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The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.

Abstract The purpose of this study is to evaluate the feasibility of using RNA sequencing data as substrate for the computational extraction of T cell receptor sequences. Data from hundreds of thousands of samples is available as RNA sequencing. However, the use of these data for repertoires has not been contrasted against a gold standard. We conducted a benchmarking analysis, comparing T cell receptor data extracted from RNA sequencing to those obtained from T cell receptor sequencing (as gold standard) of the same tissue samples. The focus was on the extraction of Complementarity-Determining Region 3 (CDR3) sequences. To evaluate the influence of sequencing read lengths, samples were analyzed using both 75 base pair single-end and 150 base pair paired-end sequencing methods. In addition we calculated T cell abundance in these samples to test for any correlation between reads and abundance. The findings reveal a significant, perhaps too great, discrepancy between the ability to extract Complementarity-Determining Region 3 sequences from RNA sequencing data and the results obtained from TCR sequencing. The lack of significant improvement with longer read lengths, combined with the absence of correlation to T cell abundance, emphasize the necessity of using T cell receptor sequencing methodologies.

Background Although B-cell depleting therapy in rheumatoid arthritis (RA) is clearly effective, response is variable and does not correlate with B cell depletion itself. Methods The B-cell receptor (BCR) repertoire was prospectively analyzed in peripheral blood samples of twenty-eight RA patients undergoing rituximab therapy. Timepoints of achieved BCR-depletion and -repopulation were defined based on the percentage of unmutated BCRs in the repertoire. The predictive value of early BCR-depletion (within one-month post-treatment) and early BCR-repopulation (within 6 months post-treatment) on clinical response was assessed. Results We observed changes in the peripheral blood BCR repertoire after rituximab treatment, i.e., increased clonal expansion, decreased clonal diversification and increased mutation load which persisted up to 12 months after treatment, but started to revert at month 6. Early BCR depletion was not associated with early clinical response but late depleters did show early response. Patients with early repopulation with unmutated BCRs showed a significant decrease in disease activity in the interval 6 to 12 months. Development of anti-drug antibodies non-significantly correlated with more BCR repopulation. Conclusion Our findings indicate that rather than BCR-depletion it is repopulation with unmutated BCRs, possibly from naïve B cells, which induces remission. This suggests that (pre-existing) differences in B-cell turnover between patients explain the interindividual differences in early clinical effect.

Increased interest in the immune system's involvement in pathophysiological phenomena coupled with decreased DNA sequencing costs have led to an explosion of antibody and T cell receptor sequencing data collectively termed \"adaptive immune receptor repertoire sequencing\" (AIRR-seq or Rep-Seq). The AIRR Community has been actively working to standardize protocols, metadata, formats, APIs, and other guidelines to promote open and reproducible studies of the immune repertoire. In this paper, we describe the work of the AIRR Community's Data Representation Working Group to develop standardized data representations for storing and sharing annotated antibody and T cell receptor data. Our file format emphasizes ease-of-use, accessibility, scalability to large data sets, and a commitment to open and transparent science. It is composed of a tab-delimited format with a specific schema. Several popular repertoire analysis tools and data repositories already utilize this AIRR-seq data format. We hope that others will follow suit in the interest of promoting interoperable standards.

The adaptive immune receptor repertoire (AIRR) contains information on an individuals' immune past, present and potential in the form of the evolving sequences that encode the B cell receptor (BCR) repertoire. AIRR sequencing (AIRR-seq) studies rely on databases of known BCR germline variable (V), diversity (D), and joining (J) genes to detect somatic mutations in AIRR-seq data via comparison to the best-aligning database alleles. However, it has been shown that these databases are far from complete, leading to systematic misidentification of mutated positions in subsets of sample sequences. We previously presented TIgGER, a computational method to identify subject-specific V gene genotypes, including the presence of novel V gene alleles, directly from AIRR-seq data. However, the original algorithm was unable to detect alleles that differed by more than 5 single nucleotide polymorphisms (SNPs) from a database allele. Here we present and apply an improved version of the TIgGER algorithm which can detect alleles that differ by any number of SNPs from the nearest database allele, and can construct subject-specific genotypes with minimal prior information. TIgGER predictions are validated both computationally (using a leave-one-out strategy) and experimentally (using genomic sequencing), resulting in the addition of three new immunoglobulin heavy chain V (IGHV) gene alleles to the IMGT repertoire. Finally, we develop a Bayesian strategy to provide a confidence estimate associated with genotype calls. All together, these methods allow for much higher accuracy in germline allele assignment, an essential step in AIRR-seq studies.

The success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance. We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls. In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients. These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.

