TRIg: a robust alignment pipeline for non-regular T-cell receptor and immunoglobulin sequences

Unraveling the chicken T cell repertoire with enhanced genome annotation

T cell receptor (TCR) repertoire sequencing has emerged as a powerful tool for understanding the diversity and functionality of T cells within the host immune system. Yet, the chicken TCR repertoire remains poorly understood due to incomplete genome annotation of the TCR loci, despite the importance of chickens in agriculture and as an immunological model. Here, we addressed this critical issue by employing 5' rapid amplification of complementary DNA ends (5'RACE) TCR repertoire sequencing with molecular barcoding of complementary DNA (cDNA) molecules. Simultaneously, we enhanced the genome annotation of TCR Variable (V), Diversity (D, only present in β and δ loci) and Joining (J) genes in the chicken genome. To enhance the efficiency of TCR annotations, we developed VJ-gene-finder, an algorithm designed to extract VJ gene candidates from deoxyribonucleic acid (DNA) sequences. Using this tool, we achieved a comprehensive annotation of all known chicken TCR loci, including the α/δ locus on chromosome 27. Evolutionary analysis revealed that each locus evolved separately by duplication of long homology units. To define the baseline TCR diversity in healthy chickens and to demonstrate the feasibility of the approach, we characterized the splenic α/β/γ/δ TCR repertoire. Analysis of the repertoires revealed preferential usage of specific V and J combinations in all chains, while the overall features were characteristic of unbiased repertoires. We observed moderate levels of shared complementarity-determining region 3 (CDR3) clonotypes among individual birds within the α and γ chain repertoires, including the most frequently occurring clonotypes. However, the β and δ repertoires were predominantly unique to each bird. Taken together, our TCR repertoire analysis allowed us to decipher the composition, diversity, and functionality of T cells in chickens. This work not only represents a significant step towards understanding avian T cell biology, but will also shed light on host-pathogen interactions, vaccine development, and the evolutionary history of avian immunology.

Enhancing diagnosis of T-cell lymphoma using non-recombined T-cell receptor sequences

Clonality assessment, which can detect neoplastic T cells by identifying the uniquely recombined T-cell receptor (TCR) genes, provides important support in the diagnosis of T-cell lymphoma (TCL). BIOMED-2 is the gold standard clonality assay and has proven to be effective in European TCL patients. However, we failed to prove its sensitivity in Taiwanese TCL patients, especially based on the TCRβ gene. To explore potential impact of genetic background in the BIOMED-2 test, we analyzed TCRβ sequences of 21 healthy individuals and two TCL patients. This analysis suggests that genetic variations in the BIOMED-2 primer sites could not explain the difference in sensitivity. The BIOMED-2 test results of the two TCL patients were positive and negative, respectively. Interestingly, a higher percentage (>81%) of non-recombined TCRβ sequences was observed in the test-negative patient than those of the test-positive patient and all healthy individuals (13~66%). The result suggests a new TCR target for enhancing TCL diagnosis. To further explore the hypothesis, we proposed a cost-effective digital PCR assay that quantifies the relative abundance of non-recombined TCRβ sequences containing a J2-2P~J2-3 segment. With the digital PCR assay, bone marrow specimens from TCL patients (n=9) showed a positive outcome (i.e., the relative abundance of the J2-2P~J2-3 sequences ≧5%), whereas non-TCL patients (n=6) gave a negative result. As five of nine TCL patients had a negative BIOMED-2 test result, the J2-2P~J2-3 sequences may improve TCL detection. This is the first report showing the capability of characterizing non-recombined TCR sequences as a supplementary strategy for the BIOMED-2 clonality test.

Tools for fundamental analysis functions of TCR repertoires: a systematic comparison

The full set of T cell receptors (TCRs) in an individual is known as his or her TCR repertoire. Defining TCR repertoires under physiological conditions and in response to a disease or vaccine may lead to a better understanding of adaptive immunity and thus has great biological and clinical value. In the past decade, several high-throughput sequencing-based tools have been developed to assign TCRs to germline genes and to extract complementarity-determining region 3 (CDR3) sequences using different algorithms. Although these tools claim to be able to perform the full range of fundamental TCR repertoire analyses, there is no clear consensus of which tool is best suited to particular projects. Here, we present a systematic analysis of 12 available TCR repertoire analysis tools using simulated data, with an emphasis on fundamental analysis functions. Our results shed light on the detailed functions of TCR repertoire analysis tools and may therefore help researchers in the field to choose the right tools for their particular experimental design.

