[T cell receptor (TCR) repertoire and common analytical tools for high-throughput sequencing].
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T cell receptor (TCR) is an integral part of T cell recognition antigen, and the sum of TCR of all T cells in an individual is called the TCR repertoire. Understanding the composition and characteristics of the TCR repertoire in healthy and pathological state helps us to gain an insight into adaptive immunity. At present, many analysis tools for high-throughput data have been developed, including basic composition analysis and downstream function analysis. The database of antigen-specific T cells established by researchers is fundamental to clinical research. The analysis of the functional characteristics of the TCR repertoire is critical to the understanding of TCR repertoire. However, the existing downstream analysis tools still requires further application and verification. This paper reviewed several commonly used TCR repertoire analysis tools, which hopefully can provide a reference for non-bioinformatics researchers in choosing analysis tools.Cite
ABSTRACT The antibody repertoire is a vast and diverse collection of B-cell receptors and antibodies that confer protection against a plethora of pathogens. The architecture of the antibody repertoire, defined by the network similarity landscape of its sequences, is unknown. Here, we established a novel high-performance computing platform to construct large-scale networks from high-throughput sequencing data (>100’000 unique antibodies), in order to uncover the architecture of antibody repertoires. We identified three fundamental principles of antibody repertoire architecture across B-cell development: reproducibility, robustness and redundancy. Reproducibility of network structure explains clonal expansion and selection. Robustness ensures a functional immune response even under extensive loss of clones (50%). Redundancy in mutational pathways suggests that there is a pre-programmed evolvability in antibody repertoires. Our analysis provides guidelines for a quantitative network analysis of antibody repertoires, which can be applied to other facets of adaptive immunity (e.g., T cell receptors), and may direct the construction of synthetic repertoires for biomedical applications.
Antibody Repertoire
Robustness
Evolvability
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Immune receptor
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Abstract Emerging high-throughput sequencing methods for the analyses of complex structure of TCR and BCR repertoires give a powerful impulse to adaptive immunity studies. However, there are still essential technical obstacles for performing a truly quantitative analysis. Specifically, it remains challenging to obtain comprehensive information on the clonal composition of small lymphocyte populations, such as Ag-specific, functional, or tissue-resident cell subsets isolated by sorting, microdissection, or fine needle aspirates. In this study, we report a robust approach based on unique molecular identifiers that allows profiling Ag receptors for several hundred to thousand lymphocytes while preserving qualitative and quantitative information on clonal composition of the sample. We also describe several general features regarding the data analysis with unique molecular identifiers that are critical for accurate counting of starting molecules in high-throughput sequencing applications.
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T and B cell repertoires are collections of lymphocytes, each characterised by its antigen-specific receptor. We review here classical technologies and analysis strategies developed to assess Immunoglobulin (IG) and T cell receptor (TR) repertoire diversity, and describe recent advances in the field. First, we describe the broad range of available methodological tools developed in the past decades, each of which answering different questions and showing complementarity for progressive identification of the level of repertoire alterations: global overview of the diversity by flow cytometry, IG repertoire descriptions at the protein level for the identification of IG reactivities, IG/TR CDR3 spectratyping strategies, and related molecular quantification or dynamics of T/B cell differentiation. Additionally, we introduce the recent technological advances in molecular biology tools allowing deeper analysis of IG/TR diversity by next-generation sequencing (NGS), offering systematic and comprehensive sequencing of IG/TR transcripts in a short amount of time. NGS provides several angles of analysis such as clonotype frequency, CDR3 diversity, CDR3 sequence analysis, V allele identification with a quantitative dimension, therefore requiring high-throughput analysis tools development. In this line, we discuss the recent efforts made for nomenclature standardisation and ontology development. We then present the variety of available statistical analysis and modelling approaches developed with regards to the various levels of diversity analysis, and reveal the increasing sophistication of modelling approaches. To conclude, we provide some examples of recent mathematical modelling strategies and perspectives that illustrate the active rise of a "next-generation" of repertoire analysis.
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Abstract Background Advances in next-generation sequencing (NGS) of antibody repertoires have led to an explosion in B cell receptor sequence data from donors with many different disease states. These data have the potential to detect patterns of immune response across populations. However, to this point it has been difficult to interpret such patterns of immune response between disease states in the absence of functional data. There is a need for a robust method that can be used to distinguish general patterns of immune responses at the antibody repertoire level. Results We developed a method for reducing the complexity of antibody repertoire datasets using principal component analysis (PCA) and refer to our method as “repertoire fingerprinting.” We reduce the high dimensional space of an antibody repertoire to just two principal components that explain the majority of variation in those repertoires. We show that repertoires from individuals with a common experience or disease state can be clustered by their repertoire fingerprints to identify common antibody responses. Conclusions Our repertoire fingerprinting method for distinguishing immune repertoires has implications for characterizing an individual disease state. Methods to distinguish disease states based on pattern recognition in the adaptive immune response could be used to develop biomarkers with diagnostic or prognostic utility in patient care. Extending our analysis to larger cohorts of patients in the future should permit us to define more precisely those characteristics of the immune response that result from natural infection or autoimmunity.
