sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data

Reduced response to SARS-CoV-2 vaccination is associated with impaired immunoglobulin class switch recombination in SLE patients

INTRODUCTION

Systemic lupus erythematosus (SLE) patients exhibit B-cell abnormalities. Although there are concerns about reduced antibody responses to SARS-CoV-2 vaccines, detailed data on B-cell-specific responses in SLE remain scarce. Understanding the responsiveness to novel vaccine antigens, and boosters number, is important to avoid unnecessarily prolonged isolation of immunocompromised individuals. We assessed humoral and antigen-specific B-cell subset responses, including changes in isotype switching, prior to and after several doses of SARS-CoV-2 vaccines.

METHODS

Blood samples were obtained prior to and after SARS-CoV-2 vaccination from cross-sectional and longitudinal cohorts of previously uninfected patients with SLE (n = 93). Healthy participants receiving SARS-CoV-2 vaccines were recruited as controls (n = 135). We measured serum antibody titres, their neutralizing capacity, and vaccine-specific memory B-cell subsets.

RESULTS

Impaired IgG, IgA, and neutralizing responses against the original and various SARS-CoV-2 variants were observed following two doses of vaccine in SLE patients. Follow-up booster doses increased humoral responses compared to baseline, but they remained lower, with poorer neutralisation capacity against most strains, compared to healthy individuals after three or more doses. Analysis of memory B-cell subsets in SLE patients revealed an increase of SARS-CoV-2-specific isotype unswitched IgM+ over SARS-CoV-2-specific isotype switched IgG+/IgA+ memory B-cells compared to healthy individuals. Culturing healthy naive B-cells with high levels of IFNα, a hallmark of SLE pathogenesis, prevented B-cells from switching to IgG under IgG-polarizing conditions.

CONCLUSION

SLE patients' protection against SARS-CoV-2 is overall impaired compared to healthy individuals and is associated with a class switch defect possibly due to chronic exposure of B-cells to IFNα.

Isotype-aware inference of B cell clonal lineage trees from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the micro-evolutionary processes of B cells during an adaptive immune response, capturing features of somatic hypermutation (SHM) and class switch recombination (CSR). Existing phylogenetic approaches for reconstructing B cell evolution have primarily focused on the SHM process alone. Here, we present tree inference of B cell clonal lineages (TRIBAL), an algorithm designed to optimally reconstruct the evolutionary history of B cell clonal lineages undergoing both SHM and CSR from scRNA-seq data. Through simulations, we demonstrate that TRIBAL produces more comprehensive and accurate B cell lineage trees compared to existing methods. Using real-world datasets, TRIBAL successfully recapitulates expected biological trends in a model affinity maturation system while reconstructing evolutionary histories with more parsimonious class switching than state-of-the-art methods. Thus, TRIBAL significantly improves B cell lineage tracing, useful for modeling vaccine responses, disease progression, and the identification of therapeutic antibodies.

Advances and challenges in investigating B-cells via single-cell transcriptomics

Single-cell RNA sequencing (scRNAseq) and Variable, Diversity, Joining (VDJ) profiling have improved our understanding of B-cells. Recent scRNAseq-based approaches have led to the discovery of intermediate B-cell states, including preplasma cells and pregerminal centre B-cells, as well as unveiling protective roles for B-cells within tertiary lymphoid structures in respiratory infections and cancers. These studies have improved our understanding of transcriptional and epigenetic control of B-cell development and of atypical and memory B-cell differentiation. Advancements in temporal profiling in parallel with transcriptomic and VDJ sequencing have consolidated our understanding of the trajectory of B-cell clones over the course of infection and vaccination. Challenges remain in studying B-cell states across tissues in humans, in relating spatial location with B-cell phenotype and function, in examining antibody isotype switching events, and in unequivocal determination of clonal relationships. Nevertheless, ongoing multiomic assessments and studies of cellular interactions within tissues promise new avenues for improving humoral immunity and combatting autoimmune conditions.

Predicting class switch recombination in B-cells from antibody repertoire data

Statistical and machine learning methods have proved useful in many areas of immunology. In this paper, we address for the first time the problem of predicting the occurrence of class switch recombination (CSR) in B-cells, a problem of interest in understanding antibody response under immunological challenges. We propose a framework to analyze antibody repertoire data, based on clonal (CG) group representation in a way that allows us to predict CSR events using CG level features as input. We assess and compare the performance of several predicting models (logistic regression, LASSO logistic regression, random forest, and support vector machine) in carrying out this task. The proposed approach can obtain an unweighted average recall of71 % $71\%$ with models based on variable region descriptors and measures of CG diversity during an immune challenge and, most notably, before an immune challenge.

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.

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.