  
High Throughput Method

BackgroundT Cell Receptor (TCR)-T cell therapies have shown some promising results in cancer clinical trials, however the efficacy of treatment remains suboptimal. Outcomes could potentially be improved by utilizing highly functional TCRs for future trials. Current TCR discovery methods are relatively low throughput and rely on synthesis and screening of individual TCRs based on tetramer binding and peptide specificity, which is costly and labor intensive. We have developed and validated a pooled approach relying on directly cloned TCRs transduced into a fluorescent Jurkat reporter system (figure 1). This approach provides an unbiased, high-throughput method for TCR discovery.MethodsAs a model for POTS, T cells specific for a peptide derived adenovirus structural protein were sorted on tetramer and subjected to 10x single cell VDJ analysis. Pools of randomly paired TCR alpha and beta chains were cloned from the 10x cDNA into a lentiviral vector and transduced into a Jurkat reporter cells. Consecutive stimulations with cognate antigen followed by cell sorts were performed to enrich for functional TCRs. Full length TCRab pools were sequenced by Oxford Nanopore Technologies (ONT) and compared to a 10x dataset to find naturally paired TCRs.ResultsComparison between the ex vivo single cell VDJ sequencing and ONT sequencing of the transduced antigen specific TCRs showed more than 99% of the TCR pairs found in reporter positive Jurkat cells were naturally paired TCRs. The functionality of 8 TCR clonotypes discovered using POTS were compared and clone #2 showed the strongest response. Of the selected clonotypes, clone #2 showed a low frequency of 0.9% in the ex vivo single cell VDJ sequencing. After the first round of stimulation and sequencing, clone #2 takes up of 5% of all reporter-positive clones. The abundance of clone #2 further increased to 17% after another round of stimulation, sorting and sequencing, suggesting this method can retrieve and enrich for highly functional antigen specific TCRs.Abstract 192 Figure 1Outline of the POTS workflow.ConclusionsPOTS provides a high-throughput method for discovery of naturally paired, high-avidity T cell receptors. This method mitigates bias introduced by T cell differentiation state by screening TCRs in a clonal reporter system. Additionally, POTS allows for screening of low abundance clones when compared with traditional TCR discovery techniques. Pooled TCRs could also be screened in vivo with primary T cells in a mouse model to screen for the most functional and physiologically fit TCR for cancer treatment.

scholarly journals Follicular T cells are clonally and transcriptionally distinct in B cell-driven mouse autoimmune disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Elliot H. Akama-Garren ◽  
Theo van den Broek ◽  
Lea Simoni ◽  
Carlos Castrillon ◽  
Cees E. van der Poel ◽  
...  
Keyword(s):  
T Cells ◽  
Autoimmune Disease ◽  
B Cell ◽  
Single Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Germinal Centers ◽  
Learning Approaches ◽  
Differential Gene ◽  
Disease Antigen

AbstractPathogenic autoantibodies contribute to tissue damage and clinical decline in autoimmune disease. Follicular T cells are central regulators of germinal centers, although their contribution to autoantibody-mediated disease remains unclear. Here we perform single cell RNA and T cell receptor (TCR) sequencing of follicular T cells in a mouse model of autoantibody-mediated disease, allowing for analyses of paired transcriptomes and unbiased TCRαβ repertoires at single cell resolution. A minority of clonotypes are preferentially shared amongst autoimmune follicular T cells and clonotypic expansion is associated with differential gene signatures in autoimmune disease. Antigen prediction using algorithmic and machine learning approaches indicates convergence towards shared specificities between non-autoimmune and autoimmune follicular T cells. However, differential autoimmune transcriptional signatures are preserved even amongst follicular T cells with shared predicted specificities. These results demonstrate that follicular T cells are phenotypically distinct in B cell-driven autoimmune disease, providing potential therapeutic targets to modulate autoantibody development.

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