scholarly journals A framework for annotation of antigen specificities in high-throughput T-cell repertoire sequencing studies

2019 ◽  
Author(s):  
Mikhail V Pogorelyy ◽  
Mikhail Shugay
Keyword(s):  
T Cell ◽  
Large Scale ◽  
Cell Receptor ◽  
Specific Response ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Antigen Specificity ◽  
Cell Repertoire ◽  
Sequencing Studies ◽  
Repertoire Sequencing

AbstractRecently developed molecular methods allow large-scale profiling of T-cell receptor (TCR) sequences that encode for antigen specificity and immunological memory of these cells. However, it is well known, that the even unperturbed TCR repertoire structure is extremely complex due to the high diversity of TCR rearrangements and multiple biases imprinted by VDJ rearrangement process. The latter gives rise to the phenomenon of “public” TCR clonotypes that can be shared across multiple individuals and non-trivial structure of the TCR similarity network. Here we outline a framework for TCR sequencing data analysis that can control for these biases in order to infer TCRs that are involved in response to antigens of interest. Using an example dataset of donors with known HLA haplotype and CMV status we demonstrate that by applying HLA restriction rules and matching against a database of TCRs with known antigen specificity it is possible to robustly detect motifs of an epitope-specific responses in individual repertoires. We also highlight potential shortcomings of TCR clustering methods and demonstrate that highly expanded TCRs should be individually assessed to get the full picture of antigen-specific response.

scholarly journals Comprehensive analysis of structural and sequencing data reveals almost unconstrained chain pairing in TCRαβ complex

2019 ◽  
Author(s):  
Dmitrii S Shcherbinin ◽  
Vlad A Belousov ◽  
Mikhail Shugay
Keyword(s):  
T Cells ◽  
T Cell ◽  
Structural Data ◽  
Cell Receptor ◽  
T Cell Subsets ◽  
Sequencing Data ◽  
Antigen Specificity ◽  
Cell Repertoire ◽  
Effective Response ◽  
Specific Choice

AbstractAntigen recognition by T-cells is guided by the T-cell receptor (TCR) heterodimer formed by α and β chains. A huge diversity of TCR sequences should be maintained by the immune system in order to be able to mount an effective response towards foreign pathogens, so, due to cooperative binding of α and β chains to the pathogen, any constraints on chain pairing can have a profound effect on immune repertoire structure, diversity and antigen specificity. By integrating available structural data and paired chain sequencing results we were able to show that there are almost no constraints on pairing in TCRαβ complexes, allowing naive T-cell repertoire to reach the highest possible diversity. Additional analysis reveals that the specific choice of contacting amino acids can still have a profound effect on complex conformation. Moreover, antigen-driven selection can distort the uniform landscape of chain pairing, while small, yet significant, differences in the pairing can be attributed to various specialized T-cell subsets such as MAIT and iNKT T-cells, as well as other putative invariant TCRs.

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 Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
William S DeWitt ◽  
Anajane Smith ◽  
Gary Schoch ◽  
John A Hansen ◽  
Frederick A Matsen ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Genetic Background ◽  
Cell Receptor ◽  
Sequencing Data ◽  
Human T Cell ◽  
Disease Associations ◽  
Repertoire Sequencing ◽  
Mhc Polymorphism ◽  
Occurrence Patterns

The T cell receptor (TCR) repertoire encodes immune exposure history through the dynamic formation of immunological memory. Statistical analysis of repertoire sequencing data has the potential to decode disease associations from large cohorts with measured phenotypes. However, the repertoire perturbation induced by a given immunological challenge is conditioned on genetic background via major histocompatibility complex (MHC) polymorphism. We explore associations between MHC alleles, immune exposures, and shared TCRs in a large human cohort. Using a previously published repertoire sequencing dataset augmented with high-resolution MHC genotyping, our analysis reveals rich structure: striking imprints of common pathogens, clusters of co-occurring TCRs that may represent markers of shared immune exposures, and substantial variations in TCR-MHC association strength across MHC loci. Guided by atomic contacts in solved TCR:peptide-MHC structures, we identify sequence covariation between TCR and MHC. These insights and our analysis framework lay the groundwork for further explorations into TCR diversity.

scholarly journals Probing T-cell response by sequence-based probabilistic modeling

2020 ◽  
Author(s):  
Barbara Bravi ◽  
Vinod P. Balachandran ◽  
Benjamin D. Greenbaum ◽  
Aleksandra M. Walczak ◽  
Thierry Mora ◽  
...  
Keyword(s):  
T Cell ◽  
Probabilistic Models ◽  
Cell Receptor ◽  
T Cell Response ◽  
Specific Response ◽  
Sequence Motifs ◽  
Antigen Specificity ◽  
Human T Cell ◽  
Machine Learning Approach ◽  
Tcr Repertoire

AbstractWith the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can therefore be used to identify both experimental and condition specific responding TCRs, of great importance to advancing personalized immunotherapies.

scholarly journals Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles

2015 ◽  
Vol 112 (8) ◽  
pp. E862-E870 ◽  
Author(s):  
Daniel Gadala-Maria ◽  
Gur Yaari ◽  
Mohamed Uduman ◽  
Steven H. Kleinstein
Keyword(s):  
B Cell ◽  
Large Scale ◽  
Gene Segment ◽  
Automated Analysis ◽  
Sequencing Data ◽  
Cell Repertoire ◽  
Accurate Analysis ◽  
V Genes ◽  
Repertoire Sequencing ◽  
Novel Alleles

