Neutralizing antibodies against West Nile virus identified directly from human B cells by single-cell analysis and next generation sequencing

The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design

Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.

Early cellular and molecular signatures correlate with severity of West Nile virus infection

Infection with West Nile virus (WNV) drives a wide range of responses, from asymptomatic to flu-like symptoms/fever or severe cases of encephalitis and death. To identify cellular and molecular signatures distinguishing WNV severity, we employed systems profiling of peripheral blood from asymptomatic and severely ill individuals infected with WNV. We interrogated immune responses longitudinally from acute infection through convalescence employing single-cell protein and transcriptional profiling complemented with matched serum proteomics and metabolomics as well as multi-omics analysis. At the acute time point, we detected both elevation of pro-inflammatory markers in innate immune cell types and reduction of regulatory T cell activity in participants with severe infection, whereas asymptomatic donors had higher expression of genes associated with anti-inflammatory CD16+ monocytes. Therefore, we demonstrated the potential of systems immunology using multiple cell-type and cell-state-specific analyses to identify correlates of infection severity and host cellular activity contributing to an effective anti-viral response.

Development of Therapeutic Monoclonal Antibodies for Emerging Arbovirus Infections

Antibody-based passive immunotherapy has been used effectively in the treatment and prophylaxis of infectious diseases. Outbreaks of emerging viral infections from arthropod-borne viruses (arboviruses) represent a global public health problem due to their rapid spread, urging measures and the treatment of infected individuals to combat them. Preparedness in advances in developing antivirals and relevant epidemiological studies protect us from damage and losses. Immunotherapy based on monoclonal antibodies (mAbs) has been shown to be very specific in combating infectious diseases and various other illnesses. Recent advances in mAb discovery techniques have allowed the development and approval of a wide number of therapeutic mAbs. This review focuses on the technological approaches available to select neutralizing mAbs for emerging arbovirus infections and the next-generation strategies to obtain highly effective and potent mAbs. The characteristics of mAbs developed as prophylactic and therapeutic antiviral agents for dengue, Zika, chikungunya, West Nile and tick-borne encephalitis virus are presented, as well as the protective effect demonstrated in animal model studies.

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope

The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype

New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner.

Engineering the Single Domain Antibodies Targeting Receptor Binding Motifs Within the Domain III of West Nile Virus Envelope Glycoprotein

West Nile virus (WNV) is a mosquito-borne neurotrophic flavivirus causing mild febrile illness to severe encephalitis and acute flaccid paralysis with long-term or permanent neurological disorders. Due to the absence of targeted therapy or vaccines, there is a growing need to develop effective anti-WNV therapy. In this study, single-domain antibodies (sdAbs) were developed against the domain III (DIII) of WNV’s envelope glycoprotein to interrupt the interaction between DIII and the human brain microvascular endothelial cells (hBMEC). The peripheral blood mononuclear cells of the llama immunized with recombinant DIIIL297–S403 (rDIII) were used to generate a variable heavy chain only (VHH)-Escherichia coli library, and phage display was performed using the M13K07ΔpIII Hyperphages system. Phages displaying sdAbs against rDIII were panned with the synthetic analogs of the DIII receptor binding motifs, DIII-1G299–K307 and DIII-2V371–R388, and the VHH gene from the eluted phages was subcloned into E. coli SHuffle. Soluble sdAbs purified from 96 E. coli SHuffle clones were screened to identify 20 candidates strongly binding to the synthetic analogs of DIII-1G299–K307 and DIII-2V371–R388 on a dot blot assay. Among them, sdAbA1, sdAbA6, sdAbA9, and sdAbA10 blocked the interaction between rDIII and human brain microvascular endothelial cells (hBMECs) on Western blot and cell ELISA. However, optimum stability during the overexpression was noticed only for sdAbA10 and it also neutralized the WNV–like particles (WNV-VLP) in the Luciferase assay with an half maximal effective concentration (EC50) of 1.48 nm. Furthermore, the hemocompatibility and cytotoxicity of sdAbA10 were assessed by a hemolytic assay and XTT-based hBMEC proliferation assay resulting in 0.1% of hemolytic activity and 82% hBMEC viability, respectively. Therefore, the sdAbA10 targeting DIII-2V371–R388 of the WNV envelope glycoprotein is observed to be suitable for in vivo trials as a specific therapy for WNV–induced neuropathogenesis.

Human B cell lineages associated with germinal centers following influenza vaccination are measurably evolving

The poor efficacy of seasonal influenza virus vaccines is often attributed to pre-existing immunity interfering with the persistence and maturation of vaccine-induced B cell responses. We previously showed that a subset of vaccine-induced B cell lineages are recruited into germinal centers (GCs) following vaccination, suggesting that affinity maturation of these lineages against vaccine antigens can occur. However, it remains to be determined whether seasonal influenza vaccination stimulates additional evolution of vaccine-specific lineages, and previous work has found no significant increase in somatic hypermutation among influenza-binding lineages sampled from the blood following seasonal vaccination in humans. Here, we investigate this issue using a phylogenetic test of measurable immunoglobulin sequence evolution. We first validate this test through simulations and survey measurable evolution across multiple conditions. We find significant heterogeneity in measurable B cell evolution across conditions, with enrichment in primary response conditions such as HIV infection and early childhood development. We then show that measurable evolution following influenza vaccination is highly compartmentalized: while lineages in the blood are rarely measurably evolving following influenza vaccination, lineages containing GC B cells are frequently measurably evolving. Many of these lineages appear to derive from memory B cells. We conclude from these findings that seasonal influenza virus vaccination can stimulate additional evolution of responding B cell lineages, and imply that the poor efficacy of seasonal influenza vaccination is not due to a complete inhibition of vaccine-specific B cell evolution.

