Reconstructing Antibody Repertoires from Error-Prone Immunosequencing Reads

Extraction of the CDRH3 sequence of the mouse antibody repertoire selected upon influenza virus infection by subtraction of the background antibody repertoire

Historically, antibody reactivity to pathogens and vaccine antigens has been evaluated using serological measurements of antigen-specific antibodies. However, it is difficult to evaluate all antibodies that contribute to various functions in a single assay, such as the measurement of the neutralizing antibody titer. Bulk antibody repertoire analysis using next-generation sequencing is a comprehensive method for analyzing the overall antibody response; however, it is unreliable for estimating antigen-specific antibodies due to individual variation. To address this issue, we propose a method to subtract the background signal from the repertoire of data of interest. In this study, we analyzed changes in antibody diversity and inferred the heavy-chain complementarity-determining region 3 (CDRH3) sequences of antibody clones that were selected upon influenza virus infection in a mouse model using bulk repertoire analysis. A decrease in the diversity of the antibody repertoire was observed upon viral infection, along with an increase in neutralizing antibody titers. Using kernel density estimation of sequences in a high-dimensional sequence space with background signal subtraction, we identified several clusters of CDRH3 sequences induced upon influenza virus infection. Most of these repertoires were detected more frequently in infected mice than in uninfected control mice, suggesting that infection-specific antibody sequences can be extracted using this method. Such an accurate extraction of antigen- or infection-specific repertoire information will be a useful tool for vaccine evaluation in the future.

IMPORTANCE

As specific interactions between antigens and cell-surface antibodies trigger the proliferation of B-cell clones, the frequency of each antibody sequence in the samples reflects the size of each clonal population. Nevertheless, it is extremely difficult to extract antigen-specific antibody sequences from the comprehensive bulk antibody sequences obtained from blood samples due to repertoire bias influenced by exposure to dietary antigens and other infectious agents. This issue can be addressed by subtracting the background noise from the post-immunization or post-infection repertoire data. In the present study, we propose a method to quantify repertoire data from comprehensive repertoire data. This method allowed subtraction of the background repertoire, resulting in more accurate extraction of expanded antibody repertoires upon influenza virus infection. This accurate extraction of antigen- or infection-specific repertoire information is a useful tool for vaccine evaluation.

Abalign: a comprehensive multiple sequence alignment platform for B-cell receptor immune repertoires

The utilization of high-throughput sequencing (HTS) for B-cell receptor (BCR) immune repertoire analysis has become widespread in the fields of adaptive immunity and antibody drug development. However, the sheer volume of sequences generated by these experiments presents a challenge in data processing. Specifically, multiple sequence alignment (MSA), a critical aspect of BCR analysis, remains inadequate for handling massive BCR sequencing data and lacks the ability to provide immunoglobulin-specific information. To address this gap, we introduce Abalign, a standalone program specifically designed for ultrafast MSA of BCR/antibody sequences. Benchmark tests demonstrate that Abalign achieves comparable or even better accuracy than state-of-the-art MSA tools, and shows remarkable advantages in terms of speed and memory consumption, reducing the time required for high-throughput analysis from weeks to hours. In addition to its alignment capabilities, Abalign offers a broad range of BCR analysis features, including extracting BCRs, constructing lineage trees, assigning VJ genes, analyzing clonotypes, profiling mutations, and comparing BCR immune repertoires. With its user-friendly graphic interface, Abalign can be easily run on personal computers instead of computing clusters. Overall, Abalign is an easy-to-use and effective tool that enables researchers to analyze massive BCR/antibody sequences, leading to new discoveries in the field of immunoinformatics. The software is freely available at http://cao.labshare.cn/abalign/.

