Ng, Joseph CF; Montamat Garcia, Guillem; Stewart, Alexander T; Blair, Paul; Mauri, Claudia; Dunn-Walters, Deborah K; Fraternali, Franca; (2023) sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data. Nature Methods 10.1038/s41592-023-02060-1. (In press).

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Abstract

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.

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