Harnessing immunological targets for COVID-19 immunotherapy

COVID-19 is an infectious and highly contagious disease caused by SARS-CoV-2. The immunotherapy strategy has a great potential to develop a permanent cure against COVID-19. Innate immune cells are in constant motion to scan molecular alteration to cells led by microbial infections throughout the body and helps in clearing invading viruses. Harnessing immunological targets for removing viral infection, generally based on the principle of enhancing the T-cell and protective immune responses. Currently-approved COVID-19 vaccines are mRNA encapsulated in liposomes that stimulate the host immune system to produce antibodies. Given the vital role of innate immunity, harnessing these immune responses opens up new hope for the generation of long-lasting and protective immunity against COVID-19.

Transcriptome Profiling of the Ovarian Cells at the Single-Cell Resolution in Adult Asian Seabass

Single-cell RNA sequencing (scRNA-seq) is widely adopted for identifying the signature molecular markers or regulators in cells, as this would benefit defining or isolating various types of cells. Likewise, the signature transcriptome profile analysis at the single cell level would well illustrate the key regulators or networks involved in gametogenesis and gonad development in animals; however, there is limited scRNA-seq analysis on gonadal cells in lower vertebrates, especially in the sexual reversal fish species. In this study, we analyzed the molecular signature of several distinct cell populations of Asian seabass adult ovaries through scRNA-seq. We identified five cell types and also successfully validated some specific genes of germ cells and granulosa cells. Likewise, we found some key pathways involved in ovarian development that may concert germline-somatic interactions. Moreover, we compared the transcriptomic profiles across fruit fly, mammals, and fish, and thus uncovered the conservation and divergence in molecular mechanisms that might drive ovarian development. Our results provide a basis for studying the crucial features of germ cells and somatic cells, which will benefit the understandings of the molecular mechanisms behind gametogenesis and gonad development in fish.

Alignment free identification of clones in B cell receptor repertoires

Following antigenic challenge, activated B cells rapidly expand and undergo somatic hypermutation, yielding groups of clonally related B cells with diversified immunoglobulin receptors. Inference of clonal relationships based on the receptor sequence is an essential step in many adaptive immune receptor repertoire sequencing studies. These relationships are typically identified by a multi-step process that involves: (i) grouping sequences based on shared V and J gene assignments, and junction lengths and (ii) clustering these sequences using a junction-based distance. However, this approach is sensitive to the initial gene assignments, which are error-prone, and fails to identify clonal relatives whose junction length has changed through accumulation of indels. Through defining a translation-invariant feature space in which we cluster the sequences, we develop an alignment free clonal identification method that does not require gene assignments and is not restricted to a fixed junction length. This alignment free approach has higher sensitivity compared to a typical junction-based distance method without loss of specificity and PPV. While the alignment free procedure identifies clones that are broadly consistent with the junction-based distance method, it also identifies clones with characteristics (multiple V or J gene assignments or junction lengths) that are not detectable with the junction-based distance method.

Memory B cell and antibody responses to flavivirus infection and vaccination

Flaviviruses are a group of mosquito- or tick-borne single-stranded RNA viruses that can cause a wide range of clinical manifestations in humans and animals, including asymptomatic, flu-like febrile illness, hemorrhagic fever, encephalitis, birth defects, and death. Many of them have no licensed vaccines available for human use. Memory B cell development and induction of neutralizing antibody responses, which are important for the control of flavivirus infection and dissemination, have been used as biomarkers for vaccine efficacy. In this review, we will discuss recent findings on memory B cells and antibody responses from studies in clinical specimen and animal models of flavivirus infection and vaccination with a focus on several clinically important flaviviruses, including dengue, West Nile, yellow fever, and Zika viruses.

Tuning the Surface Interactions between Single Cells and an OSTE+ Microwell Array for Enhanced Single Cell Manipulation

Retrieving single cells of interest from an array of microwells for further off-chip analysis is crucial in numerous biological applications. To this end, several single cell manipulation strategies have been developed, including optical tweezers (OT). OT represent a unique approach for contactless cell retrieval, but their performance is often suboptimal due to nonspecific cell adhesion to the microwell surface. In this study, we focused on improving the surface chemistry of microwell arrays to ensure efficient single cell manipulation using OT. For this purpose, the surface of an off-stoichiometry thiol-ene-epoxy (OSTE+) microwell array was grafted with polyethylene glycol (PEG) molecules with different molecular weights: PEG 360, PEG 500, PEG 2000, and a PEG Mix (an equimolar ratio of PEG 500 and PEG 2000). Contact angle measurements showed that the PEG grafting process resulted in an increased surface energy, which was stable for at least 16 weeks. Next, cell adhesion of two cell types, baker's yeast (Saccharomyces cerevisiae) and human B cells, to surfaces treated with different PEGs was evaluated by registering the presence of cellular motion inside microwells and the efficiency of optical lifting of cells that display motion. Optimal results were obtained for surfaces grafted with PEG 2000 and PEG Mix, reaching an average fraction of cells with motion of over 93% and an average lifting efficiency of over 96% for both cell types. Upon the integration of this microwell array with a polydimethylsiloxane (PDMS) microfluidic channel, PEG Mix resulted in proper washing of non-seeded cells. We further demonstrated the wide applicability of the platform by manipulating non-responding yeast cells to antifungal treatment and B cells expressing surface IgG antibodies. The combination of the optimized microwell surface with continuous microfluidics results in a powerful and versatile platform, allowing high-throughput single cell studies and retrieval of target cells for off-chip analysis.

A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies

Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However, current computational methods cannot identify epitope convergence between antibodies from different clonotypes, limiting the sequence diversity of antigen-specific antibodies that can be identified. We describe how the antibody binding site, the paratope, can be used to cluster antibodies with common antigen reactivity from different clonotypes. Our method, paratyping, uses the predicted paratope to identify these novel cross clonotype matches. We experimentally validated our predictions on a pertussis toxoid dataset. Our results show that even the simplest abstraction of the antibody binding site, using only the length of the loops involved and predicted binding residues, is sufficient to group antigen-specific antibodies and provide additional information to conventional clonotype analysis.

