High-Throughput Mapping of B Cell Receptor Sequences to Antigen Specificity

Activated polyreactive B cells are clonally expanded in autoantibody positive and patients with recent-onset type 1 diabetes

Autoreactive B cells play an important but ill-defined role in autoimmune type 1 diabetes (T1D). We isolated pancreatic islet antigen-reactive B cells from the peripheral blood of non-diabetic autoantibody-negative first-degree relatives, autoantibody-positive, and recent-onset T1D donors. Single-cell RNA sequencing analysis revealed that islet antigen-reactive B cells from autoantibody-positive and T1D donors had altered gene expression in pathways associated with B cell signaling and inflammation. Additionally, BCR sequencing uncovered a similar shift in islet antigen-reactive B cell repertoires among autoantibody-positive and T1D donors where greater clonal expansion was also observed. Notably, a substantial fraction of islet antigen-reactive B cells in autoantibody-positive and T1D donors appeared to be polyreactive, which was corroborated by analysis of recombinant monoclonal antibodies. These results expand our understanding of autoreactive B cell phenotypes during T1D and identify unique BCR repertoire changes that may serve as biomarkers for increased disease risk.

Public antibodies: convergent signatures in human humoral immunity against pathogens

The human humoral immune system has evolved to recognize a vast array of pathogenic threats. This ability is primarily driven by the immense diversity of antibodies generated by gene rearrangement during B cell development. However, different people often produce strikingly similar antibodies when exposed to the same antigen-known as public antibodies. Public antibodies not only reflect the immune system's ability to consistently select for optimal B cells but can also serve as signatures of the humoral responses triggered by infection and vaccination. In this Minireview, we examine and compare public antibody identification methods, including the identification criteria used based on V(D)J gene usage and similarity in the complementarity-determining region three sequences, and explore the molecular features of public antibodies elicited against common pathogens, including viruses, protozoa, and bacteria. Finally, we discuss the evolutionary significance and potential applications of public antibodies in informing the design of germline-targeting vaccines, predicting escape mutations in emerging viruses, and providing insights into the process of affinity maturation. The ongoing discovery of public antibodies in response to emerging pathogens holds the potential to improve pandemic preparedness, accelerate vaccine design efforts, and deepen our understanding of human B cell biology.

Contrastive Learning Enables Epitope Overlap Predictions for Targeted Antibody Discovery

Computational epitope prediction remains an unmet need for therapeutic antibody development. We present three complementary approaches for predicting epitope relationships from antibody amino acid sequences. First, we analyze ~18 million antibody pairs targeting ~250 protein families and establish that a threshold of >70% CDRH3 sequence identity among antibodies sharing both heavy and light chain V-genes reliably predicts overlapping-epitope antibody pairs. Next, we develop a supervised contrastive fine-tuning framework for antibody large language models which results in embeddings that better correlate with epitope information than those from pretrained models. Applying this contrastive learning approach to SARS-CoV-2 receptor binding domain antibodies, we achieve 82.7% balanced accuracy in distinguishing same-epitope versus different-epitope antibody pairs and demonstrate the ability to predict relative levels of structural overlap from learning on functional epitope bins (Spearman ρ = 0.25). Finally, we create AbLang-PDB, a generalized model for predicting overlapping-epitope antibodies for a broad range of protein families. AbLang-PDB achieves five-fold improvement in average precision for predicting overlapping-epitope antibody pairs compared to sequence-based methods, and effectively predicts the amount of epitope overlap among overlapping-epitope pairs (ρ = 0.81). In an antibody discovery campaign searching for overlapping-epitope antibodies to the HIV-1 broadly neutralizing antibody 8ANC195, 70% of computationally selected candidates demonstrated HIV-1 specificity, with 50% showing competitive binding with 8ANC195. Together, the computational models presented here provide powerful tools for epitope-targeted antibody discovery, while demonstrating the efficacy of contrastive learning for improving epitope-representation.

An expandable synthetic library of human paired antibody sequences

The potential diversity in the global repertoire of human antibody sequences is currently not well understood due to the limited existing paired antibody heavy-light chain sequence data that has been hindered by the low throughput and high costs of current single-cell sequencing methods. Here, we report IgHuAb, a large language model for high-throughput generation of paired human antibody sequences. Using IgHuAb, we created SynAbLib, a synthetic human antibody library that mimics population-level features of naturally occurring human antibody sequences, yet is associated with significantly greater diversity in sequence space. Further, experimental validation of a diverse set of antibodies from SynAbLib showed robust expression yields. IgHuAb and SynAbLib provide a readily expandable platform for human monoclonal antibody generation that can be efficiently mined for antibody sequences with target properties.

Systems Human Immunology and AI: Immune Setpoint and Immune Health

The immune system, critical for human health and implicated in many diseases, defends against pathogens, monitors physiological stress, and maintains tissue and organismal homeostasis. It exhibits substantial variability both within and across individuals and populations. Recent technological and conceptual progress in systems human immunology has provided predictive insights that link personal immune states to intervention responses and disease susceptibilities. Artificial intelligence (AI), particularly machine learning (ML), has emerged as a powerful tool for analyzing complex immune data sets, revealing hidden patterns across biological scales, and enabling predictive models for individualistic immune responses and potentially personalized interventions. This review highlights recent advances in deciphering human immune variation and predicting outcomes, particularly through the concepts of immune setpoint, immune health, and use of the immune system as a window for measuring health. We also provide a brief history of AI; review ML modeling approaches, including their applications in systems human immunology; and explore the potential of AI to develop predictive models and personal immune state embeddings to detect early signs of disease, forecast responses to interventions, and guide personalized health strategies.

Generation of antigen-specific paired chain antibody sequences using large language models

The traditional process of antibody discovery is limited by inefficiency, high costs, and low success rates. Recent approaches employing artificial intelligence (AI) have been developed to optimize existing antibodies and generate antibody sequences in a target-agnostic manner. In this work, we present MAGE (Monoclonal Antibody GEnerator), a sequence-based Protein Language Model (PLM) fine-tuned for the task of generating paired human variable heavy and light chain antibody sequences against targets of interest. We show that MAGE can generate novel and diverse antibody sequences with experimentally validated binding specificity against SARS-CoV-2, an emerging avian influenza H5N1, and respiratory syncytial virus A (RSV-A). MAGE represents a first-in-class model capable of designing human antibodies against multiple targets with no starting template.

