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

Mature tertiary lymphoid structures evoke intra-tumoral T and B cell responses via progenitor exhausted CD4+ T cells in head and neck cancer

Tumor tertiary lymphoid structures (TLS), especially mature TLS (mTLS), have been associated with better prognosis and improved responses to immune checkpoint blockade (ICB), but the underlying mechanisms remain incompletely understood. Here, by performing single-cell RNA, antigen receptor sequencing and spatial transcriptomics on tumor tissue from head and neck squamous cell carcinoma (HNSCC) patients with different statuses of TLS, we observe that mTLS are enriched with stem-like T cells, and B cells at various maturation stages. Notably, progenitor exhausted CD4+ T cells, with features resembling follicular helper T cells, support these responses, by activating B cells to produce plasma cells in the germinal center, and interacting with DC-LAMP+ dendritic cells to support CD8+ T cell activation. Conversely, non-mTLS tumors do not promote local anti-tumor immunity which is abundant of immunosuppressive cells or a lack of stem-like B and T cells. Furthermore, patients with mTLS manifest improved overall survival and response to ICB compared to those with non-mTLS. Overall, our study provides insights into mechanisms underlying mTLS-mediated intra-tumoral immunity events against cancer.

Systemic 4-1BB stimulation augments extrafollicular memory B cell formation and recall responses during Plasmodium infection

T-dependent germinal center (GC) output, comprising plasma cells and memory B cells (MBCs), is crucial for clearance of Plasmodium infection and protection against reinfection. In this study, we examine the effect of an agonistic antibody targeting 4-1BB (CD137) during experimental malaria. We show that exogenous 4-1BB stimulation, despite delaying the effector GC response, surprisingly enhances humoral memory recall and protection from reinfection. Single-cell RNA and assay for transposase-accessible chromatin (ATAC) sequencing of MBCs from mice receiving 4-1BB stimulation reveal populations with distinct transcriptional signatures and a chromatin landscape indicative of superior recall and proliferative potential. Importantly, our results indicate that the effects of 4-1BB stimulation are dependent on interleukin (IL)-9R signaling in B cells but independent of parasite load during primary infection. Our study proposes an immunomodulatory approach to enhance the quality of the MBC pool, providing superior protection during infection and vaccination, particularly in the context of malaria.

Self-reactive B cells are increased in all major stages of peripheral development in Sjögren's disease

Sjögren's disease (SjD) is a chronic autoimmune disorder characterized by increased circulating self-reactive B cells. While many of these self-reactive B cells emerge from the bone marrow, it is not known whether they are excluded from or enriched in specific developmental stages in the periphery. The aim of this study was to determine the immunophenotype of circulating self-reactive B cells in SjD to inform more precise therapeutic targeting. Five major B cell populations: transitional, mature naïve, switched memory, double negative and plasmablasts were single-cell sorted and cultured to produce IgG. Self-reactive IgG was identified by ELISA, flow cytometry of permeabilized HEK293 cells and HEp-2 indirect immunofluorescence. Immunoglobulin heavy chains were sequenced by Sanger and next-generation sequencing. Compared with healthy donor controls (HCs), SjD patients had higher frequencies of naïve and CD21low atypical memory B cell subsets, while antigen-experienced B cells expressed more Ki67 and CD86. B cells recognizing intracellular self-antigens were identified in all stages of peripheral B cell development for SjD and HCs, but frequencies of autoreactive B cells were up to 10-fold higher in SjD. Self-reactive transitional B cells expressed higher surface CD38 and lower surface IgM. An increase in self-reactive B cells throughout peripheral development in SjD compared with HCs suggests that counterselection of autoantibody-bearing B cells during central and peripheral tolerance checkpoints are reduced in SjD. Therapeutic strategies focused on depleting B cells based on B cell receptor specificity rather than the developmental stage would be more efficient to target self-reactive B cells in SjD.

The physiological landscape and specificity of antibody repertoires are consolidated by multiple immunizations

Diverse antibody repertoires spanning multiple lymphoid organs (i.e., bone marrow, spleen, lymph nodes) form the foundation of protective humoral immunity. Changes in their composition across lymphoid organs are a consequence of B-cell selection and migration events leading to a highly dynamic and unique physiological landscape of antibody repertoires upon antigenic challenge (e.g., vaccination). However, to what extent B cells encoding identical or similar antibody sequences (clones) are distributed across multiple lymphoid organs and how this is shaped by the strength of a humoral response remains largely unexplored. Here, we performed an in-depth systems analysis of antibody repertoires across multiple distinct lymphoid organs of immunized mice and discovered that organ-specific antibody repertoire features (i.e., germline V-gene usage and clonal expansion profiles) equilibrated upon a strong humoral response (multiple immunizations and high serum titers). This resulted in a surprisingly high degree of repertoire consolidation, characterized by highly connected and overlapping B-cell clones across multiple lymphoid organs. Finally, we revealed distinct physiological axes indicating clonal migrations and showed that antibody repertoire consolidation directly correlated with antigen specificity. Our study uncovered how a strong humoral response resulted in a more uniform but redundant physiological landscape of antibody repertoires, indicating that increases in antibody serum titers were a result of synergistic contributions from antigen-specific B-cell clones distributed across multiple lymphoid organs. Our findings provide valuable insights for the assessment and design of vaccine strategies.