Abbreviations: BCR: B-cell receptor; CDR: complementarity-determining region; PTx: pertussis toxoid

Drug Repurposing for Prevention and Treatment of COVID-19: A Clinical Landscape

SARS-CoV-2, the novel coronavirus strain responsible for the current pandemic of COVID-19, has rendered the entire humanity suffering. Several months have passed since the pandemic has struck. However, the world is still looking for an effective treatment plan to battle the viral infection. The first vaccine just received emergency approval in December 2020 for use in USA and UK. These are excellent news, however, the worldwide distribution of such vaccine, the possibility of virus mutation and the lack of data regarding the long-term effects of such vaccines are a significant concern. In addition, although remdesivir was recently approved by the FDA to be used as a clinical drug against COVID-19, it hasn't stood out yet as a proven form of therapeutics. Such inability to produce a novel therapy has caused enough inconveniences for the affected people worldwide. Repurposing the already available drugs to fight against the virus seems to be a reasonable option amidst such uncertainty. Given the vast collection of potential treatment candidates to be explored against COVID-19, there is a decent chance that a success in this regard will serve the intermediary purpose of clinically treating the infection until a COVID-19 vaccine is widely distributed worldwide and will be able to treat COVID-19 patients that do not adequately respond to vaccines. Such treatments may prove very useful in future coronavirus outbreaks too. Proper research into these repurposing treatments may yield a certain insight into the field of novel treatment production as well. This review study accumulates a relevant set of information about drugs and vaccines against COVID-19, in terms of their repurposing properties and the specific phases of clinical trials they are undergoing across the world.  A potential timeline is also suggested to estimate when an effective result can be expected from the ongoing clinical trials for a better anticipation of the drug landscape. This study will hopefully help accelerate investment of resources into development and discovery of drugs and vaccines against the infection.

Maturation of the Human Immunoglobulin Heavy Chain Repertoire With Age

B cells play a central role in adaptive immune processes, mainly through the production of antibodies. The maturation of the B cell system with age is poorly studied. We extensively investigated age-related alterations of naïve and antigen-experienced immunoglobulin heavy chain (IgH) repertoires. The most significant changes were observed in the first 10 years of life, and were characterized by altered immunoglobulin gene usage and an increased frequency of mutated antibodies structurally diverging from their germline precursors. Older age was associated with an increased usage of downstream IgH constant region genes and fewer antibodies with self-reactive properties. As mutations accumulated with age, the frequency of germline-encoded self-reactive antibodies decreased, indicating a possible beneficial role of self-reactive B cells in the developing immune system. Our results suggest a continuous process of change through childhood across a broad range of parameters characterizing IgH repertoires and stress the importance of using well-selected, age-appropriate controls in IgH studies.

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their B cell receptor (BCR) sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse junction region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the AGPLv3 license.

IgM Antibody Repertoire Fingerprints in Mice Are Personalized but Robust to Viral Infection Status

Antibody repertoire sequencing provides a molecular fingerprint of current and past pathogens encountered by the immune system. Most repertoire studies in humans require measuring the B cell response in the blood, resulting in a large bias to the IgM isotype. The extent to which the circulating IgM antibody repertoire correlates to lymphoid tissue-resident B cells in the setting of viral infection remains largely uncharacterized. Therefore, we compared the IgM repertoires from both blood and bone marrow (BM) plasma cells (PCs) following acute or chronic lymphocytic choriomeningitis virus (LCMV) infection in mice. Despite previously reported serum alterations between acute and chronic infection, IgM repertoire signatures based on clonal diversity metrics, public clones, network, and phylogenetic analysis were largely unable to distinguish infection cohorts. Our findings, however, revealed mouse-specific repertoire fingerprints between the blood and PC repertoires irrespective of infection status.

A Review of SARS-CoV-2 and the Ongoing Clinical Trials

The sudden outbreak of 2019 novel coronavirus (2019-nCoV, later named SARS-CoV-2) in Wuhan, China, which rapidly grew into a global pandemic, marked the third introduction of a virulent coronavirus into the human society, affecting not only the healthcare system, but also the global economy. Although our understanding of coronaviruses has undergone a huge leap after two precedents, the effective approaches to treatment and epidemiological control are still lacking. In this article, we present a succinct overview of the epidemiology, clinical features, and molecular characteristics of SARS-CoV-2. We summarize the current epidemiological and clinical data from the initial Wuhan studies, and emphasize several features of SARS-CoV-2, which differentiate it from SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), such as high variability of disease presentation. We systematize the current clinical trials that have been rapidly initiated after the outbreak of COVID-19 pandemic. Whereas the trials on SARS-CoV-2 genome-based specific vaccines and therapeutic antibodies are currently being tested, this solution is more long-term, as they require thorough testing of their safety. On the other hand, the repurposing of the existing therapeutic agents previously designed for other virus infections and pathologies happens to be the only practical approach as a rapid response measure to the emergent pandemic, as most of these agents have already been tested for their safety. These agents can be divided into two broad categories, those that can directly target the virus replication cycle, and those based on immunotherapy approaches either aimed to boost innate antiviral immune responses or alleviate damage induced by dysregulated inflammatory responses. The initial clinical studies revealed the promising therapeutic potential of several of such drugs, including favipiravir, a broad-spectrum antiviral drug that interferes with the viral replication, and hydroxychloroquine, the repurposed antimalarial drug that interferes with the virus endosomal entry pathway. We speculate that the current pandemic emergency will be a trigger for more systematic drug repurposing design approaches based on big data analysis.

Inferring B cell specificity for vaccines using a Bayesian mixture model

Background

Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response.

Results

Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCRs. We use data from two studies to assess the model and estimate that we can identify vaccine-specific BCRs with 69% sensitivity.

Conclusion

Our results demonstrate that statistical modelling can capture patterns associated with vaccine response and identify vaccine specific B cells in a range of different data sets. Additionally, the B cells we identify as vaccine specific show greater levels of sequence similarity than expected, suggesting that there are additional signals of vaccine response, not currently considered, which could improve the identification of vaccine specific B cells.