Early influenza virus exposure shapes the B cell response to influenza vaccination in individuals 50 years later

Pre-existing immunity impacts vaccine responses to influenza, but directly connecting influenza infections early in life with immune responses decades later is difficult. However, H2N2 stopped circulating in the human population in 1968, creating the opportunity to directly evaluate the impact of early H2N2 exposure on vaccine responses 50 years later. Here, we vaccinated individuals born before (H2 exposed) or after (H2 naive) 1968 with an H2 hemagglutinin (HA) DNA plasmid and/or a ferritin nanoparticle vaccine. H2-exposed individuals generated a rapid B cell recall response that was more potent, targeted more conserved epitopes, and differed phenotypically from the de novo response in H2-naive individuals. Furthermore, vaccinating with a DNA versus a protein nanoparticle vaccine altered the response in H2-naive but not H2-exposed individuals. This study establishes and describes the lifelong impact of influenza HA-specific memory B cells formed early in life on vaccine responses decades later.

Longitudinal analysis of immune responses to SARS-CoV-2 recombinant vaccine S-268019-b in phase 1/2 prime-boost study

Background

The durability of vaccine-induced immune memory to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for preventing infection, especially severe disease.

Methods

This follow-up report from a phase 1/2 study of S-268019-b (a recombinant spike protein vaccine) after homologous booster vaccination confirms its long-term safety, tolerability, and immunogenicity.

Results

Booster vaccination with S-268019-b resulted in an enhancement of serum neutralizing antibody (NAb) titers and a broad range of viral neutralization. Single-cell immune profiling revealed persistent and mature antigen-specific memory B cells and T follicular helper cells, with increased B-cell receptor diversity. The expansion of B- and T-cell repertoires and presence of cross-reactive NAbs targeting conserved epitopes within the receptor-binding domain following a booster accounted for the broad-spectrum neutralizing activity.

Conclusion

These findings highlight the potential of S-268019-b to provide broad and robust protection against a range of SARS-CoV-2 variants, addressing a critical challenge in the ongoing fight against coronavirus disease 2019 (COVID-19).

Single-cell sequencing reveals the mechanisms of multiple myeloma progression: clarity or confusion?

Multiple myeloma (MM), the second most common hematologic malignancy, is characterized by the clonal expansion of myeloma cells and accumulation of genetic lesions. MM progression is accompanied by increased aggressiveness and drug resistance. Even the goal of "cure" remains hard to reach for most patients, advances in diagnosis and treatment have allowed some to achieve durable remissions and transition to plateau phase. Single-cell sequencing, with its powerful ability to analyze cellular heterogeneity and molecular patterns at ground-breaking resolution, is informative for deciphering tumors and their microenvironment. In this review, we summarize the new insights of studies facilitated by emerging single-cell sequencing into clonal evolution, myeloma-supported microenvironment transformation, epigenetic changes, and novel prognostic and therapeutic strategies for MM, revealing the key mechanisms underlying MM progression and the direction of future efforts. With the continuous expansion of the research scope and optimization of related technologies, single-cell sequencing is expected to revolutionize our understanding of the biology and evolutionary dynamics of MM and contribute to the radical and precise improvement of treatment.

Glycan-reactive antibodies isolated from human HIV-1 vaccine trial participants show broad pathogen cross-reactivity

HIV-1 continues to pose a significant global health challenge, requiring ongoing research into effective prevention and treatment strategies. Understanding the B cell repertoire that can be engaged upon vaccination in humans is crucial for the development of future preventive vaccines. In this study, PBMCs from HIV-negative participants in the multivalent HVTN124 human HIV-1 vaccine clinical trial were interrogated for HIV-reactive B cells using LIBRA-seq, a high-throughput B cell mapping technology. We report the discovery of glycan-reactive antibodies capable of neutralizing diverse heterologous HIV-1 virus strains. Further, isolated antibodies showed broad cross-reactivity against antigens from a variety of other pathogens, while remaining mostly negative on autoreactivity assays. The emerging class of glycan-reactive virus-neutralizing antibodies with exceptional breadth of pathogen cross-reactivity may present an effective target for vaccination at the population level.

Potent human antibodies against SpA5 identified by high-throughput single-cell sequencing of phase I clinical volunteers' B cells

The drug resistance problem of Staphylococcus aureus needs to be solved urgently. Here, we report the rapid identification of S. aureus human antibodies by high-throughput single-cell RNA and VDJ sequencing of memory B cells derived from 64 volunteers immunized with recombinant five-component S. aureus vaccine (clinical phase I). From 676 antigen-binding IgG1+ clonotypes, TOP10 sequences were selected for expression and characterization, with the most potent one, Abs-9, having nanomolar affinity for the pentameric form of the specific antigen S. aureus protein A. Abs-9 also demonstrated strong prophylactic efficacy in mice injected with lethal doses of a wide range of drug-resistant S. aureus strains. Additionally, the potential epitopes were predicted and validated based on Alphafold2 and molecular docking methods. In all, this study screened for a potent strain of antibody that prevents infection with antibiotic-resistant S. aureus, providing important data to guide the design of vaccines based on antibody architecture.

Learning the language of antibody hypervariability

Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples. Our learned feature representations accurately predict mutational effects on antigen binding, paratope identification, and other key antibody properties. We experimentally validate AbMAP for antibody optimization by applying it to refine a set of antibodies that bind to a SARS-CoV-2 peptide, and obtain an 82% hit-rate and up to 22-fold increase in binding affinity. AbMAP also unlocks large-scale analyses of immune repertoires, revealing that B-cell receptor repertoires of individuals, while remarkably different in sequence, converge toward similar structural and functional coverage. Importantly, AbMAP's transfer learning approach can be readily adapted to advances in foundational PLMs. We anticipate AbMAP will accelerate the efficient design and modeling of antibodies, expedite the discovery of antibody-based therapeutics, and deepen our understanding of humoral immunity.

The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository in the AIRR Data Commons: a practical guide for access, use and contributions through the Type 1 Diabetes AIRR Consortium

Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissue-specific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR/BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.