A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods

Affinity maturation (AM) of B cells through somatic hypermutations (SHMs) enables the immune system to evolve to recognize diverse pathogens. The accumulation of SHMs leads to the formation of clonal lineages of antibody-secreting b cells that have evolved from a common naïve B cell. Advances in high-throughput sequencing have enabled deep scans of B cell receptor repertoires, paving the way for reconstructing clonal trees. However, it is not clear if clonal trees, which capture microevolutionary time scales, can be reconstructed using traditional phylogenetic reconstruction methods with adequate accuracy. In fact, several clonal tree reconstruction methods have been developed to fix supposed shortcomings of phylogenetic methods. Nevertheless, no consensus has been reached regarding the relative accuracy of these methods, partially because evaluation is challenging. Benchmarking the performance of existing methods and developing better methods would both benefit from realistic models of clonal lineage evolution specifically designed for emulating B cell evolution. In this paper, we propose a model for modeling B cell clonal lineage evolution and use this model to benchmark several existing clonal tree reconstruction methods. Our model, designed to be extensible, has several features: by evolving the clonal tree and sequences simultaneously, it allows modeling selective pressure due to changes in affinity binding; it enables scalable simulations of large numbers of cells; it enables several rounds of infection by an evolving pathogen; and, it models building of memory. In addition, we also suggest a set of metrics for comparing clonal trees and measuring their properties. Our results show that while maximum likelihood phylogenetic reconstruction methods can fail to capture key features of clonal tree expansion if applied naively, a simple post-processing of their results, where short branches are contracted, leads to inferences that are better than alternative methods.

Broadly neutralizing antibodies to SARS-related viruses can be readily induced in rhesus macaques

To prepare for future coronavirus (CoV) pandemics, it is desirable to generate vaccines capable of eliciting broadly neutralizing antibody responses to CoVs. Here, we show that immunization of macaques with SARS-CoV-2 spike (S) protein with a two-shot protocol generated potent serum receptor binding domain cross-neutralizing antibody responses to both SARS-CoV-2 and SARS-CoV-1. Furthermore, responses were equally effective against most SARS-CoV-2 variants of concern (VOCs) and some were highly effective against Omicron. This result contrasts with human infection or many two-shot vaccination protocols where responses were typically more SARS-CoV-2 specific and where VOCs were less well neutralized. Structural studies showed that cloned macaque neutralizing antibodies, particularly using a given heavy chain germline gene, recognized a relatively conserved region proximal to the angiotensin converting enzyme 2 receptor binding site (RBS), whereas many frequently elicited human neutralizing antibodies targeted more variable epitopes overlapping the RBS. B cell repertoire differences between humans and macaques appeared to influence the vaccine response. The macaque neutralizing antibodies identified a pan-SARS-related virus epitope region less well targeted by human antibodies that could be exploited in rational vaccine design.

Antibody repertoire sequencing analysis

High-throughput sequencing for B cell receptor (BCR) repertoire provides useful insights for the adaptive immune system. With the continuous development of the BCR-seq technology, many efforts have been made to develop methods for analyzing the ever-increasing BCR repertoire data. In this review, we comprehensively outline different BCR repertoire library preparation protocols and summarize three major steps of BCR-seq data analysis, i. e., V(D)J sequence annotation, clonal phylogenetic inference, and BCR repertoire profiling and mining. Different from other reviews in this field, we emphasize background intuition and the statistical principle of each method to help biologists better understand it. Finally, we discuss data mining problems for BCR-seq data and with a highlight on recently emerging multiple-sample analysis.

Targeted isolation of diverse human protective broadly neutralizing antibodies against SARS-like viruses

The emergence of current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) and potential future spillovers of SARS-like coronaviruses into humans pose a major threat to human health and the global economy. Development of broadly effective coronavirus vaccines that can mitigate these threats is needed. Here, we utilized a targeted donor selection strategy to isolate a large panel of human broadly neutralizing antibodies (bnAbs) to sarbecoviruses. Many of these bnAbs are remarkably effective in neutralizing a diversity of sarbecoviruses and against most SARS-CoV-2 VOCs, including the Omicron variant. Neutralization breadth is achieved by bnAb binding to epitopes on a relatively conserved face of the receptor-binding domain (RBD). Consistent with targeting of conserved sites, select RBD bnAbs exhibited protective efficacy against diverse SARS-like coronaviruses in a prophylaxis challenge model in vivo. These bnAbs provide new opportunities and choices for next-generation antibody prophylactic and therapeutic applications and provide a molecular basis for effective design of pan-sarbecovirus vaccines.