Progress and applications of single-cell sequencing techniques

Single-cell sequencing (SCS) is a next-generation sequencing method that is mainly used to analyze differences in genetic and protein information between cells, to obtain genetic information on microorganisms that are difficult to cultivate at a single-cell level and to better understand their specific roles in the microenvironment. By sequencing the whole genome, transcriptome and epigenome of a single cell, the complex heterogeneous mechanisms involved in disease occurrence and progression can be revealed, further improving disease diagnosis, prognosis prediction and monitoring of the therapeutic effects of drugs. In this study, we mainly summarized the methods and application fields of SCS, which may provide potential references for its future clinical applications, including the analysis of embryonic and organ development, the immune system, cancer progression, and parasitic and infectious diseases as well as stem cell research, antibody screening, and therapeutic research and development.

VDJbase: an adaptive immune receptor genotype and haplotype database

VDJbase is a publicly available database that offers easy searching of data describing the complete sets of gene sequences (genotypes and haplotypes) inferred from adaptive immune receptor repertoire sequencing datasets. VDJbase is designed to act as a resource that will allow the scientific community to explore the genetic variability of the immunoglobulin (Ig) and T cell receptor (TR) gene loci. It can also assist in the investigation of Ig- and TR-related genetic predispositions to diseases. Our database includes web-based query and online tools to assist in visualization and analysis of the genotype and haplotype data. It enables users to detect those alleles and genes that are significantly over-represented in a particular population, in terms of genotype, haplotype and gene expression. The database website can be freely accessed at https://www.vdjbase.org/, and no login is required. The data and code use creative common licenses and are freely downloadable from https://bitbucket.org/account/user/yaarilab/projects/GPHP.

Broadly neutralizing human antibodies against dengue virus identified by single B cell transcriptomics

Eliciting broadly neutralizing antibodies (bNAbs) against the four dengue virus serotypes (DENV1-4) that are spreading into new territories is an important goal of vaccine design. To define bNAb targets, we characterized 28 antibodies belonging to expanded and hypermutated clonal families identified by transcriptomic analysis of single plasmablasts from DENV-infected individuals. Among these, we identified J9 and J8, two somatically related bNAbs that potently neutralized DENV1-4. Mutagenesis studies showed that the major recognition determinants of these bNAbs are in E protein domain I, distinct from the only known class of human bNAbs against DENV with a well-defined epitope. B cell repertoire analysis from acute-phase peripheral blood suggested that J9 and J8 followed divergent somatic hypermutation pathways, and that a limited number of mutations was sufficient for neutralizing activity. Our study suggests multiple B cell evolutionary pathways leading to DENV bNAbs targeting a new epitope that can be exploited for vaccine design.

Liver Is a Generative Site for the B Cell Response to Ehrlichia muris

The B cell response to Ehrlichia muris is dominated by plasmablasts (PBs), with few-if any-germinal centers (GCs), yet it generates protective immunoglobulin M (IgM) memory B cells (MBCs) that express the transcription factor T-bet and harbor V-region mutations. Because Ehrlichia prominently infects the liver, we investigated the nature of liver B cell response and that of the spleen. B cells within infected livers proliferated and underwent somatic hypermutation (SHM). Vh-region sequencing revealed trafficking of clones between the spleen and liver and often subsequent local clonal expansion and intraparenchymal localization of T-bet⁺ MBCs. T-bet⁺ MBCs expressed MBC subset markers CD80 and PD-L2. Many T-bet⁺ MBCs lacked CD11b or CD11c expression but had marginal zone (MZ) B cell phenotypes and colonized the splenic MZ, revealing T-bet⁺ MBC plasticity. Hence, liver and spleen are generative sites of B cell responses, and they include V-region mutation and result in liver MBC localization.

A spectral clustering-based method for identifying clones from high-throughput B cell repertoire sequencing data

Motivation

B cells derive their antigen-specificity through the expression of Immunoglobulin (Ig) receptors on their surface. These receptors are initially generated stochastically by somatic re-arrangement of the DNA and further diversified following antigen-activation by a process of somatic hypermutation, which introduces mainly point substitutions into the receptor DNA at a high rate. Recent advances in next-generation sequencing have enabled large-scale profiling of the B cell Ig repertoire from blood and tissue samples. A key computational challenge in the analysis of these data is partitioning the sequences to identify descendants of a common B cell (i.e. a clone). Current methods group sequences using a fixed distance threshold, or a likelihood calculation that is computationally-intensive. Here, we propose a new method based on spectral clustering with an adaptive threshold to determine the local sequence neighborhood. Validation using simulated and experimental datasets demonstrates that this method has high sensitivity and specificity compared to a fixed threshold that is optimized for these measures. In addition, this method works on datasets where choosing an optimal fixed threshold is difficult and is more computationally efficient in all cases. The ability to quickly and accurately identify members of a clone from repertoire sequencing data will greatly improve downstream analyses. Clonally-related sequences cannot be treated independently in statistical models, and clonal partitions are used as the basis for the calculation of diversity metrics, lineage reconstruction and selection analysis. Thus, the spectral clustering-based method here represents an important contribution to repertoire analysis.

Availability and implementation

Source code for this method is freely available in the SCOPe (Spectral Clustering for clOne Partitioning) R package in the Immcantation framework: www.immcantation.org under the CC BY-SA 4.0 license.

Supplementary information

Supplementary data are available at Bioinformatics online.

Mosaic deletion patterns of the human antibody heavy chain gene locus shown by Bayesian haplotyping

Analysis of antibody repertoires by high-throughput sequencing is of major importance in understanding adaptive immune responses. Our knowledge of variations in the genomic loci encoding immunoglobulin genes is incomplete, resulting in conflicting VDJ gene assignments and biased genotype and haplotype inference. Haplotypes can be inferred using IGHJ6 heterozygosity, observed in one third of the people. Here, we propose a robust novel method for determining VDJ haplotypes by adapting a Bayesian framework. Our method extends haplotype inference to IGHD- and IGHV-based analysis, enabling inference of deletions and copy number variations in the entire population. To test this method, we generated a multi-individual data set of naive B-cell repertoires, and found allele usage bias, as well as a mosaic, tiled pattern of deleted IGHD and IGHV genes. The inferred haplotypes may have clinical implications for genetic disease predispositions. Our findings expand the knowledge that can be extracted from antibody repertoire sequencing data.