Applying phylogenetic methods for species delimitation to distinguish B-cell clonal families

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

RIOT-Rapid Immunoglobulin Overview Tool-annotation of nucleotide and amino acid immunoglobulin sequences using an open germline database

Antibodies are a cornerstone of the immune system, playing a pivotal role in identifying and neutralizing infections caused by bacteria, viruses, and other pathogens. Understanding their structure, and function, can provide insights into both the body's natural defenses and the principles behind many therapeutic interventions, including vaccines and antibody-based drugs. The analysis and annotation of antibody sequences, including the identification of variable, diversity, joining, and constant genes, as well as the delineation of framework regions and complementarity-determining regions, is essential for understanding their structure and function. Currently analyzing large volumes of antibody sequences is routine in antibody discovery, requiring fast and accurate tools. While there are existing tools designed for the annotation and numbering of antibody sequences, they often have limitations such as being restricted to either nucleotide or amino acid sequences; slow execution times; or reliance on germline databases that are closed, frequently changed, or have sparse coverage for some species. Here, we present the Rapid Immunoglobulin Overview Tool (RIOT), a novel open-source solution for antibody numbering that addresses these shortcomings. RIOT handles nucleotide and amino acid sequence processing, comes integrated with an Open Germline Receptor Database, and is computationally efficient. We hope that the tool will facilitate rapid annotation of antibody sequencing outputs for the benefit of understanding antibody biology and discovering novel therapeutics.

Computational detection of antigen-specific B cell receptors following immunization

B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.

Combining mutation and recombination statistics to infer clonal families in antibody repertoires

B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B-cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution, and dynamics. We present HILARy (high-precision inference of lineages in antibody repertoires), an efficient, fast, and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and dN/dS ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.

PI3Kγ in B cells promotes antibody responses and generation of antibody-secreting cells

The differentiation of naive and memory B cells into antibody-secreting cells (ASCs) is a key feature of adaptive immunity. The requirement for phosphoinositide 3-kinase-delta (PI3Kδ) to support B cell biology has been investigated intensively; however, specific functions of the related phosphoinositide 3-kinase-gamma (PI3Kγ) complex in B lineage cells have not. In the present study, we report that PI3Kγ promotes robust antibody responses induced by T cell-dependent antigens. The inborn error of immunity caused by human deficiency in PI3Kγ results in broad humoral defects, prompting our investigation of roles for this kinase in antibody responses. Using mouse immunization models, we found that PI3Kγ functions cell intrinsically within activated B cells in a kinase activity-dependent manner to transduce signals required for the transcriptional program supporting differentiation of ASCs. Furthermore, ASC fate choice coincides with upregulation of PIK3CG expression and is impaired in the context of PI3Kγ disruption in naive B cells on in vitro CD40-/cytokine-driven activation, in memory B cells on toll-like receptor activation, or in human tonsillar organoids. Taken together, our study uncovers a fundamental role for PI3Kγ in supporting humoral immunity by integrating signals instructing commitment to the ASC fate.

nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.

RAIN: machine learning-based identification for HIV-1 bNAbs

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a straightforward computational method for the Rapid Automatic Identification of bNAbs (RAIN) based on machine learning methods. In contrast to other approaches, which use one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for the accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained and sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of distinct HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using an in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.

Protocol for fast clonal family inference and analysis from large-scale B cell receptor repertoire sequencing data

The expeditious identification and comprehensive analysis of clonal families from extensive B cell receptor (BCR) repertoire sequencing data are imperative for elucidating the intricacies of B cell immune responses. Here, we introduce a computational pipeline designed to swiftly deduce clonal families from bulk BCR heavy-chain sequencing data, accompanied by a suite of functional modules tailored to streamline post-clustering analysis. The outlined methodology encompasses guidelines for software installation, meticulous data preparation, and the systematic inference and analysis of clonal families. For complete details on the use and execution of this protocol, please refer to Wang et al.1.

Evaluating methods for B-cell clonal family assignment

The adaptive immune response relies on a diverse repertoire of B-cell receptors, each of which is characterized by a distinct sequence resulting from VDJ-recombination. Upon binding to an antigen, B-cells undergo clonal expansion and in a process unique to B-cells the overall binding affinity of the repertoire is further enhanced by somatic hypermutations in the receptor sequence. For B-cell repertoires it is therefore particularly important to analyze the dynamics of clonal expansion and patterns of somatic hypermutations and thus it is necessary to group the sequences into distinct clones to determine the number and identity of expanding clonal families responding to an antigen. Multiple methods are currently used to identify clones from sequences, employing distinct approaches to the problem. Until now there has not been an extensive comparison of how well these methods perform under the same conditions. Furthermore, since this is fundamentally a phylogenetics problem, we speculated that the mPTP method, which delimits species based on an analysis of changes in the underlying process of diversification, might perform as well as or better than existing methods. Here we conducted extensive simulations of B-cell repertoires under a diverse set of conditions and studied errors in clonal assignment and in downstream ancestral state reconstruction. We demonstrated that SCOPer-H consistently yielded superior results across parameters. However, this approach relies on a good reference assembly for the germline immunoglobulin genes which is lacking for many species. Using mPTP had lower error rates than tailor-made immunogenetic methods and should therefore be considered by researchers studying antibody evolution in non-model organisms without a reference genome.

Inferring B Cell Phylogenies from Paired H and L Chain BCR Sequences with Dowser

Abs are vital to human immune responses and are composed of genetically variable H and L chains. These structures are initially expressed as BCRs. BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated H and L chains, but advancements in single-cell sequencing now pair H and L chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired H and L chain sequences to build phylogenetic trees. We found that incorporating L chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree-building methods and persisted even when mixing bulk and single-cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some L chains were missing, such as when mixing single-cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for H and L chain gene partitions. Thus, we recommend using maximum likelihood methods with separate H and L chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.

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.

Guidelines for reproducible analysis of adaptive immune receptor repertoire sequencing data

Enhancing the reproducibility and comprehension of adaptive immune receptor repertoire sequencing (AIRR-seq) data analysis is critical for scientific progress. This study presents guidelines for reproducible AIRR-seq data analysis, and a collection of ready-to-use pipelines with comprehensive documentation. To this end, ten common pipelines were implemented using ViaFoundry, a user-friendly interface for pipeline management and automation. This is accompanied by versioned containers, documentation and archiving capabilities. The automation of pre-processing analysis steps and the ability to modify pipeline parameters according to specific research needs are emphasized. AIRR-seq data analysis is highly sensitive to varying parameters and setups; using the guidelines presented here, the ability to reproduce previously published results is demonstrated. This work promotes transparency, reproducibility, and collaboration in AIRR-seq data analysis, serving as a model for handling and documenting bioinformatics pipelines in other research domains.