A multidonor class of highly glycan-dependent HIV-1 gp120-gp41 interface-targeting broadly neutralizing antibodies

Antibodies that target the gp120-gp41 interface of the HIV-1 envelope (Env) trimer comprise a commonly elicited category of broadly neutralizing antibodies (bNAbs). Here, we isolate and characterize VRC44, a bNAb lineage with up to 52% neutralization breadth. The cryoelectron microscopy (cryo-EM) structure of antibody VRC44.01 in complex with the Env trimer reveals binding to the same gp120-gp41 interface site of vulnerability as antibody 35O22 from a different HIV-1-infected donor. In addition to having similar angles of approach and extensive contacts with glycans N88 and N625, VRC44 and 35O22 derive from the same IGHV1-18 gene and share convergent mutations, indicating these two antibodies to be members of the only known highly glycan-dependent multidonor class. Strikingly, both lineages achieved almost 100% neutralization breadth against virus strains displaying high-mannose glycans. The high breadth and reproducible elicitation of VRC44 and 35O22 lineages validate germline-based methods of immunogen design for targeting the HIV-1 gp120-gp41 interface.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

Evaluating predictive patterns of antigen-specific B cells by single-cell transcriptome and antibody repertoire sequencing

The field of antibody discovery typically involves extensive experimental screening of B cells from immunized animals. Machine learning (ML)-guided prediction of antigen-specific B cells could accelerate this process but requires sufficient training data with antigen-specificity labeling. Here, we introduce a dataset of single-cell transcriptome and antibody repertoire sequencing of B cells from immunized mice, which are labeled as antigen specific or non-specific through experimental selections. We identify gene expression patterns associated with antigen specificity by differential gene expression analysis and assess their antibody sequence diversity. Subsequently, we benchmark various ML models, both linear and non-linear, trained on different combinations of gene expression and antibody repertoire features. Additionally, we assess transfer learning using features from general and antibody-specific protein language models (PLMs). Our findings show that gene expression-based models outperform sequence-based models for antigen-specificity predictions, highlighting a promising avenue for computationally guided antibody discovery.

FB5P-seq-mAbs: monoclonal antibody production from FB5P-seq libraries for integrative single-cell analysis of B cells

Parallel analysis of phenotype, transcriptome and antigen receptor sequence in single B cells is a useful method for tracking B cell activation and maturation during immune responses. However, in most cases, the specificity and affinity of the B cell antigen receptor cannot be inferred from its sequence. Antibody cloning and expression from single B cells is then required for functional assays. Here we propose a method that integrates FACS-based 5'-end single-cell RNA sequencing (FB5P-seq) and monoclonal antibody cloning for integrative analysis of single B cells. Starting from a cell suspension, single B cells are FACS-sorted into 96-well plates for reverse transcription, cDNA barcoding and amplification. A fraction of the single-cell cDNA is used for preparing 5'-end RNA-seq libraries that are sequenced for retrieving transcriptome-wide gene expression and paired BCR sequences. The archived cDNA of selected cells of interest is used as input for cloning heavy and light chain variable regions into antibody expression plasmid vectors. The corresponding monoclonal antibodies are produced by transient transfection of a eukaryotic producing cell line and purified for functional assays. We provide detailed step-by-step instructions and describe results obtained on ovalbumin-specific murine germinal center B cells after immunization. Our method is robust, flexible, cost-effective, and applicable to different B cell types and species. We anticipate it will be useful for mapping antigen specificity and affinity of rare B cell subsets characterized by defined gene expression and/or antigen receptor sequence.

High-throughput specificity profiling of antibody libraries using ribosome display and microfluidics

In this work, we developed PolyMap (polyclonal mapping), a high-throughput method for mapping protein-protein interactions. We demonstrated the mapping of thousands of antigen-antibody interactions between diverse antibody libraries isolated from convalescent and vaccinated COVID-19 donors and a set of clinically relevant SARS-CoV-2 spike variants. We identified over 150 antibodies with a variety of distinctive binding patterns toward the antigen variants and found a broader binding profile, including targeting of the Omicron variant, in the antibody repertoires of more recent donors. We then used these data to select mixtures of a small number of clones with complementary reactivity that together provide strong potency and broad neutralization. PolyMap is a generalizable platform that can be used for one-pot epitope mapping, immune repertoire profiling, and therapeutic design and, in the future, could be expanded to other families of interacting proteins.

Negative binomial mixture model for identification of noise in antibody-antigen specificity predictions from single-cell data

Motivation

LIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in single-cell B cell receptor sequencing data, such as LIBRA-seq, is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies.

Results

In this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model. This approach leverages single-cell sequencing reads from a large, multi-donor dataset described in a recent LIBRA-seq study to develop a data-driven means for identification of technical noise. We apply this method to nine donors representing separate LIBRA-seq experiments and show that our approach provides improved predictions for in vitro antibody-antigen binding when compared to the standard scoring method, despite variance in data size and noise structure across samples. This development will improve the ability of LIBRA-seq to identify antigen-specific B cells and contribute to providing more reliable datasets for machine learning based approaches as the corpus of single-cell B cell sequencing data continues to grow.

Availability and implementation

All data and code are available at https://github.com/IGlab-VUMC/mixture_model_denoising.

Multiplexed Antibody Sequencing and Profiling of the Human Hemagglutinin-specific Memory B Cell Response following Influenza Vaccination

Influenza virus is a highly contagious respiratory pathogen causing between 9.4 and 41 million infections per year in the United States in the last decade. Annual vaccination is recommended by the World Health Organization, with the goal to reduce influenza severity and transmission. Ag-specific single B cell sequencing methodologies have opened up new avenues into the dissection of the Ab response to influenza virus. The improvement of these methodologies is pivotal to reduce the associated costs and optimize the operational workflow and throughput, especially in the context of multiple samples. In this study, PBMCs and serum samples were collected longitudinally from eight influenza vaccinees either vaccinated yearly for four consecutive influenza seasons or once for one season. Following the serological and B cell profiling of their polyclonal Ab response to a panel of historical, recent, and next-generation influenza vaccine hemagglutinin (HA) and virus strains, a single multiplexed Ag-specific single B cell sequencing run allowed to capture HA-specific memory B cells that were analyzed for preferential Ig H chain/L chain pairing, isotype/subclass usage, and the presence of public BCR clonotypes across participants. Binding and functional profiles of representative private and public clonotypes confirmed their HA specificity, and their overall binding and functional activity were consistent with those observed at the polyclonal level. Collectively, this high-resolution and multiplexed Ab repertoire analysis demonstrated the validity of this optimized methodology in capturing Ag-specific BCR clonotypes, even in the context of a rare B cell population, such as in the case of the peripheral Ag-specific memory B cells.

Development of a new genotype-phenotype linked antibody screening system

Antibodies are powerful tools for the therapy and diagnosis of various diseases. In addition to conventional hybridoma-based screening, recombinant antibody-based screening has become a common choice; however, its application is hampered by two factors: (1) screening starts after Ig gene cloning and recombinant antibody production only, and (2) the antibody is composed of paired chains, heavy and light, commonly expressed by two independent expression vectors. Here, we introduce a method for the rapid screening of recombinant monoclonal antibodies by establishing a Golden Gate-based dual-expression vector and in-vivo expression of membrane-bound antibodies. Using this system, we demonstrate the rapid isolation of influenza cross-reactive antibodies with high affinity from immunized mice within 7 days. This system is particularly useful for isolating therapeutic or diagnostic antibodies, for example during foreseen pandemics.