Insights From Analysis of Human Antigen-Specific Memory B Cell Repertoires

Memory B cells that are generated during an infection or following vaccination act as sentinels to guard against future infections. Upon repeat antigen exposure memory B cells differentiate into new antibody-secreting plasma cells to provide rapid and sustained protection. Some pathogens evade or suppress the humoral immune system, or induce memory B cells with a diminished ability to differentiate into new plasma cells. This leaves the host vulnerable to chronic or recurrent infections. Single cell approaches coupled with next generation antibody gene sequencing facilitate a detailed analysis of the pathogen-specific memory B cell repertoire. Monoclonal antibodies that are generated from antibody gene sequences allow a functional analysis of the repertoire. This review discusses what has been learned thus far from analysis of diverse pathogen-specific memory B cell compartments and describes major differences in their repertoires. Such information may illuminate ways to advance the goal of improving vaccine and therapeutic antibody design.

Therapeutic Antibody Discovery in Infectious Diseases Using Single-Cell Analysis

Since the discovery of mouse hybridoma technology by Kohler and Milstein in 1975, significant progress has been made in monoclonal antibody production. Advances in B cell immortalization and phage display technologies have generated a myriad of valuable monoclonal antibodies for diagnosis and treatment. Technological breakthroughs in various fields of 'omics have shed crucial insights into cellular heterogeneity of a biological system in which the functional individuality of a single cell must be considered. Based on this important concept, remarkable discoveries in single-cell analysis have made in identifying and isolating functional B cells that produce beneficial therapeutic monoclonal antibodies. In this review, we will discuss three traditional methods of antibody discovery. Recent technological platforms for single-cell antibody discovery will be reviewed. We will discuss the application of the single-cell analysis in finding therapeutic antibodies for human immunodeficiency virus and emerging Zika arbovirus.

Network Representation of T-Cell Repertoire- A Novel Tool to Analyze Immune Response to Cancer Formation

The T cell repertoire potentially presents complexity compatible, or greater than, that of the human brain. T cell based immune response is involved with practically every part of human physiology, and high-throughput biology needed to follow the T-cell repertoire has made great leaps with the advent of massive parallel sequencing [1]. Nevertheless, tools to handle and observe the dynamics of this complexity have only recently started to emerge [e.g., 2, 3, 4] in parallel with sequencing technologies. Here, we present a network-based view of the dynamics of the T cell repertoire, during the course of mammary tumors development in a mouse model. The transition from the T cell receptor as a feature, to network-based clustering, followed by network-based temporal analyses, provides novel insights to the workings of the system and provides novel tools to observe cancer progression via the perspective of the immune system. The crux of the approach here is at the network-motivated clustering. The purpose of the clustering step is not merely data reduction and exposing structures, but rather to detect hubs, or attractors, within the T cell receptor repertoire that might shed light on the behavior of the immune system as a dynamic network. The Clone-Attractor is in fact an extension of the clone concept, i.e., instead of looking at particular clones we observe the extended clonal network by assigning clusters to graph nodes and edges to adjacent clusters (editing distance metric). Viewing the system as dynamical brings to the fore the notion of an attractors landscape, hence the possibility to chart this space and map the sample state at a given time to a vector in this large space. Based on this representation we applied two different methods to demonstrate its effectiveness in identifying changes in the repertoire that correlate with changes in the phenotype: (1) network analysis of the TCR repertoire in which two measures were calculated and demonstrated the ability to differentiate control from transgenic samples, and, (2) machine learning classifier capable of both stratifying control and trangenic samples, as well as to stratify pre-cancer and cancer samples.

Benchmarking Tree and Ancestral Sequence Inference for B Cell Receptor Sequences

B cell receptor sequences evolve during affinity maturation according to a Darwinian process of mutation and selection. Phylogenetic tools are used extensively to reconstruct ancestral sequences and phylogenetic trees from affinity-matured sequences. In addition to using general-purpose phylogenetic methods, researchers have developed new tools to accommodate the special features of B cell sequence evolution. However, the performance of classical phylogenetic techniques in the presence of B cell-specific features is not well understood, nor how much the newer generation of B cell specific tools represent an improvement over classical methods. In this paper we benchmark the performance of classical phylogenetic and new B cell-specific tools when applied to B cell receptor sequences simulated from a forward-time model of B cell receptor affinity maturation toward a mature receptor. We show that the currently used tools vary substantially in terms of tree structure and ancestral sequence inference accuracy. Furthermore, we show that there are still large performance gains to be achieved by modeling the special mutation process of B cell receptors. These conclusions are further strengthened with real data using the rules of isotype switching to count possible violations within each inferred phylogeny.

Predicting B cell receptor substitution profiles using public repertoire data

B cells develop high affinity receptors during the course of affinity maturation, a cyclic process of mutation and selection. At the end of affinity maturation, a number of cells sharing the same ancestor (i.e. in the same “clonal family”) are released from the germinal center; their amino acid frequency profile reflects the allowed and disallowed substitutions at each position. These clonal-family-specific frequency profiles, called “substitution profiles”, are useful for studying the course of affinity maturation as well as for antibody engineering purposes. However, most often only a single sequence is recovered from each clonal family in a sequencing experiment, making it impossible to construct a clonal-family-specific substitution profile. Given the public release of many high-quality large B cell receptor datasets, one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles. In this paper, we present the method “Substitution Profiles Using Related Families” (SPURF), a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence. Using this framework, we show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction. We fit this model on a large public dataset and validate the robustness of our approach on two external datasets. Furthermore, we provide a command-line tool in an open-source software package (https://github.com/krdav/SPURF) implementing these ideas and providing easy prediction using our pre-fit models.