RAIN: a Machine Learning-based identification for HIV-1 bNAbs

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infection. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoire is still lacking. Here, we developed a straightforward computational method for Rapid Automatic Identification of bNAbs (RAIN) based on Machine Learning methods. In contrast to other approaches using one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of novel HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.

Systematic evaluation of B-cell clonal family inference approaches

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

CD23+IgG1+ memory B cells are poised to switch to pathogenic IgE production in food allergy

Food allergy is caused by allergen-specific immunoglobulin E (IgE) antibodies, but little is known about the B cell memory of persistent IgE responses. Here, we describe, in human pediatric peanut allergy, a population of CD23+IgG1+ memory B cells arising in type 2 immune responses that contain high-affinity peanut-specific clones and generate IgE-producing cells upon activation. The frequency of CD23+IgG1+ memory B cells correlated with circulating concentrations of IgE in children with peanut allergy. A corresponding population of "type 2-marked" IgG1+ memory B cells was identified in single-cell RNA sequencing experiments. These cells differentially expressed interleukin-4 (IL-4)- and IL-13-regulated genes, such as FCER2/CD23+, IL4R, and germline IGHE, and carried highly mutated B cell receptors (BCRs). In children with high concentrations of serum peanut-specific IgE, high-affinity B cells that bind the main peanut allergen Ara h 2 mapped to the population of "type 2-marked" IgG1+ memory B cells and included clones with convergent BCRs across different individuals. Our findings indicate that CD23+IgG1+ memory B cells transcribing germline IGHE are a unique memory population containing precursors of high-affinity pathogenic IgE-producing cells that are likely to be involved in the long-term persistence of peanut allergy.

IgM marks persistent IgG anti-human leukocyte antigen antibodies in highly sensitized heart transplant patients

BACKGROUND

Sensitization to human leukocyte antigens (HLA) is a persistent problem in heart transplant (HT) candidates. We sought to characterize the anti-HLA antibody and circulating B cell repertoire in a cohort of highly sensitized HT candidates.

METHODS

We assessed immunoglobulin G (IgG) and immunoglobulin M (IgM) anti-HLA antibodies using Luminex single antigen bead assays in a cohort of 11 highly sensitized (HS; calculated panel reactive antibody ≥ 90%) and 3 mildly sensitized (MS) candidates. We also performed B cell receptor repertoire sequencing (BCRseq) in HS candidates and 33 non-candidate controls. HLA antibody strength was measured by mean fluorescence intensity (MFI).

RESULTS

We found that IgM anti-HLA antibodies were present in all HS candidates, but with a lower breadth and strength as compared to IgG. When anti-HLA IgG specificities intersected with IgM, binding strength was higher. In contrast, there were IgM but no intersecting IgG specificities for the MS group. In four candidates in the HS group, IgG anti-HLA antibodies decreased in both breadth and strength after HT, but the decrease in strength was smaller if the IgG possessed a specificity that intersected with pre-transplant IgM. BCRseq revealed larger B cell clonotypes in HS candidates but similar diversity as compared to controls.

CONCLUSIONS

IgM marks IgG anti-HLA antibodies with higher strength before HT and persistence after HT. The presence of IgM intersecting IgG for an anti-HLA specificity may be a useful approach to determine which donor HLA should be avoided for a sensitized candidate.

nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets. nf-core/airrflow is available free of charge, under the MIT license on GitHub (https://github.com/nf-core/airrflow). Detailed documentation and example results are available on the nf-core website at (https://nf-co.re/airrflow).

Anchor Clustering for million-scale immune repertoire sequencing data

BACKGROUND

The clustering of immune repertoire data is challenging due to the computational cost associated with a very large number of pairwise sequence comparisons. To overcome this limitation, we developed Anchor Clustering, an unsupervised clustering method designed to identify similar sequences from millions of antigen receptor gene sequences. First, a Point Packing algorithm is used to identify a set of maximally spaced anchor sequences. Then, the genetic distance of the remaining sequences to all anchor sequences is calculated and transformed into distance vectors. Finally, distance vectors are clustered using unsupervised clustering. This process is repeated iteratively until the resulting clusters are small enough so that pairwise distance comparisons can be performed.

RESULTS

Our results demonstrate that Anchor Clustering is faster than existing pairwise comparison clustering methods while providing similar clustering quality. With its flexible, memory-saving strategy, Anchor Clustering is capable of clustering millions of antigen receptor gene sequences in just a few minutes.

CONCLUSIONS

This method enables the meta-analysis of immune-repertoire data from different studies and could contribute to a more comprehensive understanding of the immune repertoire data space.

B cell phylogenetics in the single cell era

The widespread availability of single-cell RNA sequencing (scRNA-seq) has led to the development of new methods for understanding immune responses. Single-cell transcriptome data can now be paired with B cell receptor (BCR) sequences. However, RNA from BCRs cannot be analyzed like most other genes because BCRs are genetically diverse within individuals. In humans, BCRs are shaped through recombination followed by mutation and selection for antigen binding. As these processes co-occur with cell division, B cells can be studied using phylogenetic trees representing the mutations within a clone. B cell trees can link experimental timepoints, tissues, or cellular subtypes. Here, we review the current state and potential of how B cell phylogenetics can be combined with single-cell data to understand immune responses.

Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery

The naïve human antibody repertoire has theoretical access to an estimated > 1015 antibodies. Identifying subsets of this prohibitively large space where therapeutically relevant antibodies may be found is useful for development of these agents. It was previously demonstrated that, despite the immense sequence space, different individuals can produce the same antibodies. It was also shown that therapeutic antibodies, which typically follow seemingly unnatural development processes, can arise independently naturally. To check for biases in how the sequence space is explored, we data mined public repositories to identify 220 bioprojects with a combined seven billion reads. Of these, we created a subset of human bioprojects that we make available as the AbNGS database (https://naturalantibody.com/ngs/). AbNGS contains 135 bioprojects with four billion productive human heavy variable region sequences and 385 million unique complementarity-determining region (CDR)-H3s. We find that 270,000 (0.07% of 385 million) unique CDR-H3s are highly public in that they occur in at least five of 135 bioprojects. Of 700 unique therapeutic CDR-H3, a total of 6% has direct matches in the small set of 270,000. This observation extends to a match between CDR-H3 and V-gene call as well. Thus, the subspace of shared ('public') CDR-H3s shows utility for serving as a starting point for therapeutic antibody design.

Yclon: Ultrafast clustering of B cell clones from high-throughput immunoglobulin repertoire sequencing data

MOTIVATION

The next-generation sequencing technologies have transformed our understanding of immunoglobulin (Ig) profiles in various immune states. Clonotyping, which groups Ig sequences into B cell clones, is crucial in investigating the diversity of repertoires and changes in antigen exposure. Despite its importance, there is no widely accepted method for clonotyping, and existing methods are computationally intensive for large sequencing datasets.

RESULTS

To address this challenge, we introduce YClon, a fast and efficient approach for clonotyping Ig repertoire data. YClon uses a hierarchical clustering approach, similar to other methods, to group Ig sequences into B cell clones in a highly sensitive and specific manner. Notably, our approach outperforms other methods by being more than 30 to 5000 times faster in processing the repertoires analyzed. Astonishingly, YClon can effortlessly handle up to 2 million Ig sequences on a standard laptop computer. This enables in-depth analysis of large and numerous antibody repertoires.

AVAILABILITY AND IMPLEMENTATION

YClon was implemented in Python3 and is freely available on GitHub.

Fast clonal family inference from large-scale B cell repertoire sequencing data

Advances in high-throughput sequencing technologies have facilitated the large-scale characterization of B cell receptor (BCR) repertoires. However, the vast amount and high diversity of the BCR sequences pose challenges for efficient and biologically meaningful analysis. Here, we introduce fastBCR, an efficient computational approach for inferring B cell clonal families from massive BCR heavy chain sequences. We demonstrate that fastBCR substantially reduces the running time while ensuring high accuracy on simulated datasets with diverse numbers of B cell lineages and varying mutation rates. We apply fastBCR to real BCR sequencing data from peripheral blood samples of COVID-19 patients, showing that the inferred clonal families display disease-associated features, as well as corresponding antigen-binding specificity and affinity. Overall, our results demonstrate the advantages of fastBCR for analyzing BCR repertoire data, which will facilitate the identification of disease-associated antibodies and improve our understanding of the B cell immune response.

Inferring B cell phylogenies from paired heavy and light chain BCR sequences with Dowser

Antibodies are vital to human immune responses and are composed of genetically variable heavy and light chains. These structures are initially expressed as B cell receptors (BCRs). BCR diversity is shaped through somatic hypermutation and selection during immune responses. This evolutionary process produces B cell clones, cells that descend from a common ancestor but differ by mutations. Phylogenetic trees inferred from BCR sequences can reconstruct the history of mutations within a clone. Until recently, BCR sequencing technologies separated heavy and light chains, but advancements in single cell sequencing now pair heavy and light chains from individual cells. However, it is unclear how these separate genes should be combined to infer B cell phylogenies. In this study, we investigated strategies for using paired heavy and light chain sequences to build phylogenetic trees. We found incorporating light chains significantly improved tree accuracy and reproducibility across all methods tested. This improvement was greater than the difference between tree building methods and persisted even when mixing bulk and single cell sequencing data. However, we also found that many phylogenetic methods estimated significantly biased branch lengths when some light chains were missing, such as when mixing single cell and bulk BCR data. This bias was eliminated using maximum likelihood methods with separate branch lengths for heavy and light chain gene partitions. Thus, we recommend using maximum likelihood methods with separate heavy and light chain partitions, especially when mixing data types. We implemented these methods in the R package Dowser: https://dowser.readthedocs.io.

Influenza vaccine format mediates distinct cellular and antibody responses in human immune organoids

Highly effective vaccines elicit specific, robust, and durable adaptive immune responses. To advance informed vaccine design, it is critical that we understand the cellular dynamics underlying responses to different antigen formats. Here, we sought to understand how antigen-specific B and T cells were activated and participated in adaptive immune responses within the mucosal site. Using a human tonsil organoid model, we tracked the differentiation and kinetics of the adaptive immune response to influenza vaccine and virus modalities. Each antigen format elicited distinct B and T cell responses, including differences in their magnitude, diversity, phenotype, function, and breadth. These differences culminated in substantial changes in the corresponding antibody response. A major source of antigen format-related variability was the ability to recruit naive vs. memory B and T cells to the response. These findings have important implications for vaccine design and the generation of protective immune responses in the upper respiratory tract.

Tracking B cell responses to the SARS-CoV-2 mRNA-1273 vaccine

Protective immunity following vaccination is sustained by long-lived antibody-secreting cells and resting memory B cells (MBCs). Responses to two-dose SARS-CoV-2 mRNA-1273 vaccination are evaluated longitudinally by multimodal single-cell analysis in three infection-naïve individuals. Integrated surface protein, transcriptomics, and B cell receptor (BCR) repertoire analysis of sorted plasmablasts and spike+ (S-2P+) and S-2P- B cells reveal clonal expansion and accumulating mutations among S-2P+ cells. These cells are enriched in a cluster of immunoglobulin G-expressing MBCs and evolve along a bifurcated trajectory rooted in CXCR3+ MBCs. One branch leads to CD11c+ atypical MBCs while the other develops from CD71+ activated precursors to resting MBCs, the dominant population at month 6. Among 12 evolving S-2P+ clones, several are populated with plasmablasts at early timepoints as well as CD71+ activated and resting MBCs at later timepoints, and display intra- and/or inter-cohort BCR convergence. These relationships suggest a coordinated and predictable evolution of SARS-CoV-2 vaccine-generated MBCs.