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Immune receptor
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Somatic assembly of T cell receptor (TCR) and B cell receptor (BCR) genes produces a vast diversity of lymphocyte antigen recognition capacity. The advent of efficient high throughput sequencing of lymphocyte antigen receptor genes has recently generated unprecedented opportunities for exploration of adaptive immune responses. With these opportunities have come significant challenges in understanding the analysis techniques that most accurately reflect underlying biological phenomena. In this regard, sample preparation and sequence analysis techniques, which have largely been borrowed and adapted from other fields, continue to evolve. Here we review current methods and challenges of library preparation, sequencing and statistical analysis of lymphocyte receptor repertoire studies. We discuss the general steps in the process of immune repertoire generation including sample preparation, platforms available for sequencing, processing of sequencing data, measurable features of the immune repertoire and the statistical tools that can be used for analysis and interpretation of the data. Because BCR analysis harbors additional complexities, such as immunoglobulin (Ig) (i.e. antibody) gene somatic hypermutation and class switch recombination, the emphasis of this review is on Ig/BCR sequence analysis.
breakpoint cluster region
Immunoglobulin gene
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High-throughput sequencing technologies have exposed the possibilities for the in-depth evaluation of T-cell receptor (TCR) repertoires. These studies are highly relevant to gain insights into human adaptive immunity and to decipher the composition and diversity of antigen receptors in physiological and disease conditions. The major objective of TCR sequencing data analysis is the identification of V, D and J gene segments, complementarity-determining region 3 (CDR3) sequence extraction and clonality analysis. With the advancement in sequencing technologies, new TCR analysis approaches and programs have been developed. However, there is still a deficit of systematic comparative studies to assist in the selection of an optimal analysis approach. Here, we present a detailed comparison of 10 state-of-the-art TCR analysis tools on samples with different complexities by taking into account many aspects such as clonotype detection [unique V(D)J combination], CDR3 identification or accuracy in error correction. We used our in silico and experimental data sets with known clonalities enabling the identification of potential tool biases. We also established a new strategy, named clonal plane, which allows quantifying and comparing the clonality of multiple samples. Our results provide new insights into the effect of method selection on analysis results, and it will assist users in the selection of an appropriate analysis method.
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Convergence across B-cell receptor (BCR) and antibody repertoires has become instrumental in prioritizing candidates in recent rapid therapeutic antibody discovery campaigns. It has also increased our understanding of the immune system, providing evidence for the preferential selection of BCRs to particular (immunodominant) epitopes post vaccination/infection. These important implications for both drug discovery and immunology mean that it is essential to consider the optimal way to combine experimental and computational technology when probing BCR repertoires for convergence signatures. Here, we discuss the theoretical basis for observing BCR repertoire functional convergence and explore factors of study design that can impact functional signal. We also review the computational arsenal available to detect antibodies with similar functional properties, highlighting opportunities enabled by recent clustering algorithms that exploit structural similarities between BCRs. Finally, we suggest future areas of development that should increase the power of BCR repertoire functional clustering.
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Abstract Recent advancements in single-cell immune profiling that enable the measurement of the transcriptome and T-cell receptor (TCR) sequences simultaneously have emerged as a promising approach to study immune responses at cellular resolution. Yet, combining these different types of information from multiple datasets into a joint representation is complicated by the unique characteristics of each modality and the technical effects between datasets. Here, we present mvTCR , a multimodal generative model to learn a unified representation across modalities and datasets for joint analysis of single-cell immune profiling data. We show that mvTCR allows the construction of large-scale and multimodal T-cell atlases by distilling modality-specific properties into a shared view, enabling unique and improved data analysis. Specifically, we demonstrated mvTCR’s potential by revealing and separating SARS-CoV-2-specific T-cell clusters from bystanders that would have been missed in individual unimodal data analysis. Finally, mvTCR can enable automated analysis of new datasets when combined with transfer-learning approaches. Overall, mvTCR provides a principled solution for standard analysis tasks such as multimodal integration, clustering, specificity analysis, and batch correction for single-cell immune profiling data.
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T-cell antigen receptor (TCR) variability enables the cellular immune system to discriminate between self and non-self. High-throughput TCR sequencing (TCR-seq) involves the use of next generation sequencing platforms to generate large numbers of short DNA sequences covering key regions of the TCR coding sequence, which enables quantification of T-cell diversity at unprecedented resolution. TCR-seq studies have provided new insights into the healthy human T-cell repertoire, such as revised estimates of repertoire size and the understanding that TCR specificities are shared among individuals more frequently than previously anticipated. In the context of disease, TCR-seq has been instrumental in characterizing the recovery of the immune repertoire after hematopoietic stem cell transplantation, and the method has been used to develop biomarkers and diagnostics for various infectious and neoplastic diseases. However, T-cell repertoire sequencing is still in its infancy. It is expected that maturation of the field will involve the introduction of improved, standardized tools for data handling, deposition and statistical analysis, as well as the emergence of new and equivalently large-scale technologies for T-cell functional analysis and antigen discovery. In this review, we introduce this nascent field and TCR-seq methodology, we discuss recent insights into healthy and diseased TCR repertoires, and we examine the applications and challenges for TCR-seq in the clinic.
Single cell sequencing
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