Summary
Class-switch recombination (CSR) is a cornerstone of B cell maturation and activation, enabling antibody isotype diversification for optimal humoral response. Researchers recently introduced sciCSR (single-cell inference of class-switch recombination), a computational pipeline dedicated to analyzing CSR events and B cell state transitions from single-cell RNA-sequencing (scRNA-seq) data. Validated on both simulated and real datasets, sciCSR leverages B cell-specific features like sterile transcripts and BCR repertoire information to infer cell transition probabilities via CellRank. Subsequently, transition path theory (TPT) is applied to analyze the dynamics and directionality of CSR within and between identified cell clusters. Through application to time-course scRNA-seq data from SARS-CoV-2 vaccination, sciCSR demonstrates accurate prediction of B cell repertoire isotype distribution at later time points, highlighting its potential for studying B cell maturation dynamics and antibody evolution.
Challenges in B Cell Transition Analysis
B-cells are an essential component of humoral immunity, in which antibodies produced by activated B-cells protect extracellular spaces by causing the destruction of extracellular microorganisms and preventing the spread of intracellular infections. Class switch recombination (CSR) is an essential component in B-cell activation and maturation, resulting in the change from IgM and IgD expression by naive B-cells to an expansion of IgG, IgA, and IgE immunoglobulins . The AID/ APOBEC enzymes play a critical role in CSR, and the associated somatic hypermutation (SHM) process, which initiate end-joining type of DNA recombination events and NHEJ DNA repair pathways, respectively. Both CSR and SHM are essential for humoral immunity because they allow the immune system to generate a diverse repertoire of antibodies with distinct functionality in order to battle different types of pathogens.
The capturing of cell-state transitions during B cell maturation and activation necessitates an evaluation of mechanisms driven by CSR and SHM. . In recent years, there has been a proliferation of computational methodologies aimed at delineating cell state transitions using scRNA-seq datasets that can be categorized into ‘pseudotime’ ordering and ‘RNA velocity.’ Pseudotime ordering leverages transcriptional similarity networks to depict cell trajectories, whereas, RNA velocity quantifies cell transition dynamics based on splicing kinetics. It is important to note, however, that conventional RNA velocity and pseudotime methodologies don’t take into consideration processes driven by CSR and SHM. CSR modifies the constant region of the B cell receptor (BCR) to enable its functionality in diverse immune challenges and tissue environments. AID identifies and acts on specific genomic switch regions upstream of each constant gene, thereby, catalyzing deamination and initiating DNA recombination. The resulting sterile transcripts, devoid of functional immunoglobulin heavy chain segments, indicate a predisposition to CSR events, providing potential transcriptomic footprints detectable in single cell RNA sequencing (scRNA-seq). Additionally, the BCR sequence substitutions would be indicative of SHM events that can be extracted via the single cell BCR sequencing (scBCR-seq) data. The extraction and integration of CSR- and SHM-related signals from scRNA-seq data into an analytical framework, holds the potential to augment the accuracy of B cell maturation dynamics reconstruction. This integrative strategy promises a more comprehensive elucidation of B cell maturation processes.
In a recent publication by Ng et al . (2024), the Fraternali lab introduce the Scissor single-cell inference of class-switch recombination (sciCSR) pipeline which analyzes CSR events to characterize B-cell maturation dynamics from scRNA-seq experiments.
sciCSR: Bridging the Gap
To address the challenges associated with the use of conventional cell state transition algorithms being unable to capture CSR events, , a new bioinformatic tool called sciCSR offers a dedicated toolset for B cell transition analysis. It leverages scRNA-seq data and, if available, paired BCR repertoire information, to extract B cell-specific features like:
- Sterile transcripts: These are germline transcripts that lack VDJ gene segments to encode a fully functional immunoglobulin heavy (IgH) chain. These transcripts are indicative of pre-CSR events due to AID targeting at switch regions. These transcripts can be captured in scRNA-seq experiments.
- SHM-induced sequence variations: Reflecting BCR diversification for antigen affinity enhancement. These events can be captured in scBCR-seq experiments.
These features, alongside conventional gene expression patterns, inform CellRank analysis, enabling accurate inference of transition probabilities between B cell states. Subsequently, TPT is applied to the generated transition matrix, providing a deeper understanding of CSR dynamics and revealing not just the directionality but also the ensemble of potential transition paths taken by B cells.
Validation and Applications
Validation of sciCSR involved simulated data, in vitro cell culture experiments, and gene knockout studies. In each case, it successfully recovered BCR isotype distributions at steady state and captured CSR dynamics over time courses. Moreover, application to SARS-CoV-2 vaccination scRNA-seq data demonstrated impressive accuracy in predicting B cell repertoire isotype distribution at later time points, showcasing its potential for studying antibody evolution and vaccine efficacy.
Conclusion
The sciCSR pipeline offers a novel and powerful approach for analyzing B cell transitions and predicting CSR dynamics from scRNA-seq data. In the end this helps us predict how vaccines work. More specifically, by incorporating B cell-specific features and employing TPT, it provides a more nuanced and accurate understanding of B cell maturation trajectories compared to conventional methods. This opens exciting avenues for investigating B cell responses to vaccination, autoimmune diseases, and immunotherapeutic interventions, ultimately advancing our understanding of the humoral immune system.
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Haider M. Hassan, Data Scientist, Bridge Informatics
Haider is one of our premier data scientists. He provides bioinformatic services to clients, including high throughput sequencing, data pre-processing, analysis, and custom pipeline development. Drawing on his rich experience with a variety of high-throughput sequencing technologies, Haider analyzes transcriptional (spatial and single-cell), epigenetic, and genetic landscapes.
Before joining Bridge Informatics, Haider was a Postdoctoral Associate at the London Regional Cancer Centre in Ontario, Canada. During his postdoc, he investigated the epigenetics of late-onset liver cancer using murine and human models. Haider holds a Ph.D. in biochemistry from Western University, where he studied the molecular mechanisms behind oncogenesis. Haider still lives in Ontario and enjoys spending his spare time visiting local parks. If you’re interested in reaching out, please email [email protected] or [email protected]