T cell deletional tolerance restricts AQP4 but not MOG CNS autoimmunity

Aquaporin-4 (AQP4)-specific Th17 cells are thought to have a central role in neuromyelitis optica (NMO) pathogenesis. When modeling NMO, only AQP4-reactive Th17 cells from AQP4-deficient (AQP4-/-), but not wild-type (WT) mice, caused CNS autoimmunity in recipient WT mice, indicating that a tightly regulated mechanism normally ensures tolerance to AQP4. Here, we found that pathogenic AQP4 T cell epitopes bind MHC II with exceptionally high affinity. Examination of T cell receptor (TCR) α/β usage revealed that AQP4-specific T cells from AQP4-/- mice employed a distinct TCR repertoire and exhibited clonal expansion. Selective thymic AQP4 deficiency did not fully restore AQP4-reactive T cells, demonstrating that thymic negative selection alone did not account for AQP4-specific tolerance in WT mice. Indeed, AQP4-specific Th17 cells caused paralysis in recipient WT or B cell-deficient mice, which was followed by complete recovery that was associated with apoptosis of donor T cells. However, donor AQP4-reactive T cells survived and caused persistent paralysis in recipient mice deficient in both T and B cells or mice lacking T cells only. Thus, AQP4 CNS autoimmunity was limited by T cell-dependent deletion of AQP4-reactive T cells. In contrast, myelin oligodendrocyte glycoprotein (MOG)-specific T cells survived and caused sustained disease in WT mice. These findings underscore the importance of peripheral T cell deletional tolerance to AQP4, which may be relevant to understanding the balance of AQP4-reactive T cells in health and in NMO. T cell tolerance to AQP4, expressed in multiple tissues, is distinct from tolerance to MOG, an autoantigen restricted in its expression.

FLAIRR-Seq: A Method for Single-Molecule Resolution of Near Full-Length Antibody H Chain Repertoires

Current Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using short-read sequencing strategies resolve expressed Ab transcripts with limited resolution of the C region. In this article, we present the near-full-length AIRR-seq (FLAIRR-seq) method that uses targeted amplification by 5' RACE, combined with single-molecule, real-time sequencing to generate highly accurate (99.99%) human Ab H chain transcripts. FLAIRR-seq was benchmarked by comparing H chain V (IGHV), D (IGHD), and J (IGHJ) gene usage, complementarity-determining region 3 length, and somatic hypermutation to matched datasets generated with standard 5' RACE AIRR-seq using short-read sequencing and full-length isoform sequencing. Together, these data demonstrate robust FLAIRR-seq performance using RNA samples derived from PBMCs, purified B cells, and whole blood, which recapitulated results generated by commonly used methods, while additionally resolving H chain gene features not documented in IMGT at the time of submission. FLAIRR-seq data provide, for the first time, to our knowledge, simultaneous single-molecule characterization of IGHV, IGHD, IGHJ, and IGHC region genes and alleles, allele-resolved subisotype definition, and high-resolution identification of class switch recombination within a clonal lineage. In conjunction with genomic sequencing and genotyping of IGHC genes, FLAIRR-seq of the IgM and IgG repertoires from 10 individuals resulted in the identification of 32 unique IGHC alleles, 28 (87%) of which were previously uncharacterized. Together, these data demonstrate the capabilities of FLAIRR-seq to characterize IGHV, IGHD, IGHJ, and IGHC gene diversity for the most comprehensive view of bulk-expressed Ab repertoires to date.

Age-associated B cells are heterogeneous and dynamic drivers of autoimmunity in mice

Age-associated B cells (ABCs) are formed under inflammatory conditions and are considered a type of memory B cell (MBC) expressing the transcription factor T-bet. In SLE, ABC frequency is correlated with disease, and they are thought to be the source of autoantibody-secreting cells. However, in inflammatory conditions, whether autoreactive B cells can become resting MBCs is uncertain. Further, the phenotypic identity of ABCs and their relationship to other B cell subsets, such as plasmablasts, is unclear. Whether ABCs directly promote disease is untested. Here we report, in the MRL/lpr SLE model, unexpected heterogeneity among ABC-like cells for expression of the integrins CD11b and CD11c, T-bet, and memory or plasmablast markers. Transfer and labeling studies demonstrated that ABCs are dynamic, rapidly turning over. scRNA-seq identified B cell clones present in multiple subsets, revealing that ABCs can be plasmablast precursors or undergo cycles of reactivation. Deletion of CD11c-expressing B cells revealed a direct role for ABC-like B cells in lupus pathogenesis.

Remodeling of colon plasma cell repertoire within ulcerative colitis patients

Plasma cells (PCs) constitute a significant fraction of colonic mucosal cells and contribute to inflammatory infiltrates in ulcerative colitis (UC). While gut PCs secrete bacteria-targeting IgA antibodies, their role in UC pathogenesis is unknown. We performed single-cell V(D)J- and RNA-seq on sorted B cells from the colon of healthy individuals and patients with UC. A large fraction of B cell clones is shared between different colon regions, but inflammation in UC broadly disrupts this landscape, causing transcriptomic changes characterized by an increase in the unfolded protein response (UPR) and antigen presentation genes, clonal expansion, and isotype skewing from IgA1 and IgA2 to IgG1. We also directly expressed and assessed the specificity of 152 mAbs from expanded PC clones. These mAbs show low polyreactivity and autoreactivity and instead target both shared bacterial antigens and specific bacterial strains. Altogether, our results characterize the microbiome-specific colon PC response and how its disruption might contribute to inflammation in UC.