Immunoglobulins or antibodies are the main effector molecules of the B-cell lineage and are encoded by hundreds of variable (V), diversity (D), and joining (J) germline genes, which recombine to generate enormous IG diversity. Recently, high-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) of recombined V-(D)-J genes has offered unprecedented insights into the dynamics of IG repertoires in health and disease. Faithful biological interpretation of AIRR-seq studies depends upon the annotation of raw AIRR-seq data, using reference germline gene databases to identify the germline genes within each rearrangement. Existing reference databases are incomplete, as shown by recent AIRR-seq studies that have inferred the existence of many previously unreported polymorphisms. Completing the documentation of genetic variation in germline gene databases is therefore of crucial importance. Lymphocyte receptor genes and alleles are currently assigned by the Immunoglobulins, T cell Receptors and Major Histocompatibility Nomenclature Subcommittee of the International Union of Immunological Societies (IUIS) and managed in IMGT , the international ImMunoGeneTics information system (IMGT). In 2017, the IMGT Group reached agreement with a group of AIRR-seq researchers on the principles of a streamlined process for identifying and naming inferred allelic sequences, for their incorporation into IMGT . These researchers represented the AIRR Community, a network of over 300 researchers whose objective is to promote all aspects of immunoglobulin and T-cell receptor repertoire studies, including the standardization of experimental and computational aspects of AIRR-seq data generation and analysis. The Inferred Allele Review Committee (IARC) was established by the AIRR Community to devise policies, criteria, and procedures to perform this function. Formalized evaluations of novel inferred sequences have now begun and submissions are invited via a new dedicated portal (https://ogrdb.airr-community.org). Here, we summarize recommendations developed by the IARC-focusing, to begin with, on human IGHV genes-with the goal of facilitating the acceptance of inferred allelic variants of germline IGHV genes. We believe that this initiative will improve the quality of AIRR-seq studies by facilitating the description of human IG germline gene variation, and that in time, it will expand to the documentation of TR and IG genes in many vertebrate species.

Background T and B cell receptor (TCR, BCR) repertoires constitute the foundation of adaptive immunity. Adaptive immune receptor repertoire sequencing (AIRR-seq) is a common approach to study immune system dynamics. Understanding the genetic factors influencing the composition and dynamics of these repertoires is of major scientific and clinical importance. The chromosomal loci encoding for the variable regions of TCRs and BCRs are challenging to decipher due to repetitive elements and undocumented structural variants. Methods To confront this challenge, AIRR-seq-based methods have recently been developed for B cells, enabling genotype and haplotype inference and discovery of undocumented alleles. However, this approach relies on complete coverage of the receptors’ variable regions, whereas most T cell studies sequence a small fraction of that region. Here, we adapted a B cell pipeline for undocumented alleles, genotype, and haplotype inference for full and partial AIRR-seq TCR data sets. The pipeline also deals with gene assignment ambiguities, which is especially important in the analysis of data sets of partial sequences. Results From the full and partial AIRR-seq TCR data sets, we identified 39 undocumented polymorphisms in T cell receptor Beta V (TRBV) and 31 undocumented 5 ′ UTR sequences. A subset of these inferences was also observed using independent genomic approaches. We found that a single nucleotide polymorphism differentiating between the two documented T cell receptor Beta D2 (TRBD2) alleles is strongly associated with dramatic changes in the expressed repertoire. Conclusions We reveal a rich picture of germline variability and demonstrate how a single nucleotide polymorphism dramatically affects the composition of the whole repertoire. Our findings provide a basis for annotation of TCR repertoires for future basic and clinical studies.

To characterize the T cell receptor (TCRβ) repertoire in peripheral blood and muscle tissues of treatment naïve patients with newly diagnosed idiopathic inflammatory myopathies (IIMs). High throughput RNA sequencing of the TCRβ chain was performed in peripheral blood and muscle tissue in twenty newly-diagnosed treatment-naïve IIM patients (9 DM, 5 NM/OM, 5 IMNM and 1 ASyS) and healthy controls. Results thereof were correlated with markers of disease activity. Muscle tissue of IIM patients shows more expansion of TCRβ clones and decreased diversity when compared to peripheral blood of IIM as well as healthy controls (both p=0.0001). Several expanded TCRβ clones in muscle are tissue restricted and cannot be retrieved in peripheral blood. These clones have significantly longer CDR3 regions when compared to clones (also) found in circulation (p=0.0002), while their CDR3 region is more hydrophobic (p<0.01). Network analysis shows that clonal TCRβ signatures are shared between patients. Increased clonal expansion in muscle tissue is significantly correlated with increased CK levels (p=0.03), while it tends to correlate with decreased muscle strength (p=0.08). Network analysis of clones in muscle of IIM patients shows shared clusters of sequences across patients. Muscle-restricted CDR3 TCRβ clones show specific structural features in their T cell receptor. Our results that clonal TCRβ expansion in muscle tissue might be associated with disease activity. Collectively, these findings support a role for specific clonal T cell responses in muscle tissue in the pathogenesis of the IIM subtypes studied.

The adaptive immune system generates a diverse array of B-cell receptors through the processes of V(D)J recombination and somatic hypermutation. B-cell receptors that bind to an antigen will undergo clonal expansion, creating a Darwinian evolutionary dynamic within individuals. A key step in studying these dynamics is to identify sequences derived from the same ancestral V(D)J recombination event (i.e. a clonal family). There are a number of widely used methods for accomplishing this task but a major limitation of all of them is that they rely, at least in part, on the ability to map sequences to a germline reference set. This requirement is particularly problematic in non-model systems where we often know little about the germline allelic diversity in the study population. Recognizing that delimiting B-cell clonal families is analogous to delimiting species from single locus data, we propose a novel strategy of reconstructing the phylogenetic tree of all B-cell sequences in a sample and using a popular species delimitation method, multi-rate Poisson Tree Processes (mPTP), to delimit clonal families. Using extensive simulations, we show that not only does this phylogenetically explicit approach perform well for the purpose of delimiting clonal families when no reference allele set is available, it performs similarly to state-of-the-art techniques developed specifically for B-cell data even when we have a complete reference allele set. Additionally, our analysis of an empirical dataset shows that mPTP performs similarly to leading methods in the field. These findings demonstrate the utility of using off-the-shelf phylogenetic techniques for analyzing B-cell clonal dynamics in non-model systems, and suggests that phylogenetic inference techniques may be potentially combined with mapping based approaches for even more robust inferences, even in model systems.