Discovery and characterization of a pan-betacoronavirus S2-binding antibody

The continued emergence of deadly human coronaviruses from animal reservoirs highlights the need for pan-coronavirus interventions for effective pandemic preparedness. Here, using linking B cell receptor to antigen specificity through sequencing (LIBRA-seq), we report a panel of 50 coronavirus antibodies isolated from human B cells. Of these, 54043-5 was shown to bind the S2 subunit of spike proteins from alpha-, beta-, and deltacoronaviruses. A cryoelectron microscopy (cryo-EM) structure of 54043-5 bound to the prefusion S2 subunit of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike defined an epitope at the apex of S2 that is highly conserved among betacoronaviruses. Although non-neutralizing, 54043-5 induced Fc-dependent antiviral responses in vitro, including antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP). In murine SARS-CoV-2 challenge studies, protection against disease was observed after introduction of Leu234Ala, Leu235Ala, and Pro329Gly (LALA-PG) substitutions in the Fc region of 54043-5. Together, these data provide new insights into the protective mechanisms of non-neutralizing antibodies and define a broadly conserved epitope within the S2 subunit.

A potently neutralizing and protective human antibody targeting antigenic site V on RSV and hMPV fusion glycoprotein

Human respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) are frequent drivers of morbidity and mortality in susceptible populations, most often infantile, older adults, and immunocompromised. The primary target of neutralizing antibodies is the fusion (F) glycoprotein on the surface of the RSV and hMPV virion. As a result of the structural conservation between RSV and hMPV F, three antigenic regions are known to induce cross-neutralizing responses: sites III, IV, and V. Leveraging LIBRA-seq, we identify five RSV/hMPV cross-reactive human antibodies. One antibody, 5-1, potently neutralizes all tested viruses from the major subgroups of RSV and hMPV and provides protection against RSV and hMPV in a mouse challenge model. Structural analysis reveals that 5-1 utilizes an uncommon genetic signature to bind an epitope that spans sites Ø, II and V, defining a new mode of antibody cross-reactivity between RSV and hMPV F. These findings highlight the molecular and structural elements influencing RSV and hMPV cross-reactivity as well as the potential of antibody 5-1 for translational development.

Antibody sequence determinants of viral antigen specificity

Throughout life, humans experience repeated exposure to viral antigens through infection and vaccination, resulting in the generation of diverse, antigen-specific antibody repertoires. A paramount feature of antibodies that enables their critical contributions in counteracting recurrent and novel pathogens, and consequently fostering their utility as valuable targets for therapeutic and vaccine development, is the exquisite specificity displayed against their target antigens. Yet, there is still limited understanding of the determinants of antibody-antigen specificity, particularly as a function of antibody sequence. In recent years, experimental characterization of antibody repertoires has led to novel insights into fundamental properties of antibody sequences but has been largely decoupled from at-scale antigen specificity analysis. Here, using the LIBRA-seq technology, we generated a large data set mapping antibody sequence to antigen specificity for thousands of B cells, by screening the repertoires of a set of healthy individuals against 20 viral antigens representing diverse pathogens of biomedical significance. Analysis uncovered virus-specific patterns in variable gene usage, gene pairing, somatic hypermutation, as well as the presence of convergent antiviral signatures across multiple individuals, including the presence of public antibody clonotypes. Notably, our results showed that, for B-cell receptors originating from different individuals but leveraging an identical combination of heavy and light chain variable genes, there is a specific CDRH3 identity threshold above which B cells appear to exclusively share the same antigen specificity. This finding provides a quantifiable measure of the relationship between antibody sequence and antigen specificity and further defines experimentally grounded criteria for defining public antibody clonality.IMPORTANCEThe B-cell compartment of the humoral immune system plays a critical role in the generation of antibodies upon new and repeated pathogen exposure. This study provides an unprecedented level of detail on the molecular characteristics of antibody repertoires that are specific to each of the different target pathogens studied here and provides empirical evidence in support of a 70% CDRH3 amino acid identity threshold in pairs of B cells encoded by identical IGHV:IGL(K)V genes, as a means of defining public clonality and therefore predicting B-cell antigen specificity in different individuals. This is of exceptional importance when leveraging public clonality as a method to annotate B-cell receptor data otherwise lacking antigen specificity information. Understanding the fundamental rules of antibody-antigen interactions can lead to transformative new approaches for the development of antibody therapeutics and vaccines against current and emerging viruses.

B cell epitope mapping: The journey to better vaccines and therapeutic antibodies

B-cell epitope mapping is an approach that can identify and characterise specific antigen binding sites of B-cell receptors and secreted antibodies. The ability to determine the antigenic clusters of amino acids bound by B-cell clones provides unprecedented detail that will aid in developing novel and effective vaccine targets and therapeutic antibodies for various diseases. Here, we discuss conventional approaches and emerging techniques that are used to map B-cell epitopes.

Full-length single-cell BCR sequencing paired with RNA sequencing reveals convergent responses to pneumococcal vaccination

Single-cell RNA sequencing (scRNA-seq) can resolve transcriptional features from individual cells, but scRNA-seq techniques capable of resolving the variable regions of B cell receptors (BCRs) remain limited, especially from widely-used 3'-barcoded libraries. Here, we report a method that can recover paired, full-length variable region sequences of BCRs from 3'-barcoded scRNA-seq libraries. We first verify this method (B3E-seq) can produce accurate, full-length BCR sequences. We then apply this method to profile B cell responses elicited against the capsular polysaccharide of Streptococcus pneumoniae serotype 3 (ST3) by glycoconjugate vaccines in five infant rhesus macaques. We identify BCR features associated with specificity for the ST3 antigen which are present in multiple vaccinated monkeys, indicating a convergent response to vaccination. These results demonstrate the utility of our method to resolve key features of the B cell repertoire and profile antigen-specific responses elicited by vaccination.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

B cell tolerance and autoimmunity: Lessons from repertoires

Adaptive immune cell function is regulated by a highly diverse receptor recombined from variable germline-encoded segments that can recognize an almost unlimited array of epitopes. While this diversity enables the recognition of any pathogen, it also poses a risk of self-recognition, leading to autoimmunity. Many layers of regulation are present during both the generation and activation of B cells to prevent this phenomenon, although they are evidently imperfect. In recent years, our ability to analyze immune repertoires at scale has drastically increased, both through advances in sequencing and single-cell analyses. Here, we review the current knowledge on B cell repertoire analyses, focusing on their implication for autoimmunity. These studies demonstrate that a failure of tolerance occurs at multiple independent checkpoints in different autoimmune contexts, particularly during B cell maturation, plasmablast differentiation, and within germinal centers. These failures are marked by distinct repertoire features that may be used to identify disease- or patient-specific therapeutic approaches.