The methods and advances of adaptive immune receptors repertoire sequencing

The adaptive immune response is a powerful tool, capable of recognizing, binding to, and neutralizing a vast number of internal and external threats via T or B lymphatic receptors with widespread sets of antigen specificities. The emergence of high-throughput sequencing technology and bioinformatics provides opportunities for research in the fields of life sciences and medicine. The analysis and annotation for immune repertoire data can reveal biologically meaningful information, including immune prediction, target antigens, and effective evaluation. Continuous improvements of the immunological repertoire sequencing methods and analysis tools will help to minimize the experimental and calculation errors and realize the immunological information to meet the clinical requirements. That said, the clinical application of adaptive immune repertoire sequencing requires appropriate experimental methods and standard analytical tools. At the population cell level, we can acquire the overview of cell groups, but the information about a single cell is not obtained accurately. The information that is ignored may be crucial for understanding the heterogeneity of each cell, gene expression and drug response. The combination of high-throughput sequencing and single-cell technology allows us to obtain single-cell information with low-cost and high-throughput. In this review, we summarized the current methods and progress in this area.

Dissecting efficiency of a 5' rapid amplification of cDNA ends (5'-RACE) approach for profiling T-cell receptor beta repertoire

Deep sequencing of T-cell receptor (TCR) genes is powerful at profiling immune repertoire. To prepare a TCR sequencing library, multiplex polymerase chain reaction (mPCR) is widely applied and is highly efficient. That is, most mPCR products contain the region critical for antigen recognition, which also indicates regular V(D)J recombination. Multiplex PCR, however, may suffer from primer bias. A promising alternative is 5'-RACE, which avoids primer bias by applying only one primer pair. In 5'-RACE data, however, non-regular V(D)J recombination (e.g., TCR sequences without a V gene segment) has been observed and the frequency varies (30-80%) between studies. This suggests that the cause of or how to reduce non-regular TCR sequences is not yet well known by the science community. Although it is possible to speculate the cause by comparing the 5'-RACE protocols, careful experimental confirmation is needed and such a systematic study is still not available. Here, we examined the 5'-RACE protocol of a commercial kit and demonstrated how a modification increased the fraction of regular TCR-β sequences to >85%. We also found a strong linear correlation between the fraction of short DNA fragments and the percentage of non-regular TCR-β sequences, indicating that the presence of short DNA fragments in the library was the main cause of non-regular TCR-β sequences. Therefore, thorough removal of short DNA fragments from a 5'-RACE library is the key to high data efficiency. We highly recommend conducting a fragment length analysis before sequencing, and the fraction of short DNA fragments can be used to estimate the percentage of non-regular TCR sequences. As deep sequencing of TCR genes is still relatively expensive, good quality control should be valuable.

Molecular T-Cell Repertoire Analysis as Source of Prognostic and Predictive Biomarkers for Checkpoint Blockade Immunotherapy

The T cells are key players of the response to checkpoint blockade immunotherapy (CBI) and monitoring the strength and specificity of antitumor T-cell reactivity remains a crucial but elusive component of precision immunotherapy. The entire assembly of T-cell receptor (TCR) sequences accounts for antigen specificity and strength of the T-cell immune response. The TCR repertoire hence represents a “footprint” of the conditions faced by T cells that dynamically evolves according to the challenges that arise for the immune system, such as tumor neo-antigenic load. Hence, TCR repertoire analysis is becoming increasingly important to comprehensively understand the nature of a successful antitumor T-cell response, and to improve the success and safety of current CBI.

Systematic comparative study of computational methods for T-cell receptor sequencing data analysis

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.

High-throughput sequencing of the T-cell receptor repertoire: pitfalls and opportunities

T-cell specificity is determined by the T-cell receptor, a heterodimeric protein coded for by an extremely diverse set of genes produced by imprecise somatic gene recombination. Massively parallel high-throughput sequencing allows millions of different T-cell receptor genes to be characterized from a single sample of blood or tissue. However, the extraordinary heterogeneity of the immune repertoire poses significant challenges for subsequent analysis of the data. We outline the major steps in processing of repertoire data, considering low-level processing of raw sequence files and high-level algorithms, which seek to extract biological or pathological information. The latest generation of bioinformatics tools allows millions of DNA sequences to be accurately and rapidly assigned to their respective variable V and J gene segments, and to reconstruct an almost error-free representation of the non-templated additions and deletions that occur. High-level processing can measure the diversity of the repertoire in different samples, quantify V and J usage and identify private and public T-cell receptors. Finally, we discuss the major challenge of linking T-cell receptor sequence to function, and specifically to antigen recognition. Sophisticated machine learning algorithms are being developed that can combine the paradoxical degeneracy and cross-reactivity of individual T-cell receptors with the specificity of the overall T-cell immune response. Computational analysis will provide the key to unlock the potential of the T-cell receptor repertoire to give insight into the fundamental biology of the adaptive immune system and to provide powerful biomarkers of disease.