Variations in antibody repertoires correlate with vaccine responses

An important challenge in vaccine development is to figure out why a vaccine succeeds in some individuals and fails in others. Although antibody repertoires hold the key to answering this question, there have been very few personalized immunogenomics studies so far aimed at revealing how variations in immunoglobulin genes affect a vaccine response. We conducted an immunosequencing study of 204 calves vaccinated against bovine respiratory disease (BRD) with the goal to reveal variations in immunoglobulin genes and somatic hypermutations that impact the efficacy of vaccine response. Our study represents the largest longitudinal personalized immunogenomics study reported to date across all species, including humans. To analyze the generated data set, we developed an algorithm for identifying variations of the immunoglobulin genes (as well as frequent somatic hypermutations) that affect various features of the antibody repertoire and titers of neutralizing antibodies. In contrast to relatively short human antibodies, cattle have a large fraction of ultralong antibodies that have opened new therapeutic opportunities. Our study reveals that ultralong antibodies are a key component of the immune response against the costliest disease of beef cattle in North America. The detected variants of the cattle immunoglobulin genes, which are implicated in the success/failure of the BRD vaccine, have the potential to direct the selection of individual cattle for ongoing breeding programs.

Broadly neutralizing anti-S2 antibodies protect against all three human betacoronaviruses that cause severe disease

Pan-betacoronavirus neutralizing antibodies may hold the key to developing broadly protective vaccines against coronaviruses that cause severe disease, for anticipating novel pandemic-causing viruses, and to respond more effectively to SARS-CoV-2 variants. The emergence of the Omicron variant of SARS-CoV-2 has illustrated the limitations of solely targeting the receptor binding domain (RBD) of the envelope Spike (S)-protein. Here, we isolated a large panel of broadly neutralizing antibodies (bnAbs) from SARS-CoV-2 recovered-vaccinated donors that target a conserved S2 region in the fusion machinery on betacoronavirus spikes. Select bnAbs show broad in vivo protection against all three pathogenic betacoronaviruses, SARS-CoV-1, SARS-CoV-2 and MERS-CoV, that have spilled over into humans in the past 20 years to cause severe disease. The bnAbs provide new opportunities for antibody-based interventions and key insights for developing pan-betacoronavirus vaccines.

Targeted isolation of panels of diverse human protective broadly neutralizing antibodies against SARS-like viruses

The emergence of current SARS-CoV-2 variants of concern (VOCs) and potential future spillovers of SARS-like coronaviruses into humans pose a major threat to human health and the global economy 1-7 . Development of broadly effective coronavirus vaccines that can mitigate these threats is needed 8, 9 . Notably, several recent studies have revealed that vaccination of recovered COVID-19 donors results in enhanced nAb responses compared to SARS-CoV-2 infection or vaccination alone 10-13 . Here, we utilized a targeted donor selection strategy to isolate a large panel of broadly neutralizing antibodies (bnAbs) to sarbecoviruses from two such donors. Many of the bnAbs are remarkably effective in neutralization against sarbecoviruses that use ACE2 for viral entry and a substantial fraction also show notable binding to non-ACE2-using sarbecoviruses. The bnAbs are equally effective against most SARS-CoV-2 VOCs and many neutralize the Omicron variant. Neutralization breadth is achieved by bnAb binding to epitopes on a relatively conserved face of the receptor binding domain (RBD) as opposed to strain-specific nAbs to the receptor binding site that are commonly elicited in SARS-CoV-2 infection and vaccination 14-18 . Consistent with targeting of conserved sites, select RBD bnAbs exhibited in vivo protective efficacy against diverse SARS-like coronaviruses in a prophylaxis challenge model. The generation of a large panel of potent bnAbs provides new opportunities and choices for next-generation antibody prophylactic and therapeutic applications and, importantly, provides a molecular basis for effective design of pan-sarbecovirus vaccines.

Next Generation Sequencing of Cerebrospinal Fluid B Cell Repertoires in Multiple Sclerosis and Other Neuro-Inflammatory Diseases-A Comprehensive Review

During the last few decades, the role of B cells has been well established and redefined in neuro-inflammatory diseases, including multiple sclerosis and autoantibody-associated diseases. In particular, B cell maturation and trafficking across the blood–brain barrier (BBB) has recently been deciphered with the development of next-generation sequencing (NGS) approaches, which allow the assessment of representative cerebrospinal fluid (CSF) and peripheral blood B cell repertoires. In this review, we perform literature research focusing on NGS studies that allow further insights into B cell pathophysiology during neuro-inflammation. Besides the analysis of CSF B cells, the paralleled assessment of peripheral blood B cell repertoire provides deep insights into not only the CSF compartment, but also in B cell trafficking patterns across the BBB. In multiple sclerosis, CSF-specific B cell maturation, in combination with a bidirectional exchange of B cells across the BBB, is consistently detectable. These data suggest that B cells most likely encounter antigen(s) within the CSF and migrate across the BBB, with further maturation also taking place in the periphery. Autoantibody-mediated diseases, such as neuromyelitis optica spectrum disorder and LGI1 / NMDAR encephalitis, also show features of a CSF-specific B cell maturation and clonal connectivity with peripheral blood. In conclusion, these data suggest an intense exchange of B cells across the BBB, possibly feeding autoimmune circuits. Further developments in sequencing technologies will help to dissect the exact pathophysiologic mechanisms of B cells during neuro-inflammation.