Epitope landscape in autoimmune neurological disease and beyond

Autoantibody binding has a central role in autoimmune diseases and has also been linked to cancer, infections, and behavioral disorders. Autoimmune neurological diseases remain misclassified also due to an incomplete understanding of the underlying disease-specific epitopes. Such epitopes are crucial for both pathology and diagnosis, but have historically been overlooked. Recent technological advancements have enabled the exploration of these epitopes, potentially opening novel clinical avenues. The precise identification of novel B and T cell epitopes and their autoreactivity has led to the discovery of autoantigen-specific biomarkers for patients at high risk of autoimmune neurological diseases. In this review, we propose utilizing newly available synthetic and cellular-surface display technologies and guide epitope-focused studies to unlock the potential of disease-specific epitopes for improving diagnosis and treatments. Additionally, we offer recommendations to guide emerging epitope-focused studies to broaden the current landscape.

Isolation and characterization of IgG3 glycan-targeting antibodies with exceptional cross-reactivity for diverse viral families

Broadly reactive antibodies that target sequence-diverse antigens are of interest for vaccine design and monoclonal antibody therapeutic development because they can protect against multiple strains of a virus and provide a barrier to evolution of escape mutants. Using LIBRA-seq (linking B cell receptor to antigen specificity through sequencing) data for the B cell repertoire of an individual chronically infected with human immunodeficiency virus type 1 (HIV-1), we identified a lineage of IgG3 antibodies predicted to bind to HIV-1 Envelope (Env) and influenza A Hemagglutinin (HA). Two lineage members, antibodies 2526 and 546, were confirmed to bind to a large panel of diverse antigens, including several strains of HIV-1 Env, influenza HA, coronavirus (CoV) spike, hepatitis C virus (HCV) E protein, Nipah virus (NiV) F protein, and Langya virus (LayV) F protein. We found that both antibodies bind to complex glycans on the antigenic surfaces. Antibody 2526 targets the stem region of influenza HA and the N-terminal domain (NTD) region of SARS-CoV-2 spike. A crystal structure of 2526 Fab bound to mannose revealed the presence of a glycan-binding pocket on the light chain. Antibody 2526 cross-reacted with antigens from multiple pathogens and displayed no signs of autoreactivity. These features distinguish antibody 2526 from previously described glycan-reactive antibodies. Further study of this antibody class may aid in the selection and engineering of broadly reactive antibody therapeutics and can inform the development of effective vaccines with exceptional breadth of pathogen coverage.

bcRflow: a Nextflow pipeline for characterizing B cell receptor repertoires from non-targeted transcriptomic data

B cells play a critical role in the adaptive recognition of foreign antigens through diverse receptor generation. While targeted immune sequencing methods are commonly used to profile B cell receptors (BCRs), they have limitations in cost and tissue availability. Analyzing B cell receptor profiling from non-targeted transcriptomics data is a promising alternative, but a systematic pipeline integrating tools for accurate immune repertoire extraction is lacking. Here, we present bcRflow, a Nextflow pipeline designed to characterize BCR repertoires from non-targeted transcriptomics data, with functional modules for alignment, processing, and visualization. bcRflow is a comprehensive, reproducible, and scalable pipeline that can run on high-performance computing clusters, cloud-based computing resources like Amazon Web Services (AWS), the Open OnDemand framework, or even local desktops. bcRflow utilizes institutional configurations provided by nf-core to ensure maximum portability and accessibility. To demonstrate the functionality of the bcRflow pipeline, we analyzed a public dataset of bulk transcriptomic samples from COVID-19 patients and healthy controls. We have shown that bcRflow streamlines the analysis of BCR repertoires from non-targeted transcriptomics data, providing valuable insights into the B cell immune response for biological and clinical research. bcRflow is available at https://github.com/Bioinformatics-Core-at-Childrens/bcRflow.

Large field of view and spatial region of interest transcriptomics in fixed tissue

Expression profiling in spatially defined regions is crucial for systematically understanding tissue complexity. Here, we report a method of photo-irradiation for in-situ barcoding hybridization and ligation sequencing, named PBHL-seq, which allows targeted expression profiling from the photo-irradiated region of interest in intact fresh frozen and formalin fixation and paraffin embedding (FFPE) tissue samples. PBHL-seq uses photo-caged oligodeoxynucleotides for in situ reverse transcription followed by spatially targeted barcoding of cDNAs to create spatially indexed transcriptomes of photo-illuminated regions. We recover thousands of differentially enriched transcripts from different regions by applying PBHL-seq to OCT-embedded tissue (E14.5 mouse embryo and mouse brain) and FFPE mouse embryo (E15.5). We also apply PBHL-seq to the subcellular microstructures (cytoplasm and nucleus, respectively) and detect thousands of differential expression genes. Thus, PBHL-seq provides an accessible workflow for expression profiles from the region of interest in frozen and FFPE tissue at subcellular resolution with areas expandable to centimeter scale, while preserving the sample intact for downstream analysis to promote the development of transcriptomics.

Rapid discovery of monoclonal antibodies by microfluidics-enabled FACS of single pathogen-specific antibody-secreting cells

Monoclonal antibodies are increasingly used to prevent and treat viral infections and are pivotal in pandemic response efforts. Antibody-secreting cells (ASCs; plasma cells and plasmablasts) are an excellent source of high-affinity antibodies with therapeutic potential. Current methods to study antigen-specific ASCs either have low throughput, require expensive and labor-intensive screening or are technically demanding and therefore not widely accessible. Here we present a straightforward technology for the rapid discovery of monoclonal antibodies from ASCs. Our approach combines microfluidic encapsulation of single cells into an antibody capture hydrogel with antigen bait sorting by conventional flow cytometry. With our technology, we screened millions of mouse and human ASCs and obtained monoclonal antibodies against severe acute respiratory syndrome coronavirus 2 with high affinity (<1 pM) and neutralizing capacity (<100 ng ml-1) in 2 weeks with a high hit rate (>85% of characterized antibodies bound the target). By facilitating access to the underexplored ASC compartment, the approach enables efficient antibody discovery and immunological studies into the generation of protective antibodies.