Antibody engineering can be greatly informed by knowledge about the underlying affinity maturation process. As such this can be probed by sequencing, but unfortunately, in practice often only one member of the clonal family is sequenced, making it difficult to determine a set of possible amino acid mutations that would retain the original antibody antigen binding affinity. We overcome this data sparsity by developing a statistical learning approach that leverages vast information about amino acid preferences available in public immune system repertoire data. We use a penalized regression approach to devise a flexible statistical model that integrates multiple sources of information into a coherent prediction framework and validate our prediction algorithm using subsampling and held out data.

Tracing Antibody Repertoire Evolution by Systems Phylogeny

Antibody evolution studies have been traditionally limited to either tracing a single clonal lineage (B cells derived from a single V-(D)-J recombination) over time or examining bulk functionality changes (e.g., tracing serum polyclonal antibody proteins). Studying a single B cell disregards the majority of the humoral immune response, whereas bulk functional studies lack the necessary resolution to analyze the co-existing clonal diversity. Recent advances in high-throughput sequencing (HTS) technologies and bioinformatics have made it possible to examine multiple co-evolving antibody monoclonal lineages within the context of a single repertoire. A plethora of accompanying methods and tools have been introduced in hopes of better understanding how pathogen presence dictates the global evolution of the antibody repertoire. Here, we provide a comprehensive summary of the tremendous progress of this newly emerging field of systems phylogeny of antibody responses. We present an overview encompassing the historical developments of repertoire phylogenetics, state-of-the-art tools, and an outlook on the future directions of this fast-advancing and promising field.

Optimized Threshold Inference for Partitioning of Clones From High-Throughput B Cell Repertoire Sequencing Data

During adaptive immune responses, activated B cells expand and undergo somatic hypermutation of their B cell receptor (BCR), forming a clone of diversified cells that can be related back to a common ancestor. Identification of B cell clones from high-throughput Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data relies on computational analysis. Recently, we proposed an automated method to partition sequences into clonal groups based on single-linkage hierarchical clustering of the BCR junction region with length-normalized Hamming distance metric. This method could identify clonal sequences with high confidence on several benchmark experimental and simulated data sets. However, determining the threshold to cut the hierarchy, a key step in the method, is computationally expensive for large-scale repertoire sequencing data sets. Moreover, the methodology was unable to provide estimates of accuracy for new data. Here, a new method is presented that addresses this computational bottleneck and also provides a study-specific estimation of performance, including sensitivity and specificity. The method uses a finite mixture model fitting procedure for learning the parameters of two univariate curves which fit the bimodal distribution of the distance vector between pairs of sequences. These distributions are used to estimate the performance of different threshold choices for partitioning sequences into clones. These performance estimates are validated using simulated and experimental data sets. With this method, clones can be identified from AIRR-seq data with sensitivity and specificity profiles that are user-defined based on the overall goals of the study.

Evaluation of Antigen-Conjugated Fluorescent Beads to Identify Antigen-Specific B Cells

Selection of single antigen-specific B cells to identify their expressed antibodies is of considerable interest for evaluating human immune responses. Here, we present a method to identify single antibody-expressing cells using antigen-conjugated fluorescent beads. To establish this, we selected Folate Receptor alpha (FRα) as a model antigen and a mouse B cell line, expressing both the soluble and the membrane-bound forms of a human/mouse chimeric antibody (MOv18 IgG1) specific for FRα, as test antibody-expressing cells. Beads were conjugated to FRα using streptavidin/avidin-biotin bridges and used to select single cells expressing the membrane-bound form of anti-FRα. Bead-bound cells were single cell-sorted and processed for single cell RNA retrotranscription and PCR to isolate antibody heavy and light chain variable regions. Variable regions were then cloned and expressed as human IgG1/k antibodies. Like the original clone, engineered antibodies from single cells recognized native FRα. To evaluate whether antigen-coated beads could identify specific antibody-expressing cells in mixed immune cell populations, human peripheral blood mononuclear cells (PBMCs) were spiked with test antibody-expressing cells. Antigen-specific cells could comprise up to 75% of cells selected with antigen-conjugated beads when the frequency of the antigen-positive cells was 1:100 or higher. In PBMC pools, beads conjugated to recombinant antigens FRα and HER2 bound antigen-specific anti-FRα MOv18 and anti-HER2 Trastuzumab antibody-expressing cells, respectively. From melanoma patient-derived B cells selected with melanoma cell line-derived protein-coated fluorescent beads, we generated a monoclonal antibody that recognized melanoma antigen-coated beads. This approach may be further developed to facilitate analysis of B cells and their antibody profiles at the single cell level and to help unravel humoral immune repertoires.

The use of single-cell RNA-Seq to understand virus-host interactions

Single-cell analyses allow uncovering cellular heterogeneity, not only per se, but also in response to viral infection. Similarly, single cell transcriptome analyses (scRNA-Seq) can highlight specific signatures, identifying cell subsets with particular phenotypes, which are relevant in the understanding of virus-host interactions.

Many Routes to an Antibody Heavy-Chain CDR3: Necessary, Yet Insufficient, for Specific Binding

Because of its great potential for diversity, the immunoglobulin heavy-chain complementarity-determining region 3 (HCDR3) is taken as an antibody molecule’s most important component in conferring binding activity and specificity. For this reason, HCDR3s have been used as unique identifiers to investigate adaptive immune responses in vivo and to characterize in vitro selection outputs where display systems were employed. Here, we show that many different HCDR3s can be identified within a target-specific antibody population after in vitro selection. For each identified HCDR3, a number of different antibodies bearing differences elsewhere can be found. In such selected populations, all antibodies with the same HCDR3 recognize the target, albeit at different affinities. In contrast, within unselected populations, the majority of antibodies with the same HCDR3 sequence do not bind the target. In one HCDR3 examined in depth, all target-specific antibodies were derived from the same VDJ rearrangement, while non-binding antibodies with the same HCDR3 were derived from many different V and D gene rearrangements. Careful examination of previously published in vivo datasets reveals that HCDR3s shared between, and within, different individuals can also originate from rearrangements of different V and D genes, with up to 26 different rearrangements yielding the same identical HCDR3 sequence. On the basis of these observations, we conclude that the same HCDR3 can be generated by many different rearrangements, but that specific target binding is an outcome of unique rearrangements and VL pairing: the HCDR3 is necessary, albeit insufficient, for specific antibody binding.