Vaccine genetics of IGHV1-2 VRC01-class broadly neutralizing antibody precursor naïve human B cells

A successful HIV vaccine eliciting broadly neutralizing antibodies (bnAbs) must overcome the hurdle of being able to activate naive precursor B cells encoding features within their germline B cell receptors (BCR) that allow recognition of broadly neutralizing epitopes. Knowledge of whether bnAb precursor B cells are circulating at sufficient frequencies within individuals in communities heavily impacted by HIV may be important. Using a germline-targeting eOD-GT8 immunogen and high-throughput droplet-based single-cell BCR sequencing, we demonstrate that large numbers of paired BCR sequences from multiple donors can be efficiently screened to elucidate precursor frequencies of rare, naive VRC01-class B cells. Further, we analyzed IGHV1-2 allelic usage among three different cohorts; we find that IGHV1-2 alleles traditionally thought to be incompatible with VRC01-class responses are relatively common in various human populations and that germline variation within IGHV1-2 associates with gene usage frequencies in the naive BCR repertoire.

B cell engagement with HIV-1 founder virus envelope predicts development of broadly neutralizing antibodies

Determining which immunological mechanisms contribute to the development of broad neutralizing antibodies (bNAbs) during HIV-1 infection is a major goal to inform vaccine design. Using samples from a longitudinal HIV-1 acute infection cohort, we found key B cell determinants within the first 14-43 days of viremia that predict the development of bNAbs years later. Individuals who develop neutralization breadth had significantly higher B cell engagement with the autologous founder HIV envelope (Env) within 1 month of initial viremia. A higher frequency of founder-Env-specific naive B cells was associated with increased B cell activation and differentiation and predictive of bNAb development. These data demonstrate that the initial B cell interaction with the founder HIV Env is important for the development of broadly neutralizing antibodies and provide evidence that events within HIV acute infection lead to downstream functional outcomes.

V(DD)J recombination is an important and evolutionarily conserved mechanism for generating antibodies with unusually long CDR3s

The V(DD)J recombination is currently viewed as an aberrant and inconsequential variant of the canonical V(D)J recombination. Moreover, since the classical 12/23 rule for the V(D)J recombination fails to explain the V(DD)J recombination, the molecular mechanism of tandem D-D fusions has remained unknown since they were discovered three decades ago. Revealing this mechanism is a biomedically important goal since tandem fusions contribute to broadly neutralizing antibodies with ultralong CDR3s. We reveal previously overlooked cryptic nonamers in the recombination signal sequences of human IGHD genes and demonstrate that these nonamers explain the vast majority of tandem fusions in human repertoires. We further reveal large clonal lineages formed by tandem fusions in antigen-stimulated immunosequencing data sets, suggesting that such data sets contain many more tandem fusions than previously thought and that about a quarter of large clonal lineages with unusually long CDR3s are generated through tandem fusions. Finally, we developed the SEARCH-D algorithm for identifying D genes in mammalian genomes and applied it to the recently completed Vertebrate Genomes Project assemblies, nearly doubling the number of mammalian species with known D genes. Our analysis revealed cryptic nonamers in RSSs of many mammalian genomes, thus demonstrating that the V(DD)J recombination is not a "bug" but an important feature preserved throughout mammalian evolution.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Automated analysis of immunosequencing datasets reveals novel immunoglobulin D genes across diverse species

Immunoglobulin genes are formed through V(D)J recombination, which joins the variable (V), diversity (D), and joining (J) germline genes. Since variations in germline genes have been linked to various diseases, personalized immunogenomics focuses on finding alleles of germline genes across various patients. Although reconstruction of V and J genes is a well-studied problem, the more challenging task of reconstructing D genes remained open until the IgScout algorithm was developed in 2019. In this work, we address limitations of IgScout by developing a probabilistic MINING-D algorithm for D gene reconstruction, apply it to hundreds of immunosequencing datasets from multiple species, and validate the newly inferred D genes by analyzing diverse whole genome sequencing datasets and haplotyping heterozygous V genes.