The memory of pathogenic IgE is contained within CD23 + IgG1 + memory B cells poised to switch to IgE in food allergy

Food allergy is caused by allergen-specific IgE antibodies but little is known about the B cell memory of persistent IgE responses. Here we describe in human pediatric peanut allergy CD23 + IgG1 + memory B cells arising in type 2 responses that contain peanut specific clones and generate IgE cells on activation. These 'type2-marked' IgG1 + memory B cells differentially express IL-4/IL-13 regulated genes FCER2 / CD23, IL4R , and germline IGHE and carry highly mutated B cell receptors (BCRs). Further, high affinity memory B cells specific for the main peanut allergen Ara h 2 mapped to the population of 'type2-marked' IgG1 + memory B cells and included convergent BCRs across different individuals. Our findings indicate that CD23 + IgG1 + memory B cells transcribing germline IGHE are a unique memory population containing precursors of pathogenic IgE.

One-Sentence Summary

We describe a unique population of IgG + memory B cells poised to switch to IgE that contains high affinity allergen-specific clones in peanut allergy.

BTG1 mutation yields supercompetitive B cells primed for malignant transformation

Multicellular life requires altruistic cooperation between cells. The adaptive immune system is a notable exception, wherein germinal center B cells compete vigorously for limiting positive selection signals. Studying primary human lymphomas and developing new mouse models, we found that mutations affecting BTG1 disrupt a critical immune gatekeeper mechanism that strictly limits B cell fitness during antibody affinity maturation. This mechanism converted germinal center B cells into supercompetitors that rapidly outstrip their normal counterparts. This effect was conferred by a small shift in MYC protein induction kinetics but resulted in aggressive invasive lymphomas, which in humans are linked to dire clinical outcomes. Our findings reveal a delicate evolutionary trade-off between natural selection of B cells to provide immunity and potentially dangerous features that recall the more competitive nature of unicellular organisms.

Adaptive immune responses to SARS-CoV-2 persist in the pharyngeal lymphoid tissue of children

Most studies of adaptive immunity to SARS-CoV-2 infection focus on peripheral blood, which may not fully reflect immune responses at the site of infection. Using samples from 110 children undergoing tonsillectomy and adenoidectomy during the COVID-19 pandemic, we identified 24 samples with evidence of previous SARS-CoV-2 infection, including neutralizing antibodies in serum and SARS-CoV-2-specific germinal center and memory B cells in the tonsils and adenoids. Single-cell B cell receptor (BCR) sequencing indicated virus-specific BCRs were class-switched and somatically hypermutated, with overlapping clones in the two tissues. Expanded T cell clonotypes were found in tonsils, adenoids and blood post-COVID-19, some with CDR3 sequences identical to previously reported SARS-CoV-2-reactive T cell receptors (TCRs). Pharyngeal tissues from COVID-19-convalescent children showed persistent expansion of germinal center and antiviral lymphocyte populations associated with interferon (IFN)-γ-type responses, particularly in the adenoids, and viral RNA in both tissues. Our results provide evidence for persistent tissue-specific immunity to SARS-CoV-2 in the upper respiratory tract of children after infection.

Inference of B cell clonal families using heavy/light chain pairing information

Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the light chain. The light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package.

A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data

The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clonal lineages. A high number of clonal lineages in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity reflect antigen selective pressure. Identifying clonal lineages is fundamental to many repertoire studies, including repertoire comparisons, clonal tracking, and statistical analysis. Several methods have been developed to group sequences from high-throughput B cell repertoire data. Current methods use clustering algorithms to group clonally-related sequences based on their similarities or distances. Such approaches create groups by optimizing a single objective that typically minimizes intra-clonal distances. However, optimizing several objective functions can be advantageous and boost the algorithm convergence rate. Here we propose MobiLLe, a new method based on multi-objective clustering. Our approach requires V(D)J annotations to obtain the initial groups and iteratively applies two objective functions that optimize cohesion and separation within clonal lineages simultaneously. We show that our method greatly improves clonal lineage grouping on simulated benchmarks with varied mutation rates compared to other tools. When applied to experimental repertoires generated from high-throughput sequencing, its clustering results are comparable to the most performing tools and can reproduce the results of previous publications. The method based on multi-objective clustering can accurately identify clonally-related antibody sequences and presents the lowest running time among state-of-art tools. All these features constitute an attractive option for repertoire analysis, particularly in the clinical context. MobiLLe can potentially help unravel the mechanisms involved in developing and evolving B cell malignancies.

High-throughput sequencing can produce a large set of sequences and has profoundly changed our ability to study immune repertoires, particularly B cell receptor sequences. An important application is the analysis of the clonal lineage composition of B cell populations; it is the starting point of many immune repertoire studies, for instance, to differentiate between healthy individuals and those with lymphoid malignancies or other diseases. Several computational methods have been developed to identify clonal lineages from a set of B cell receptor sequences. Most of them apply clustering algorithms and optimize a single objective function that typically minimizes intra-clonal distances. However, optimizing several objective functions in parallel can benefit and increase the clustering performance and efficiency. We propose MobiLLe, the first multi-objective clonal lineage grouping method, which simultaneously optimizes two objective functions for minimizing intra-clonal diversity and maximizing inter-clonal differences. Our approach greatly improved clonal grouping on simulated benchmarks and performed comparably to the most powerful and recent methods on experimental samples. MobiLLe is computationally more efficient than existing tools and does not require any training process or hyper-parameter optimization. It can easily manage large-scale experimental repertoires, providing useful plots to help researchers detect clonally-related sequences in high-throughput B cell repertoire data.