Individual variation in germline and expressed B-cell immunoglobulin (Ig) repertoires has been associated with aging, disease susceptibility, and differential response to infection and vaccination. Repertoire properties can now be studied at large-scale through next-generation sequencing of rearranged Ig genes. Accurate analysis of these repertoire-sequencing (Rep-Seq) data requires identifying the germline variable (V), diversity (D), and joining (J) gene segments used by each Ig sequence. Current V(D)J assignment methods work by aligning sequences to a database of known germline V(D)J segment alleles. However, existing databases are likely to be incomplete and novel polymorphisms are hard to differentiate from the frequent occurrence of somatic hypermutations in Ig sequences. Here we develop a Tool for Ig Genotype Elucidation via Rep-Seq (TIgGER). TIgGER analyzes mutation patterns in Rep-Seq data to identify novel V segment alleles, and also constructs a personalized germline database containing the specific set of alleles carried by a subject. This information is then used to improve the initial V segment assignments from existing tools, like IMGT/HighV-QUEST. The application of TIgGER to Rep-Seq data from seven subjects identified 11 novel V segment alleles, including at least one in every subject examined. These novel alleles constituted 13% of the total number of unique alleles in these subjects, and impacted 3% of V(D)J segment assignments. These results reinforce the highly polymorphic nature of human Ig V genes, and suggest that many novel alleles remain to be discovered. The integration of TIgGER into Rep-Seq processing pipelines will increase the accuracy of V segment assignments, thus improving B-cell repertoire analyses.

scholarly journals Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data

2020 ◽  
Author(s):  
Gregor Sturm ◽  
Tamas Szabo ◽  
Georgios Fotakis ◽  
Marlene Haider ◽  
Dietmar Rieder ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Cell Receptor ◽  
Sequencing Data ◽  
Seamless Integration ◽  
T Cell Phenotypes ◽  
Cell Phenotypes

AbstractSummaryAdvances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we propose Scirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data.Availability and implementationScirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

scholarly journals Early life imprints the hierarchy of T cell clone sizes

2020 ◽  
Author(s):  
Mario U. Gaimann ◽  
Maximilian Nguyen ◽  
Jonathan Desponds ◽  
Andreas Mayer
Keyword(s):  
T Cell ◽  
Time Window ◽  
Cell Clone ◽  
Clonal Selection ◽  
Scaling Law ◽  
Cell Receptor ◽  
Sequencing Data ◽  
Cell Repertoire ◽  
Human T Cell ◽  
T Cell Clone

The adaptive immune system responds to pathogens by selecting clones of cells with specific receptors. While clonal selection in response to particular antigens has been studied in detail, it is unknown how a lifetime of exposures to many antigens collectively shape the immune repertoire. Here, through mathematical modeling and statistical analyses of T cell receptor sequencing data we demonstrate that clonal expansions during a perinatal time window leave a long-lasting imprint on the human T cell repertoire. We demonstrate how the empirical scaling law relating the rank of the largest clones to their size can emerge from clonal growth during repertoire formation. We statistically identify early founded clones and find that they are indeed highly enriched among the largest clones. This enrichment persists even after decades of human aging, in a way that is quantitatively predicted by a model of fluctuating clonal selection. Our work presents a quantitative theory of human T cell dynamics compatible with the statistical laws of repertoire organization and provides a mechanism for how early clonal dynamics imprint the hierarchy of T cell clone sizes with implications for pathogen defense and autoimmunity.

scholarly journals Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data

2020 ◽  
Vol 36 (18) ◽  
pp. 4817-4818 ◽  
Author(s):  
Gregor Sturm ◽  
Tamas Szabo ◽  
Georgios Fotakis ◽  
Marlene Haider ◽  
Dietmar Rieder ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Large Scale ◽  
Single Cells ◽  
Cell Receptor ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Seamless Integration ◽  
T Cell Phenotypes ◽  
Cell Phenotypes

Abstract Summary Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementation Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Early life imprints the hierarchy of T cell clone sizes

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Mario U Gaimann ◽  
Maximilian Nguyen ◽  
Jonathan Desponds ◽  
Andreas Mayer
Keyword(s):  
T Cell ◽  
Time Window ◽  
Cell Clone ◽  
Clonal Selection ◽  
Adult Life ◽  
Cell Receptor ◽  
Sequencing Data ◽  
Cell Repertoire ◽  
Human T Cell ◽  
T Cell Clone

The adaptive immune system responds to pathogens by selecting clones of cells with specific receptors. While clonal selection in response to particular antigens has been studied in detail, it is unknown how a lifetime of exposures to many antigens collectively shape the immune repertoire. Here, using mathematical modeling and statistical analyses of T cell receptor sequencing data we develop a quantitative theory of human T cell dynamics compatible with the statistical laws of repertoire organization. We find that clonal expansions during a perinatal time window leave a long-lasting imprint on the human T cell repertoire, which is only slowly reshaped by fluctuating clonal selection during adult life. Our work provides a mechanism for how early clonal dynamics imprint the hierarchy of T cell clone sizes with implications for pathogen defense and autoimmunity.

scholarly journals VisTCR: An Interactive Software for T Cell Repertoire Sequencing Data Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Qingshan Ni ◽  
Jianyang Zhang ◽  
Zihan Zheng ◽  
Gang Chen ◽  
Laura Christian ◽  
...  
Keyword(s):  
Data Analysis ◽  
T Cell ◽  
Interactive Software ◽  
T Cell Repertoire ◽  
Sequencing Data ◽  
Cell Repertoire ◽  
Repertoire Sequencing ◽  
Sequencing Data Analysis
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