Antibodies provide specific binding to an enormous range of antigens and represent a key component of the adaptive immune system. Immunosequencing has emerged as a method of choice for generating millions of reads that sample antibody repertoires and provides insights into monitoring immune response to disease and vaccination. Most of the previous immunogenomics studies rely on the reference germline genes in the immunoglobulin locus rather than the germline genes in a specific patient. This approach is deficient since the set of known germline genes is incomplete (particularly for non-European humans and non-human species) and contains alleles that resulted from sequencing and annotation errors. The problem of de novo inference of diversity (D) genes from immunosequencing data remained open until the IgScout algorithm was developed in 2019. We address limitations of IgScout by developing a probabilistic MINING-D algorithm for D gene reconstruction and infer multiple D genes across multiple species that are not present in standard databases.

De novo Inference of Diversity Genes and Analysis of Non-canonical V(DD)J Recombination in Immunoglobulins

The V(D)J recombination forms the immunoglobulin genes by joining the variable (V), diversity (D), and joining (J) germline genes. Since variations in germline genes have been linked to various diseases, personalized immunogenomics aims at finding alleles of germline genes across various patients. Although recent studies described algorithms for de novo inference of V and J genes from immunosequencing data, they stopped short of solving a more difficult problem of reconstructing D genes that form the highly divergent CDR3 regions and provide the most important contribution to the antigen binding. We present the IgScout algorithm for de novo D gene reconstruction and apply it to reveal new alleles of human D genes and previously unknown D genes in camel, an important model organism in immunology. We further analyze non-canonical V(DD)J recombination that results in unusually long CDR3s with tandem fused IGHD genes and thus expands the diversity of the antibody repertoires. We demonstrate that tandem CDR3s represent a consistent and functional feature of all analyzed immunosequencing datasets, reveal ultra-long CDR3s, and shed light on the mechanism responsible for their formation.

The Pipeline Repertoire for Ig-Seq Analysis

With the advent of high-throughput sequencing of immunoglobulin genes (Ig-Seq), the understanding of antibody repertoires and their dynamics among individuals and populations has become an exciting area of research. There is an increasing number of computational tools that aid in every step of the immune repertoire characterization. However, since not all tools function identically, every pipeline has its unique rationale and capabilities, creating a rich blend of useful features that may appear intimidating for newcomer laboratories with the desire to plunge into immune repertoire analysis to expand and improve their research; hence, all pipeline strengths and differences may not seem evident. In this review we provide a practical and organized list of the current set of computational tools, focusing on their most attractive features and differences in order to carry out the characterization of antibody repertoires so that the reader better decides a strategic approach for the experimental design, and computational pathways for the analyses of immune repertoires.

A comparison of unamplified and massively multiplexed PCR amplification for murine antibody repertoire sequencing

Sequencing antibody repertoires has steadily become cheaper and easier. Sequencing methods usually rely on some form of amplification, often a massively multiplexed PCR prior to sequencing. To eliminate potential biases and create a data set that could be used for other studies, our lab compared unamplified sequencing results from the splenic heavy-chain repertoire in the mouse to those processed through two commercial applications. We also compared the use of mRNA vs total RNA, reverse transcriptase, and primer usage for cDNA synthesis and submission. The use of mRNA for cDNA synthesis resulted in higher read counts but reverse transcriptase and primer usage had no statistical effects on read count. Although most of the amplified data sets contained more antibody reads than the unamplified data set, we detected more unique V-gene segments in the unamplified data set. Although unique CDR3 detection was much lower in the unamplified data set, RNASeq detected 98% of the high frequency CDR3s. We have shown that unamplified profiling of the antibody repertoire is possible, detects more V-gene segments, and detects high frequency clones in the repertoire.