Inactivated whole influenza virus particle vaccines induce neutralizing antibodies with an increase in immunoglobulin gene subclones of B-lymphocytes in cynomolgus macaques

The All-Japan Influenza Vaccine Study Group has been developing a more effective vaccine than the current split vaccines for seasonal influenza virus infection. In the present study, the efficacy of formalin- and/or β-propiolactone-inactivated whole virus particle vaccines for seasonal influenza was compared to that of the current ether-treated split vaccines in a nonhuman primate model. The monovalent whole virus particle vaccines or split vaccines of influenza A virus (H1N1) and influenza B virus (Victoria lineage) were injected subcutaneously into naïve cynomolgus macaques twice. The whole virus particle vaccines induced higher titers of neutralizing antibodies against H1N1 influenza A virus and influenza B virus in the plasma of macaques than did the split vaccines. At challenge with H1N1 influenza A virus or influenza B virus, the virus titers in nasal swabs and the increases in body temperatures were lower in the macaques immunized with the whole virus particle vaccine than in those immunized with the split vaccine. Repertoire analyses of immunoglobulin heavy chain genes demonstrated that the number of B-lymphocyte subclones was increased in macaques after the 1st vaccination with the whole virus particle vaccine, but not with the split vaccine, indicating that the whole virus particle vaccine induced the activation of vaccine antigen-specific B-lymphocytes more vigorously than did the split vaccine at priming. Thus, the present findings suggest that the superior antibody induction ability of the whole virus particle vaccine as compared to the split vaccine is attributable to its stimulatory properties on the subclonal differentiation of antigen-specific B-lymphocytes.

Ontogeny of the B Cell Receptor Repertoire and Microbiome in Mice

The immune system matures throughout childhood to achieve full functionality in protecting our bodies against threats. The immune system has a strong reciprocal symbiosis with the host bacterial population and the two systems co-develop, shaping each other. Despite their fundamental role in health physiology, the ontogeny of these systems is poorly characterized. In this study, we investigated the development of the BCR repertoire by analyzing high-throughput sequencing of their receptors in several time points of young C57BL/6J mice. In parallel, we explored the development of the gut microbiome. We discovered that the gut IgA repertoires change from birth to adolescence, including an increase in CDR3 lengths and somatic hypermutation levels. This contrasts with the spleen IgM repertoires that remain stable and distinct from the IgA repertoires in the gut. We also discovered that large clones that germinate in the gut are initially confined to a specific gut compartment, then expand to nearby compartments and later on expand also to the spleen and remain there. Finally, we explored the associations between diversity indices of the B cell repertoires and the microbiome, as well as associations between bacterial and BCR clusters. Our results shed light on the ontogeny of the adaptive immune system and the microbiome, providing a baseline for future research.

Lupus Susceptibility Loci Predispose Mice to Clonal Lymphocytic Responses and Myeloid Expansion

Lupus susceptibility results from the combined effects of numerous genetic loci, but the contribution of these loci to disease pathogenesis has been difficult to study due to the large cellular heterogeneity of the autoimmune immune response. We performed single-cell RNA, BCR, and TCR sequencing of splenocytes from mice with multiple polymorphic lupus susceptibility loci. We not only observed lymphocyte and myeloid expansion, but we also characterized changes in subset frequencies and gene expression, such as decreased CD8 and marginal zone B cells and increased Fcrl5- and Cd5l-expressing macrophages. Clonotypic analyses revealed expansion of B and CD4 clones, and TCR repertoires from lupus-prone mice were distinguishable by algorithmic specificity prediction and unsupervised machine learning classification. Myeloid differential gene expression, metabolism, and altered ligand-receptor interaction were associated with decreased Ag presentation. This dataset provides novel mechanistic insight into the pathophysiology of a spontaneous model of lupus, highlighting potential therapeutic targets for autoantibody-mediated disease.

Robust, persistent adaptive immune responses to SARS-CoV-2 in the oropharyngeal lymphoid tissue of children

SARS-CoV-2 infection triggers adaptive immune responses from both T and B cells. However, most studies focus on peripheral blood, which may not fully reflect immune responses in lymphoid tissues at the site of infection. To evaluate both local and systemic adaptive immune responses to SARS-CoV-2, we collected peripheral blood, tonsils, and adenoids from 110 children undergoing tonsillectomy/adenoidectomy during the COVID-19 pandemic and found 24 with evidence of prior SARS-CoV-2 infection, including detectable neutralizing antibodies against multiple viral variants. We identified SARS-CoV-2-specific germinal center (GC) and memory B cells; single cell BCR sequencing showed that these virus-specific B cells were class-switched and somatically hypermutated, with overlapping clones in the adenoids and tonsils. Oropharyngeal tissues from COVID-19-convalescent children showed persistent expansion of GC and anti-viral lymphocyte populations associated with an IFN-γ-type response, with particularly prominent changes in the adenoids, as well as evidence of persistent viral RNA in both tonsil and adenoid tissues of many participants. Our results show robust, tissue-specific adaptive immune responses to SARS-CoV-2 in the upper respiratory tract of children weeks to months after acute infection, providing evidence of persistent localized immunity to this respiratory virus.

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis

Adaptive immune receptor repertoires (AIRRs) are rich with information that can be mined for insights into the workings of the immune system. Gene usage, CDR3 properties, clonal lineage structure, and sequence diversity are all capable of revealing the dynamic immune response to perturbation by disease, vaccination, or other interventions. Here we focus on a conceptual introduction to the many aspects of repertoire analysis and orient the reader toward the uses and advantages of each. Along the way, we note some of the many software tools that have been developed for these investigations and link the ideas discussed to chapters on methods provided elsewhere in this volume.

MOUSSE: Multi-Omics Using Subject-Specific SignaturEs

Simple Summary

Modern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient classification is to integrate multiple data types for the same subjects to increase the chance of identifying meaningful phenotype groups. However, these methodologies are still in their infancy, with their performance varying widely depending on the biological conditions analyzed. We developed MOUSSE, a new unsupervised and normalization-free tool for multi-omics integration able to maintain good clustering performance across a wide range of omics data. We verified its efficiency in clustering patients based on survival for ten different cancer types. The results we obtained show a higher average score in classification performance than ten other state-of-the-art algorithms. We have further validated the method by identifying a list of biological features potentially involved in patient survival, finding a high degree of concordance with the literature.

Abstract

High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.