Longitudinal multiparameter single-cell analysis of macaques immunized with pneumococcal protein-conjugated or unconjugated polysaccharide vaccines reveals distinct antigen specific memory B cell repertoires

BACKGROUND

The efficacy of protein-conjugated pneumococcal polysaccharide vaccines has been well characterized for children. The level of protection conferred by unconjugated polysaccharide vaccines remains less clear, particularly for elderly individuals who have had prior antigenic experience through immunization with unconjugated polysaccharide vaccines or natural exposure to Streptococcus pneumoniae.

METHODS

We compared the magnitude, diversity and genetic biases of antigen-specific memory B cells in two groups of adult cynomolgus macaques that were immunized with a 7-valent conjugated vaccine and boosted after five years with either a 13-valent pneumococcal polysaccharide conjugate vaccine (13vPnC) or a 23-valent unconjugated pneumococcal polysaccharide vaccine (23vPS) using microengraving (a single-cell analysis method) and single-cell RT-PCR.

RESULTS

Seven days after boosting, the mean frequency of antigen-specific memory B cells was significantly increased in macaques vaccinated with 13vPnC compared to those receiving 23vPS. The 13vPnC-vaccinated macaques also exhibited a more even distribution of antibody specificities to four polysaccharides in the vaccine (PS4, 6B, 14, 23F) that were examined. However, single-cell analysis of the antibody variable region sequences from antigen-specific B cells elicited by unconjugated and conjugated vaccines indicated that both the germline gene segments forming the heavy chains and the average lengths of the Complementary Determining Region 3 (CDR3) were similar.

CONCLUSIONS

Our results confirm that distinctive differences can manifest between antigen-specific memory B cell repertoires in nonhuman primates immunized with conjugated and unconjugated pneumococcal polysaccharide vaccines. The study also supports the notion that the conjugated vaccines have a favorable profile in terms of both the frequency and breadth of the anamnestic response among antigen-specific memory B cells.

Novel Approaches to Analyze Immunoglobulin Repertoires

Analysis of immunoglobulin (Ig) repertoires aims to comprehend Ig diversity with the goal of predicting humoral immune responses in the context of infection, vaccination, autoimmunity, and malignancies. The first next-generation sequencing (NGS) analyses of bulk B cell populations dramatically advanced sampling depth over previous low-throughput single-cell-based protocols, albeit at the expense of accuracy and loss of chain-pairing information. In recent years the field has substantially differentiated, with bulk analyses becoming more accurate while single-cell approaches have gained in throughput. Additionally, new platforms striving to combine high throughput and chain pairing have been developed as well as various computational tools for analysis. Here we review the developments of the past 4-5 years and discuss the open challenges.

A label-free immunoassay for Flavivirus detection by the Reflective Phantom Interface technology

Flaviviruses are widespread and cause clinically relevant arboviral diseases that impact locally and as imported travel-related infections. Direct detection of viraemia is limited, being typically undetectable at onset of symptoms. Therefore, diagnosis is primarily based on serology, which is complicated by high cross-reactivity across different species. The overlapping geographical distribution of the vectors in areas with a weak healthcare system, the increase of international travel and the similarity of symptoms highlight the need for rapid and reliable multi-parametric diagnostic tests in point-of-care formats. To this end we developed a bi-parametric serological microarray using recombinant NS1 proteins from Tick-borne encephalitis virus and West Nile virus coupled to a low-cost, label-free detection device based on the Reflective Phantom Interface (RPI) principle. Specific sequential detection of antibodies in solution demonstrates the feasibility of the approach for the surveillance and diagnosis of Flaviviruses.

Hierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data

Adaptive immunity is driven by the expansion, somatic hypermutation, and selection of B cell clones. Each clone is the progeny of a single B cell responding to Ag, with diversified Ig receptors. These receptors can now be profiled on a large scale by next-generation sequencing. Such data provide a window into the microevolutionary dynamics that drive successful immune responses and the dysregulation that occurs with aging or disease. Clonal relationships are not directly measured, but they must be computationally inferred from these sequencing data. Although several hierarchical clustering-based methods have been proposed, they vary in distance and linkage methods and have not yet been rigorously compared. In this study, we use a combination of human experimental and simulated data to characterize the performance of hierarchical clustering-based methods for partitioning sequences into clones. We find that single linkage clustering has high performance, with specificity, sensitivity, and positive predictive value all >99%, whereas other linkages result in a significant loss of sensitivity. Surprisingly, distance metrics that incorporate the biases of somatic hypermutation do not outperform simple Hamming distance. Although errors were more likely in sequences with short junctions, using the entire dataset to choose a single distance threshold for clustering is near optimal. Our results suggest that hierarchical clustering using single linkage with Hamming distance identifies clones with high confidence and provides a fully automated method for clonal grouping. The performance estimates we develop provide important context to interpret clonal analysis of repertoire sequencing data and allow for rigorous testing of other clonal grouping algorithms.

