BRILIA: Integrated Tool for High-Throughput Annotation and Lineage Tree Assembly of B-Cell Repertoires

Low-dose dengue virus 3 human challenge model: a phase 1 open-label study

Dengue human infection models present an opportunity to explore the potential of a vaccine, anti-viral or immuno-compound for clinical benefit in a controlled setting. Here we report the outcome of a phase 1 open-label assessment of a low-dose dengue virus 3 (DENV-3) challenge model (NCT04298138), in which nine participants received a subcutaneous inoculation with 0.5 ml of a 1.4 × 103 plaque-forming unit per ml suspension of the attenuated DENV-3 strain CH53489. The primary and secondary endpoints of the study were to assess the safety of this DENV-3 strain in healthy flavivirus-seronegative individuals. All participants developed RNAaemia within 7 days after inoculation with peak titre ranging from 3.13 × 104 to 7.02 × 108 genome equivalents per ml. Solicited symptoms such as fever and rash, clinical laboratory abnormalities such as lymphopenia and thrombocytopenia, and self-reported symptoms such as myalgia were consistent with mild-to-moderate dengue in all volunteers. DENV-3-specific seroconversion and memory T cell responses were observed within 14 days after inoculation as assessed by enzyme-linked immunosorbent assay and interferon-gamma-based enzyme-linked immunospot. RNA sequencing and serum cytokine analysis revealed anti-viral responses that overlapped with the period of viraemia. The magnitude and frequency of clinical and immunologic endpoints correlated with an individual's peak viral titre.

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.

Reconstructing B cell lineage trees with minimum spanning tree and genotype abundances

B cell receptor (BCR) genes exposed to an antigen undergo somatic hypermutations and Darwinian antigen selection, generating a large BCR-antibody diversity. This process, known as B cell affinity maturation, increases antibody affinity, forming a specific B cell lineage that includes the unmutated ancestor and mutated variants. In a B cell lineage, cells with a higher antigen affinity will undergo clonal expansion, while those with a lower affinity will not proliferate and probably be eliminated. Therefore, cellular (genotype) abundance provides a valuable perspective on the ongoing evolutionary process. Phylogenetic tree inference is often used to reconstruct B cell lineage trees and represents the evolutionary dynamic of BCR affinity maturation. However, such methods should process B-cell population data derived from experimental sampling that might contain different cellular abundances. There are a few phylogenetic methods for tracing the evolutionary events occurring in B cell lineages; best-performing solutions are time-demanding and restricted to analysing a reduced number of sequences, while time-efficient methods do not consider cellular abundances. We propose ClonalTree, a low-complexity and accurate approach to construct B-cell lineage trees that incorporates genotype abundances into minimum spanning tree (MST) algorithms. Using both simulated and experimental data, we demonstrate that ClonalTree outperforms MST-based algorithms and achieves a comparable performance to a method that explores tree-generating space exhaustively. Furthermore, ClonalTree has a lower running time, being more convenient for building B-cell lineage trees from high-throughput BCR sequencing data, mainly in biomedical applications, where a lower computational time is appreciable. It is hundreds to thousands of times faster than exhaustive approaches, enabling the analysis of a large set of sequences within minutes or seconds and without loss of accuracy. The source code is freely available at github.com/julibinho/ClonalTree.

Immunoglobulin VDJ repertoires reveal hallmarks of germinal centers in unique cell clusters isolated from zebrafish (Danio rerio) lymphoid tissues

DNA mutagenesis during antibody affinity maturation has potentially oncogenic or autoimmune outcomes if not tightly controlled as it is in mammalian germinal centers. Cold blooded vertebrates lack germinal centers, yet have a functional Ig gene mutator enzyme, Aicda. In fish there are clusters of Aicda⁺ cells encircled by pigmented 'melano-macrophages' and we test the hypothesis that these clusters are functionally analogous to germinal centers. Sequenced IgH VDJ repertoire libraries from individual isolated clusters showed evidence of B-cell clonal expansion and VDJ somatic hypermutation. Construction of Ig clonal lineage trees revealed that unlike surrounding lymphoid tissue, each cluster is dominated by a few B-cell VDJ clonotypes having hundreds of mutated variants. Recruitment of B-cells to the clusters appears to be ongoing, as there are additional Ig clones having smaller lineages. Finally, we show evidence for positive selection for replacement mutations in regions encoding the antigen contact loops, but not in the framework regions, consistent with functional antibody modification. Melano-macrophages appear to trap the Ag used for post-mutation B-cell selection, performing a role analogous to the follicular dendritic cells of mammalian germinal centers. These findings provide insights into the evolution of the affinity maturation process, the improvement of fish vaccines and possibly also the workings of atypical ectopic germinal centers generated in several human diseases.