A comparison of unamplified and massively multiplexed PCR amplification for murine antibody repertoire sequencing

Sequencing antibody repertoires has steadily become cheaper and easier. Sequencing methods usually rely on some form of amplification, often a massively multiplexed PCR prior to sequencing. To eliminate potential biases and create a data set that could be used for other studies, our laboratory compared unamplified sequencing results from the splenic heavy-chain repertoire in the mouse to those processed through two commercial applications. We also compared the use of mRNA vs total RNA, reverse transcriptase, and primer usage for cDNA synthesis and submission. The use of mRNA for cDNA synthesis resulted in higher read counts but reverse transcriptase and primer usage had no statistical effects on read count. Although most of the amplified data sets contained more antibody reads than the unamplified data set, we detected more unique variable (V)-gene segments in the unamplified data set. Although unique CDR3 detection was much lower in the unamplified data set, RNASeq detected 98% of the high-frequency CDR3s. We have shown that unamplified profiling of the antibody repertoire is possible, detects more V-gene segments, and detects high-frequency clones in the repertoire.

Filtering Next-Generation Sequencing of the Ig Gene Repertoire Data Using Antibody Structural Information

Next-generation sequencing of the Ig gene repertoire (Ig-seq) produces large volumes of information at the nucleotide sequence level. Such data have improved our understanding of immune systems across numerous species and have already been successfully applied in vaccine development and drug discovery. However, the high-throughput nature of Ig-seq means that it is afflicted by high error rates. This has led to the development of error-correction approaches. Computational error-correction methods use sequence information alone, primarily designating sequences as likely to be correct if they are observed frequently. In this work, we describe an orthogonal method for filtering Ig-seq data, which considers the structural viability of each sequence. A typical natural Ab structure requires the presence of a disulfide bridge within each of its variable chains to maintain the fold. Our Ab Sequence Selector (ABOSS) uses the presence/absence of this bridge as a way of both identifying structurally viable sequences and estimating the sequencing error rate. On simulated Ig-seq datasets, ABOSS is able to identify more than 99% of structurally viable sequences. Applying our method to six independent Ig-seq datasets (one mouse and five human), we show that our error calculations are in line with previous experimental and computational error estimates. We also show how ABOSS is able to identify structurally impossible sequences missed by other error-correction methods.

Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants

Background

Many biological analysis tasks require extraction of families of genetically similar sequences from large datasets produced by Next-generation Sequencing (NGS). Such tasks include detection of viral transmissions by analysis of all genetically close pairs of sequences from viral datasets sampled from infected individuals or studying of evolution of viruses or immune repertoires by analysis of network of intra-host viral variants or antibody clonotypes formed by genetically close sequences. The most obvious naïeve algorithms to extract such sequence families are impractical in light of the massive size of modern NGS datasets.

Results

In this paper, we present fast and scalable k-mer-based framework to perform such sequence similarity queries efficiently, which specifically targets data produced by deep sequencing of heterogeneous populations such as viruses. It shows better filtering quality and time performance when comparing to other tools. The tool is freely available for download at https://github.com/vyacheslav-tsivina/signature-sj

Conclusion

The proposed tool allows for efficient detection of genetic relatedness between genomic samples produced by deep sequencing of heterogeneous populations. It should be especially useful for analysis of relatedness of genomes of viruses with unevenly distributed variable genomic regions, such as HIV and HCV. For the future we envision, that besides applications in molecular epidemiology the tool can also be adapted to immunosequencing and metagenomics data.

Immunoglobulin gene analysis as a tool for investigating human immune responses

The human immunoglobulin repertoire is a hugely diverse set of sequences that are formed by processes of gene rearrangement, heavy and light chain gene assortment, class switching and somatic hypermutation. Early B cell development produces diverse IgM and IgD B cell receptors on the B cell surface, resulting in a repertoire that can bind many foreign antigens but which has had self-reactive B cells removed. Later antigen-dependent development processes adjust the antigen affinity of the receptor by somatic hypermutation. The effector mechanism of the antibody is also adjusted, by switching the class of the antibody from IgM to one of seven other classes depending on the required function. There are many instances in human biology where positive and negative selection forces can act to shape the immunoglobulin repertoire and therefore repertoire analysis can provide useful information on infection control, vaccination efficacy, autoimmune diseases, and cancer. It can also be used to identify antigen-specific sequences that may be of use in therapeutics. The juxtaposition of lymphocyte development and numerical evaluation of immune repertoires has resulted in the growth of a new sub-speciality in immunology where immunologists and computer scientists/physicists collaborate to assess immune repertoires and develop models of immune action.

Analyzing Immunoglobulin Repertoires

Somatic assembly of T cell receptor 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.

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.