Benchmarking and integrating human B-cell receptor genomic and antibody proteomic profiling

Immunoglobulins (Ig), which exist either as B-cell receptors (BCR) on the surface of B cells or as antibodies when secreted, play a key role in the recognition and response to antigenic threats. The capability to jointly characterize the BCR and antibody repertoire is crucial for understanding human adaptive immunity. From peripheral blood, bulk BCR sequencing (bulkBCR-seq) currently provides the highest sampling depth, single-cell BCR sequencing (scBCR-seq) allows for paired chain characterization, and antibody peptide sequencing by tandem mass spectrometry (Ab-seq) provides information on the composition of secreted antibodies in the serum. Yet, it has not been benchmarked to what extent the datasets generated by these three technologies overlap and complement each other. To address this question, we isolated peripheral blood B cells from healthy human donors and sequenced BCRs at bulk and single-cell levels, in addition to utilizing publicly available sequencing data. Integrated analysis was performed on these datasets, resolved by replicates and across individuals. Simultaneously, serum antibodies were isolated, digested with multiple proteases, and analyzed with Ab-seq. Systems immunology analysis showed high concordance in repertoire features between bulk and scBCR-seq within individuals, especially when replicates were utilized. In addition, Ab-seq identified clonotype-specific peptides using both bulk and scBCR-seq library references, demonstrating the feasibility of combining scBCR-seq and Ab-seq for reconstructing paired-chain Ig sequences from the serum antibody repertoire. Collectively, our work serves as a proof-of-principle for combining bulk sequencing, single-cell sequencing, and mass spectrometry as complementary methods towards capturing humoral immunity in its entirety.

Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in various biological contexts. The capability to profile the landscape of antigen-binding affinity of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for profiling antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of the binding between antibodies and antigens that can be scaled to high-throughput sequencing data. Cmai achieved an AUROC of 0.91 in our validation cohort. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data. We found that, during immune-related adverse events (irAEs) caused by immune-checkpoint inhibitor (ICI) treatment, the humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. In contrast, extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of ICI treatment response. Overall, Cmai and our biomarker approach filled in a gap that is not addressed by current antibody optimization works nor works such as AlphaFold3 that predict the structures of complexes of proteins that are known to bind.

Parallel single B cell transcriptomics to elucidate pig B cell repertoire

Pork is the most widely consumed meat on the planet, placing swine health as a critical factor for both the world economy and the food industry. Infectious diseases in pigs not only threaten these sectors but also raise zoonotic concerns, as pigs can act as "mixing vessels" for several animals and human viruses and can lead to the emergence of new viruses that are capable of infecting humans. Several efforts are ongoing to develop pig vaccines, albeit with limited success. This has been largely attributed to the complex nature of pig infections and incomplete understanding of the pig immune responses. Additionally, pig has been suggested to be a good experimental model to study viral infections (e.g., human influenza). Despite the significant importance of studying pig immunology for developing infection models, zoonosis, and the crucial need to develop better swine vaccines, there is still very limited information on the response of the swine adaptive immune system to several emerging pathogens. Particularly, very little is known about the pig B cell repertoire upon infection. Understanding the B cell repertoire is especially crucial towards designing better vaccines, predicting zoonosis and can provide insights into developing new diagnostic agents. Here, we developed methods for performing parallel single pig B cell (up to 10,000 B cells) global and immunoglobulin transcriptome sequencing. We then adapted a computational pipeline previously built for human/mouse sequences, to now analyze pig sequences. This allowed us to comprehensively map the B cell repertoire and get paired antibody sequences from pigs in a single parallel sequencing experiment. We believe that these approaches will have significant implications for swine diseases, particularly in the context of swine mediated zoonosis and swine and human vaccine development.

Two-dimensional high-throughput on-cell screening of immunoglobulins against broad antigen repertoires

Identifying high-affinity antibodies in human serum is challenging due to extremely low number of circulating B cells specific to the desired antigens. Delays caused by a lack of information on the immunogenic proteins of viral origin hamper the development of therapeutic antibodies. We propose an efficient approach allowing for enrichment of high-affinity antibodies against pathogen proteins with simultaneous epitope mapping, even in the absence of structural information about the pathogenic immunogens. To screen therapeutic antibodies from blood of recovered donors, only pathogen transcriptome is required to design an antigen polypeptide library, representing pathogen proteins, exposed on the bacteriophage surface. We developed a two-dimensional screening approach enriching lentiviral immunoglobulin libraries from the convalescent or vaccinated donors against bacteriophage library expressing the overlapping set of polypeptides covering the spike protein of SARS-CoV-2. This platform is suitable for pathogen-specific immunoglobulin enrichment and allows high-throughput selection of therapeutic human antibodies.

Monoclonal Antibody Generation Using Single B Cell Screening for Treating Infectious Diseases

The screening of antigen-specific B cells has been pivotal for biotherapeutic development for over four decades. Conventional antibody discovery strategies, including hybridoma technology and single B cell screening, remain widely used based on their simplicity, accessibility, and proven track record. Technological advances and the urgent demand for infectious disease applications have shifted paradigms in single B cell screening, resulting in increased throughput and decreased time and labor, ultimately enabling the rapid identification of monoclonal antibodies with desired biological and biophysical properties. Herein, we provide an overview of conventional and emergent single B cell screening approaches and highlight their potential strengths and weaknesses. We also detail the impact of innovative technologies-including miniaturization, microfluidics, multiplexing, and deep sequencing-on the recent identification of broadly neutralizing antibodies for infectious disease applications. Overall, the coronavirus disease 2019 (COVID-19) pandemic has reinvigorated efforts to improve the efficiency of monoclonal antibody discovery, resulting in the broad application of innovative antibody discovery methodologies for treating a myriad of infectious diseases and pathological conditions.