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

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

Antibody repertoire sequencing analysis

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

Dynamics of B cell repertoires and emergence of cross-reactive responses in patients with different severities of COVID-19

Individuals with the 2019 coronavirus disease (COVID-19) show varying severity of the disease, ranging from asymptomatic to requiring intensive care. Although monoclonal antibodies specific to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified, we still lack an understanding of the overall landscape of B cell receptor (BCR) repertoires in individuals with COVID-19. We use high-throughput sequencing of bulk and plasma B cells collected at multiple time points during infection to characterize signatures of the B cell response to SARS-CoV-2 in 19 individuals. Using principled statistical approaches, we associate differential features of BCRs with different disease severity. We identify 38 significantly expanded clonal lineages shared among individuals as candidates for responses specific to SARS-CoV-2. Using single-cell sequencing, we verify the reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identify the natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in some individuals. Our results provide insights important for development of rational therapies and vaccines against COVID-19.

Dynamics of B-cell repertoires and emergence of cross-reactive responses in COVID-19 patients with different disease severity

COVID-19 patients show varying severity of the disease ranging from asymptomatic to requiring intensive care. Although a number of SARS-CoV-2 specific monoclonal antibodies have been identified, we still lack an understanding of the overall landscape of B-cell receptor (BCR) repertoires in COVID-19 patients. Here, we used high-throughput sequencing of bulk and plasma B-cells collected over multiple time points during infection to characterize signatures of B-cell response to SARS-CoV-2 in 19 patients. Using principled statistical approaches, we determined differential features of BCRs associated with different disease severity. We identified 38 significantly expanded clonal lineages shared among patients as candidates for specific responses to SARS-CoV-2. Using single-cell sequencing, we verified reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identified natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in a number of patients. Our results provide important insights for development of rational therapies and vaccines against COVID-19.

Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification

High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in these large datasets, in particular the identification of therapeutic antibodies (Abs) or those associated with disease exposure and protection. Here, we describe our efforts to use artificial intelligence (AI)-based image-analyses for prospective classification of Abs based solely on sequence information. We hypothesized that Abs recognizing the same part of an antigen share a limited set of features at the binding interface, and that the binding site regions of these Abs share share common structure and physicochemical property patterns that can serve as a "fingerprint" to recognize uncharacterized Abs. We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. We evaluated this approach using Ab sequences derived from human HIV and Ebola viral infections to differentiate between two Abs, Abs belonging to specific B-cell family lineages, and Abs with different epitope preferences. In addition, we explored a different type of DNN method to detect one class of Abs from a larger pool of Abs. Testing on Ab sets that had been kept aside during model training, we achieved average prediction accuracies ranging from 71-96% depending on the complexity of the classification task. The high level of accuracies reached during these classification tests suggests that the DNN models were able to learn a series of structural patterns shared by Abs belonging to the same class. The developed methodology provides a means to apply AI-based image recognition techniques to analyze high-throughput B-cell sequencing datasets (repertoires) for Ab classification.

SARS-CoV-2-specific antibody rearrangements in prepandemic immune repertoires of risk cohorts and patients with COVID-19

A considerable fraction of B cells recognize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with germline-encoded elements of their B cell receptor, resulting in the production of neutralizing and nonneutralizing antibodies. We found that antibody sequences from different discovery cohorts shared biochemical properties and could be retrieved across validation cohorts, confirming the stereotyped character of this naive response in coronavirus disease 2019 (COVID-19). While neutralizing antibody sequences were found independently of disease severity, in line with serological data, individual nonneutralizing antibody sequences were associated with fatal clinical courses, suggesting detrimental effects of these antibodies. We mined 200 immune repertoires from healthy individuals and 500 repertoires from patients with blood or solid cancers - all acquired prior to the pandemic - for SARS-CoV-2 antibody sequences. While the largely unmutated B cell rearrangements occurred in a substantial fraction of immune repertoires from young and healthy individuals, these sequences were less likely to be found in individuals over 60 years of age and in those with cancer. This reflects B cell repertoire restriction in aging and cancer, and may to a certain extent explain the different clinical courses of COVID-19 observed in these risk groups. Future studies will have to address if this stereotyped B cell response to SARS-CoV-2 emerging from unmutated antibody rearrangements will create long-lived memory.