Exploration of immunoglobulin transcriptomes from mice immunized with three-finger toxins and phospholipases A2 from the Central American coral snake, Micrurus nigrocinctus

Snakebite envenomings represent a neglected public health issue in many parts of the rural tropical world. Animal-derived antivenoms have existed for more than a hundred years and are effective in neutralizing snake venom toxins when timely administered. However, the low immunogenicity of many small but potent snake venom toxins represents a challenge for obtaining a balanced immune response against the medically relevant components of the venom. Here, we employ high-throughput sequencing of the immunoglobulin (Ig) transcriptome of mice immunized with a three-finger toxin and a phospholipase A₂ from the venom of the Central American coral snake, Micrurus nigrocinctus. Although exploratory in nature, our indicate results showed that only low frequencies of mRNA encoding IgG isotypes, the most relevant isotype for therapeutic purposes, were present in splenocytes of five mice immunized with 6 doses of the two types of toxins over 90 days. Furthermore, analysis of Ig heavy chain transcripts showed that no particular combination of variable (V) and joining (J) gene segments had been selected in the immunization process, as would be expected after a strong humoral immune response to a single antigen. Combined with the titration of toxin-specific antibodies in the sera of immunized mice, these data support the low immunogenicity of three-finger toxins and phospholipases A₂found in M. nigrocinctusvenoms, and highlight the need for future studies analyzing the complexity of antibody responses to toxins at the molecular level.

Exploring viral infection using single-cell sequencing

Single-cell sequencing (SCS) has emerged as a valuable tool to study cellular heterogeneity in diverse fields, including virology. By studying the viral and cellular genome and/or transcriptome, the dynamics of viral infection can be investigated at single cell level. Most studies have explored the impact of cell-to-cell variation on the viral life cycle from the point of view of the virus, by analyzing viral sequences, and from the point of view of the cell, mainly by analyzing the cellular host transcriptome. In this review, we will focus on recent studies that use single-cell sequencing to explore viral diversity and cell variability in response to viral replication.

A Model of Somatic Hypermutation Targeting in Mice Based on High-Throughput Ig Sequencing Data

Analyses of somatic hypermutation (SHM) patterns in B cell Ig sequences have important basic science and clinical applications, but they are often confounded by the intrinsic biases of SHM targeting on specific DNA motifs (i.e., hot and cold spots). Modeling these biases has been hindered by the difficulty in identifying mutated Ig sequences in vivo in the absence of selection pressures, which skew the observed mutation patterns. To generate a large number of unselected mutations, we immunized B1-8 H chain transgenic mice with nitrophenyl to stimulate nitrophenyl-specific λ⁺ germinal center B cells and sequenced the unexpressed κ L chains using next-generation methods. Most of these κ sequences had out-of-frame junctions and were presumably uninfluenced by selection. Despite being nonfunctionally rearranged, they were targeted by SHM and displayed a higher mutation frequency than functional sequences. We used 39,173 mutations to construct a quantitative SHM targeting model. The model showed targeting biases that were consistent with classic hot and cold spots, yet revealed additional highly mutable motifs. We observed comparable targeting for functional and nonfunctional sequences, suggesting similar biological processes operate at both loci. However, we observed species- and chain-specific targeting patterns, demonstrating the need for multiple SHM targeting models. Interestingly, the targeting of C/G bases and the frequency of transition mutations at C/G bases was higher in mice compared with humans, suggesting lower levels of DNA repair activity in mice. Our models of SHM targeting provide insights into the SHM process and support future analyses of mutation patterns.

High-Throughput Sequencing-Based Immune Repertoire Study during Infectious Disease

The selectivity of the adaptive immune response is based on the enormous diversity of T and B cell antigen-specific receptors. The immune repertoire, the collection of T and B cells with functional diversity in the circulatory system at any given time, is dynamic and reflects the essence of immune selectivity. In this article, we review the recent advances in immune repertoire study of infectious diseases, which were achieved by traditional techniques and high-throughput sequencing (HTS) techniques. HTS techniques enable the determination of complementary regions of lymphocyte receptors with unprecedented efficiency and scale. This progress in methodology enhances the understanding of immunologic changes during pathogen challenge and also provides a basis for further development of novel diagnostic markers, immunotherapies, and vaccines.

B-cell repertoire dynamics after sequential hepatitis B vaccination and evidence for cross-reactive B-cell activation

BACKGROUND

A diverse B-cell repertoire is essential for recognition and response to infectious and vaccine antigens. High-throughput sequencing of B-cell receptor (BCR) genes can now be used to study the B-cell repertoire at great depth and may shed more light on B-cell responses than conventional immunological methods. Here, we use high-throughput BCR sequencing to provide novel insight into B-cell dynamics following a primary course of hepatitis B vaccination.

METHODS

Nine vaccine-naïve participants were administered three doses of hepatitis B vaccine (months 0, 1, and 2 or 7). High-throughput Illumina sequencing of the total BCR repertoire was combined with targeted sequencing of sorted vaccine antigen-enriched B cells to analyze the longitudinal response of both the total and vaccine-specific repertoire after each vaccine. ELISpot was used to determine vaccine-specific cell numbers following each vaccine.

RESULTS

Deconvoluting the vaccine-specific from total BCR repertoire showed that vaccine-specific sequence clusters comprised <0.1 % of total sequence clusters, and had certain stereotypic features. The vaccine-specific BCR sequence clusters were expanded after each of the three vaccine doses, despite no vaccine-specific B cells being detected by ELISpot after the first vaccine dose. These vaccine-specific BCR clusters detected after the first vaccine dose had distinct properties compared to those detected after subsequent doses; they were more mutated, present at low frequency even prior to vaccination, and appeared to be derived from more mature B cells.

CONCLUSIONS

These results demonstrate the high-sensitivity of our vaccine-specific BCR analysis approach and suggest an alternative view of the B-cell response to novel antigens. In the response to the first vaccine dose, many vaccine-specific BCR clusters appeared to largely derive from previously activated cross-reactive B cells that have low affinity for the vaccine antigen, and subsequent doses were required to yield higher affinity B cells.

Single-Cell Genomics for Virology

Single-cell sequencing technologies, i.e., single cell analysis followed by deep sequencing investigate cellular heterogeneity in many biological settings. It was only in the past year that single-cell sequencing analyses has been applied in the field of virology, providing new ways to explore viral diversity and cell response to viral infection, which are summarized in the present review.