Islet-antigen reactive B cells display a unique phenotype and BCR repertoire in autoantibody positive and recent-onset type 1 diabetes patients

Autoreactive B cells play an important but ill-defined role in autoimmune type 1 diabetes (T1D). To better understand their contribution, we performed single cell gene and BCR-seq analysis on pancreatic islet antigen-reactive (IAR) B cells from the peripheral blood of nondiabetic (ND), autoantibody positive prediabetic (AAB), and recent-onset T1D individuals. We found that the frequency of IAR B cells was increased in AAB and T1D. IAR B cells from these donors had altered expression of B cell signaling, pro-inflammatory, infection, and antigen processing and presentation genes. Both AAB and T1D donors demonstrated a significant increase in certain heavy and light chain V genes, and these V genes were enriched in islet-reactivity. Public clones of IAR B cells were restricted almost entirely to AAB and T1D donors. IAR B cells were clonally expanded in the autoimmune donors, particularly the AAB group. Notably, a substantial fraction of IAR B cells in AAB and T1D donors appeared to be polyreactive, which was corroborated by analysis of recombinant monoclonal antibodies. These results expand our understanding of autoreactive B cell activation during T1D and identify unique BCR repertoire changes that may serve as biomarkers for increased disease risk.

The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research

Single-cell RNA sequencing technology (scRNA-seq) has been steadily developing since its inception in 2009. Unlike bulk RNA-seq, scRNA-seq identifies the heterogeneity of tissue cells and reveals gene expression changes in individual cells at the microscopic level. Here, we review the development of scRNA-seq, which has gone through iterations of reverse transcription, in vitro transcription, smart-seq, drop-seq, 10 × Genomics, and spatial single-cell transcriptome technologies. The technology of 10 × Genomics has been widely applied in medicine and biology, producing rich research results. Furthermore, this review presents a summary of the analytical process for single-cell transcriptome data and its integration with other omics analyses, including genomes, epigenomes, proteomes, and metabolomics. The single-cell transcriptome has a wide range of applications in biology and medicine. This review analyzes the applications of scRNA-seq in cancer, stem cell research, developmental biology, microbiology, and other fields. In essence, scRNA-seq provides a means of elucidating gene expression patterns in single cells, thereby offering a valuable tool for scientific research. Nevertheless, the current single-cell transcriptome technology is still imperfect, and this review identifies its shortcomings and anticipates future developments. The objective of this review is to facilitate a deeper comprehension of scRNA-seq technology and its applications in biological and medical research, as well as to identify avenues for its future development in alignment with practical needs.

Antigen-level resolution of commensal-specific B cell responses can be enabled by phage display screening coupled with B cell tetramers

Induction of commensal-specific immunity contributes to tissue homeostasis, yet the mechanisms underlying induction of commensal-specific B cells remain poorly understood in part due to a lack of tools to identify these cells. Using phage display, we identified segmented filamentous bacteria (SFB) antigens targeted by serum and intestinal antibodies and generated B cell tetramers to track SFB-specific B cells in gut-associated lymphoid tissues. We revealed a compartmentalized response in SFB-specific B cell activation, with a gradient of immunoglobulin A (IgA), IgG1, and IgG2b isotype production along Peyer's patches contrasted by selective production of IgG2b within mesenteric lymph nodes. V(D)J sequencing and monoclonal antibody generation identified somatic hypermutation driven affinity maturation to SFB antigens under homeostatic conditions. Combining phage display and B cell tetramers will enable investigation of the ontogeny and function of commensal-specific B cell responses in tissue immunity, inflammation, and repair.

Mosaic RBD nanoparticle elicits immunodominant antibody responses across sarbecoviruses

Nanoparticle vaccines displaying mosaic receptor-binding domains (RBDs) or spike (S) from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or other sarbecoviruses are used in preparedness against potential zoonotic outbreaks. Here, we describe a self-assembling nanoparticle using lumazine synthase (LuS) as the scaffold to display RBDs from different sarbecoviruses. Mosaic nanoparticles induce sarbecovirus cross-neutralizing antibodies comparable to a nanoparticle cocktail. We find mosaic nanoparticles elicit a B cell receptor repertoire using an immunodominant germline gene pair of IGHV14-3:IGKV14-111. Most of the tested IGHV14-3:IGKV14-111 monoclonal antibodies (mAbs) are broadly cross-reactive to clade 1a, 1b, and 3 sarbecoviruses. Using mAb competition and cryo-electron microscopy, we determine that a representative IGHV14-3:IGKV14-111 mAb, M2-7, binds to a conserved epitope on the RBD, largely overlapping with the pan-sarbecovirus mAb S2H97. This suggests mosaic nanoparticles expand B cell recognition of the common epitopes shared by different clades of sarbecoviruses. These results provide immunological insights into the cross-reactive responses elicited by mosaic nanoparticles against sarbecoviruses.

Potent HPIV3-neutralizing IGHV5-51 Antibodies Identified from Multiple Individuals Show L Chain and CDRH3 Promiscuity

Human parainfluenza virus 3 (HPIV3) is a widespread pathogen causing severe and lethal respiratory illness in at-risk populations. Effective countermeasures are in various stages of development; however, licensed therapeutic and prophylactic options are not available. The fusion glycoprotein (HPIV3 F), responsible for facilitating viral entry into host cells, is a major target of neutralizing Abs that inhibit infection. Although several neutralizing Abs against a small number of HPIV3 F epitopes have been identified to date, relatively little is known about the Ab response to HPIV3 compared with other pathogens, such as influenza virus and SARS-CoV-2. In this study, we aimed to characterize a set of HPIV3-specific Abs identified in multiple individuals for genetic signatures, epitope specificity, neutralization potential, and publicness. We identified 12 potently neutralizing Abs targeting three nonoverlapping epitopes on HPIV3 F. Among these, six Abs identified from two different individuals used Ig heavy variable gene IGHV 5-51, with five of the six Abs targeting the same epitope. However, despite the use of the same H chain variable (VH) gene, these Abs used multiple different L chain variable genes (VL) and diverse H chain CDR 3 (CDRH3) sequences. Together, these results provide further information about the genetic and functional characteristics of HPIV3-neutralizing Abs and suggest the existence of a reproducible VH-dependent Ab response associated with VL and CDRH3 promiscuity. Understanding sites of HPIV3 F vulnerability and the genetic and molecular characteristics of Abs targeting these sites will help guide efforts for effective vaccine and therapeutic development.

Single-cell immune repertoire analysis

Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.

