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

Class-switch recombination (CSR) is an integral part of B cell maturation. Here we present sciCSR (pronounced ‘scissor’, single-cell inference of class-switch recombination), a computational pipeline that analyzes CSR events and dynamics of B cells from single-cell RNA sequencing (scRNA-seq) experiments. Validated on both simulated and real data, sciCSR re-analyzes scRNA-seq alignments to differentiate productive heavy-chain immunoglobulin transcripts from germline ‘sterile’ transcripts. From a snapshot of B cell scRNA-seq data, a Markov state model is built to infer the dynamics and direction of CSR. Applying sciCSR on severe acute respiratory syndrome coronavirus 2 vaccination time-course scRNA-seq data, we observe that sciCSR predicts, using data from an earlier time point in the collected time-course, the isotype distribution of B cell receptor repertoires of subsequent time points with high accuracy (cosine similarity ~0.9). Using processes specific to B cells, sciCSR identifies transitions that are often missed by conventional RNA velocity analyses and can reveal insights into the dynamics of B cell CSR during immune response. sciCSR is a computational workflow that leverages single-cell RNA sequencing data to predict B cell dynamics and class-switch recombination events.