Single-cell immunophenotyping of the skin lesion erythema migrans identifies IgM memory B cells

The skin lesion erythema migrans (EM) is an initial sign of the Ixodes tick-transmitted Borreliella spirochetal infection known as Lyme disease. T cells and innate immune cells have previously been shown to predominate the EM lesion and promote the reaction. Despite the established importance of B cells and antibodies in preventing infection, the role of B cells in the skin immune response to Borreliella is unknown. Here, we used single-cell RNA-Seq in conjunction with B cell receptor (BCR) sequencing to immunophenotype EM lesions and their associated B cells and BCR repertoires. We found that B cells were more abundant in EM in comparison with autologous uninvolved skin; many were clonally expanded and had circulating relatives. EM-associated B cells upregulated the expression of MHC class II genes and exhibited preferential IgM isotype usage. A subset also exhibited low levels of somatic hypermutation despite a gene expression profile consistent with memory B cells. Our study demonstrates that single-cell gene expression with paired BCR sequencing can be used to interrogate the sparse B cell populations in human skin and reveals that B cells in the skin infection site in early Lyme disease expressed a phenotype consistent with local antigen presentation and antibody production.

B cell genomics behind cross-neutralization of SARS-CoV-2 variants and SARS-CoV

Monoclonal antibodies (mAbs) are a focus in vaccine and therapeutic design to counteract severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Here, we combined B cell sorting with single-cell VDJ and RNA sequencing (RNA-seq) and mAb structures to characterize B cell responses against SARS-CoV-2. We show that the SARS-CoV-2-specific B cell repertoire consists of transcriptionally distinct B cell populations with cells producing potently neutralizing antibodies (nAbs) localized in two clusters that resemble memory and activated B cells. Cryo-electron microscopy structures of selected nAbs from these two clusters complexed with SARS-CoV-2 spike trimers show recognition of various receptor-binding domain (RBD) epitopes. One of these mAbs, BG10-19, locks the spike trimer in a closed conformation to potently neutralize SARS-CoV-2, the recently arising mutants B.1.1.7 and B.1.351, and SARS-CoV and cross-reacts with heterologous RBDs. Together, our results characterize transcriptional differences among SARS-CoV-2-specific B cells and uncover cross-neutralizing Ab targets that will inform immunogen and therapeutic design against coronaviruses.

Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing

The world is facing a pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Adaptive immune responses are essential for SARS-CoV-2 virus clearance. Although a large body of studies have been conducted to investigate the immune mechanism in COVID-19 patients, we still lack a comprehensive understanding of the BCR repertoire in patients. In this study, we used the single-cell V(D)J sequencing to characterize the BCR repertoire across convalescent COVID-19 patients. We observed that the BCR diversity was significantly reduced in disease compared with healthy controls. And BCRs tend to skew toward different V gene segments in COVID-19 and healthy controls. The CDR3 sequences of heavy chain in clonal BCRs in patients were more convergent than that in healthy controls. In addition, we discovered increased IgG and IgA isotypes in the disease, including IgG1, IgG3 and IgA1. In all clonal BCRs, IgG isotypes had the most frequent class switch recombination events and the highest somatic hypermutation rate, especially IgG3. Moreover, we found that an IgG3 cluster from different clonal groups had the same IGHV, IGHJ and CDR3 sequences (IGHV4-4-CARLANTNQFYDSSSYLNAMDVW-IGHJ6). Overall, our study provides a comprehensive characterization of the BCR repertoire in COVID-19 patients, which contributes to the understanding of the mechanism for the immune response to SARS-CoV-2 infection.

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.

Population genomics reveals the expansion of highly inbred Plasmodium vivax lineages in the main malaria hotspot of Brazil

BACKGROUND

Plasmodium vivax is a neglected human malaria parasite that causes significant morbidity in the Americas, the Middle East, Asia, and the Western Pacific. Population genomic approaches remain little explored to map local and regional transmission pathways of P. vivax across the main endemic sites in the Americas, where great progress has been made towards malaria elimination over the past decades.

METHODOLOGY/PRINCIPAL FINDINGS

We analyze 38 patient-derived P. vivax genome sequences from Mâncio Lima (ML)-the Amazonian malaria hotspot next to the Brazil-Peru border-and 24 sequences from two other sites in Acre State, Brazil, a country that contributes 23% of malaria cases in the Americas. We show that the P. vivax population of ML is genetically diverse (π = 4.7 × 10-4), with a high polymorphism particularly in genes encoding proteins putatively involved in red blood cell invasion. Paradoxically, however, parasites display strong genome-wide linkage disequilibrium, being fragmented into discrete lineages that are remarkably stable across time and space, with only occasional recombination between them. Using identity-by-descent approaches, we identified a large cluster of closely related sequences that comprises 16 of 38 genomes sampled in ML over 26 months. Importantly, we found significant ancestry sharing between parasites at a large geographic distance, consistent with substantial gene flow between regional P. vivax populations.

CONCLUSIONS/SIGNIFICANCE

We have characterized the sustained expansion of highly inbred P. vivax lineages in a malaria hotspot that can seed regional transmission. Potential source populations in hotspots represent a priority target for malaria elimination in the Amazon.

Single-cell repertoire tracing identifies rituximab-resistant B cells during myasthenia gravis relapses

Rituximab, a B cell-depleting therapy, is indicated for treating a growing number of autoantibody-mediated autoimmune disorders. However, relapses can occur after treatment, and autoantibody-producing B cell subsets may be found during relapses. It is not understood whether these autoantibody-producing B cell subsets emerge from the failed depletion of preexisting B cells or are generated de novo. To further define the mechanisms that cause postrituximab relapse, we studied patients with autoantibody-mediated muscle-specific kinase (MuSK) myasthenia gravis (MG) who relapsed after treatment. We carried out single-cell transcriptional and B cell receptor profiling on longitudinal B cell samples. We identified clones present before therapy that persisted during relapse. Persistent B cell clones included both antibody-secreting cells and memory B cells characterized by gene expression signatures associated with B cell survival. A subset of persistent antibody-secreting cells and memory B cells were specific for the MuSK autoantigen. These results demonstrate that rituximab is not fully effective at eliminating autoantibody-producing B cells and provide a mechanistic understanding of postrituximab relapse in MuSK MG.