Tracking the immune response profiles elicited by the BNT162b2 vaccine in COVID-19 unexperienced and experienced individuals

Multiple vaccines have been approved to control COVID-19 pandemic, with Pfizer/BioNTech (BNT162b2) being widely used. We conducted a longitudinal analysis of the immune response elicited after three doses of the BNT162b2 vaccine in individuals who have previously experienced SARS-CoV-2 infection and in unexperienced ones. We conducted immunological analyses and single-cell transcriptomics of circulating T and B lymphocytes, combined to CITE-seq or LIBRA-seq, and VDJ-seq. We found that antibody levels against SARS-CoV-2 Spike, NTD and RBD from wild-type, delta and omicron VoCs show comparable dynamics in both vaccination groups, with a peak after the second dose, a decline after six months and a restoration after the booster dose. The antibody neutralization activity was maintained, with lower titers against the omicron variant. Spike-specific memory B cell response was sustained over the vaccination schedule. Clonal analysis revealed that Spike-specific B cells were polyclonal, with a partial clone conservation from natural infection to vaccination. Spike-specific T cell responses were oriented towards effector and effector memory phenotypes, with similar trends in unexperienced and experienced individuals. The CD8 T cell compartment showed a higher clonal expansion and persistence than CD4 T cells. The first two vaccinations doses tended to induce new clones rather than promoting expansion of pre-existing clones. However, we identified a fraction of Spike-specific CD8 T cell clones persisting from natural infection that were boosted by vaccination and clones specifically induced by vaccination. Collectively, our observations revealed a moderate effect of the second dose in enhancing the immune responses elicited after the first vaccination. Differently, we found that a third dose was necessary to restore comparable levels of neutralizing antibodies and Spike-specific T and B cell responses in individuals who experienced a natural SARS-CoV-2 infection.

Vaccinating people living with HIV: a fast track to preventive and therapeutic HIV vaccines

Globally, the number of new HIV infections remains unacceptably high, and urgent new approaches are needed to advance HIV vaccine science. However, the development of a preventive HIV vaccine has proven to be an intractable scientific challenge. Recent advances in HIV immunogen design have taken the field a step closer to triggering the rare precursors of broadly neutralising antibodies, which are widely assumed to be necessary for a vaccine. Nonetheless, these same studies and previous studies in people living with HIV have also highlighted the major hurdles that must be overcome to boost the cross-reactivity and potency of these responses to sufficient levels. Here, we describe an opportunity for fast-tracking the evaluation of candidate preventive and therapeutic vaccines by immunising people with HIV who are antiretroviral therapy suppressed. We argue that such studies, unlike traditional studies of vaccines in participants not infected with HIV, will be faster and more informative and will allow the vaccine field to bypass multiple hurdles. This approach will accelerate the process of defining the capacity of immunogens to trigger relevant antibodies, currently an extremely slow and expensive pathway, and provide a quick path to creating an HIV vaccine.

Innovations and trends in antibody repertoire analysis

Monoclonal antibodies have revolutionized the treatment of human diseases, which has made them the fastest-growing class of therapeutics, with global sales expected to reach $346.6 billion USD by 2028. Advances in antibody engineering and development have led to the creation of increasingly sophisticated antibody-based therapeutics (e.g. bispecific antibodies and chimeric antigen receptor T cells). However, approaches for antibody discovery have remained comparatively grounded in conventional yet reliable in vitro assays. Breakthrough developments in high-throughput single B-cell sequencing and immunoglobulin proteomic serology, however, have enabled the identification of high-affinity antibodies directly from endogenous B cells or circulating immunoglobulin produced in vivo. Moreover, advances in artificial intelligence offer vast potential for antibody discovery and design with large-scale repertoire datasets positioned as the optimal source of training data for such applications. We highlight advances and recent trends in how these technologies are being applied to antibody repertoire analysis.

Frequency-potency analysis of IgG+ memory B cells delineates neutralizing antibody responses at single-cell resolution

Identifying individual functional B cell receptors (BCRs) is common, but two-dimensional analysis of B cell frequency versus BCR potency would delineate both quantity and quality of antigen-specific memory B cells. We efficiently determine quantitative BCR neutralizing activities using a single-cell-derived antibody supernatant analysis (SCAN) workflow and develop a frequency-potency algorithm to estimate B cell frequencies at various neutralizing activity or binding affinity cutoffs. In an HIV-1 fusion peptide (FP) immunization study, frequency-potency curves elucidate the quantity and quality of FP-specific immunoglobulin G (IgG)+ memory B cells for different animals, time points, and antibody lineages at single-cell resolution. The BCR neutralizing activities are mainly determined by their affinities to soluble envelope trimer. Frequency analysis definitively demonstrates dominant neutralizing antibody lineages. These findings establish SCAN and frequency-potency analyses as promising approaches for general B cell analysis and monoclonal antibody (mAb) discovery. They also provide specific rationales for HIV-1 FP-directed vaccine optimization.

Design of Antigen-Specific Antibody CDRH3 Sequences Using AI and Germline-Based Templates

Antibody-antigen specificity is engendered and refined through a number of complex B cell processes, including germline gene recombination and somatic hypermutation. Here, we present an AI-based technology for de novo generation of antigen-specific antibody CDRH3 sequences using germline-based templates, and validate this technology through the generation of antibodies against SARS-CoV-2. AI-based processes that mimic the outcome, but bypass the complexity of natural antibody generation, can be efficient and effective alternatives to traditional experimental approaches for antibody discovery.

B cell receptor repertoire analysis in primary Sjogren's syndrome salivary glands identifies repertoire features associated with clinical activity

BACKGROUND

Primary Sjogren's syndrome (pSS) is a complex autoimmune disease featuring damage to salivary and lacrimal glands, with the possibility of manifestations across multiple organs. Antibody-producing B cells have long been appreciated to play a significant role in pSS pathogenesis, with a number of autoreactive antibody species having been identified to be elevated in pSS patients. While several studies have attempted to characterize the BCR repertoires of peripheral blood B cells in pSS patients, much remains unknown about the repertoire characteristics of gland-infiltrating B cells.

METHODS

Through paired scRNAseq and scBCRseq, we profiled the BCR repertoires of both infiltrating and circulating B cells in a small cohort of patients. We further utilize receptor reconstruction analyses to further investigate repertoire characteristics in a wider cohort of pSS patients previously profiled through RNAseq.

RESULTS

Via integrated BCR and transcriptome analysis of B cell clones, we generate a trajectory progression pattern for infiltrated memory B cells in pSS. We observe significant differences in BCR repertoires between the peripheral blood and labial gland B cells of pSS patients in terms of relative expansion, isotype usage, and BCR clustering. We further observe significant decreases in IgA2 isotype usage among pSS patient labial and parotid gland B cells these analyses relative to controls as well as a positive correlation between kappa/lambda light chain usage and clinical disease activity.

CONCLUSIONS

Through BCR repertoire analysis of pSS patient salivary glands, we identify a number of novel repertoire characteristics that may serve as useful indicators of clinical disease and disease activity. By collecting these BCR repertoires into an accessible database, we hope to also enable comparative analysis of patient repertoires in pSS and potentially other autoimmune disorders.