Transcriptional and clonal characterization of B cell plasmablast diversity following primary and secondary natural DENV infection

Antibody-mediated humoral immunity is thought to play a central role in mediating the immunopathogenesis of acute DENV infection, but limited data are available on the diversity, specificity, and functionality of the antibody response at the molecular level elicited by primary or secondary DENV infection. In order to close this functional gap in our understanding of DENV-specific humoral immunity, we utilized high-throughput single cell RNA sequencing to investigate B cells circulating in both primary and secondary natural DENV infections. We captured full-length paired immunoglobulin receptor sequence data from 9,027 B cells from a total of 6 subjects, including 2,717 plasmablasts. In addition to IgG and IgM class-switched cells, we unexpectedly found a high proportion of the DENV-elicited plasmablasts expressing IgA, principally in individuals with primary DENV infections. These IgA class-switched cells were extensively hypermutated even in individuals with a serologically confirmed primary DENV infection. Utilizing a combination of conventional biochemical assays and high-throughput shotgun mutagenesis, we determined that DENV-reactive IgA class-switched antibodies represent a significant fraction of DENV-reactive Igs generated in response to DENV infection, and that they exhibit a comparable epitope specificity to DENV-reactive IgG antibodies. These results provide insight into the molecular-level diversity of DENV-elicited humoral immunity and identify a heretofore unappreciated IgA plasmablast response to DENV infection.

Bordetella pertussis Whole Cell Immunization, Unlike Acellular Immunization, Mimics Naïve Infection by Driving Hematopoietic Stem and Progenitor Cell Expansion in Mice

Hematopoietic stem and progenitor cell (HSPC) compartments are altered to direct immune responses to infection. Their roles during immunization are not well-described. To elucidate mechanisms for waning immunity following immunization with acellular vaccines (ACVs) against Bordetella pertussis (Bp), we tested the hypothesis that immunization with Bp ACVs and whole cell vaccines (WCVs) differ in directing the HSPC characteristics and immune cell development patterns that ultimately contribute to the types and quantities of cells produced to fight infection. Our data demonstrate that compared to control and ACV-immunized CD-1 mice, immunization with an efficacious WCV drives expansion of hematopoietic multipotent progenitor cells (MPPs), increases circulating white blood cells (WBCs), and alters the size and composition of lymphoid organs. In addition to MPPs, common lymphoid progenitor (CLP) proportions increase in the bone marrow of WCV-immunized mice, while B220⁺ cell proportions decrease. Upon subsequent infection, increases in maturing B cell populations are striking in WCV-immunized mice. RNAseq analyses of HSPCs revealed that WCV and ACV-immunized mice vastly differ in developing VDJ gene segment diversity. Moreover, gene set enrichment analyses demonstrate WCV-immunized mice exhibit unique gene signatures that suggest roles for interferon (IFN) induced gene expression. Also observed in naïve infection, these IFN stimulated gene (ISG) signatures point toward roles in cell survival, cell cycle, autophagy, and antigen processing and presentation. Taken together, these findings underscore the impact of vaccine antigen and adjuvant content on skewing and/or priming HSPC populations for immune response.

The Bayesian optimist's guide to adaptive immune receptor repertoire analysis

Probabilistic modeling is fundamental to the statistical analysis of complex data. In addition to forming a coherent description of the data-generating process, probabilistic models enable parameter inference about given datasets. This procedure is well developed in the Bayesian perspective, in which one infers probability distributions describing to what extent various possible parameters agree with the data. In this paper, we motivate and review probabilistic modeling for adaptive immune receptor repertoire data then describe progress and prospects for future work, from germline haplotyping to adaptive immune system deployment across tissues. The relevant quantities in immune sequence analysis include not only continuous parameters such as gene use frequency but also discrete objects such as B-cell clusters and lineages. Throughout this review, we unravel the many opportunities for probabilistic modeling in adaptive immune receptor analysis, including settings for which the Bayesian approach holds substantial promise (especially if one is optimistic about new computational methods). From our perspective, the greatest prospects for progress in probabilistic modeling for repertoires concern ancestral sequence estimation for B-cell receptor lineages, including uncertainty from germline genotype, rearrangement, and lineage development.

Computational Evaluation of B-Cell Clone Sizes in Bulk Populations

B cell clones expand and contract during adaptive immune responses and can persist or grow uncontrollably in lymphoproliferative disorders. One way to monitor and track B cell clones is to perform large-scale sampling of bulk cell populations, amplifying, and sequencing antibody gene rearrangements by next-generation sequencing (NGS). Here, we describe a series of computational approaches for estimating B cell clone size in NGS immune repertoire profiling data of antibody heavy chain gene rearrangements. We define three different measures of B cell clone size-copy numbers, instances, and unique sequences-and show how these measures can be used to rank clones, analyze their diversity, and study their distribution within and between individuals. We provide a detailed, step-by-step procedure for performing these analyses using two different data sets of spleen samples from human organ donors. In the first data set, 19 independently generated biological replicates from a single individual are analyzed for B cell clone size, diversity and sampling sufficiency for clonal overlap analysis. In the second data set, B cell clones are compared in eight different organ donors. We comment upon frequently encountered pitfalls and offer practical advice with alternative approaches. Overall, we provide a series of pragmatic analytical approaches and show how different clone size measures can be used to study the clonal landscape in bulk B cell immune repertoire profiling data.