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.

类别转换重组 (CSR) 是 B 细胞成熟的一个组成部分。在这里，我们提出了 sciCSR（发音为“scissor”，类别转换重组的单细胞推断），这是一种计算管道，可通过单细胞 RNA 测序 (scRNA-seq) 实验分析 B 细胞的 CSR 事件和动态。 sciCSR 经模拟和真实数据验证，重新分析 scRNA-seq 比对，以区分生产性重链免疫球蛋白转录本和种系“无菌”转录本。根据 B 细胞 scRNA-seq 数据的快照，建立马尔可夫状态模型来推断 CSR 的动态和方向。将 sciCSR 应用于严重急性呼吸综合征冠状病毒 2 疫苗接种时间过程 scRNA-seq 数据，我们观察到 sciCSR 使用收集的时间过程中较早时间点的数据预测后续时间点 B 细胞受体库的同种型分布具有高精度（余弦相似度~0.9）。使用 B 细胞特有的过程，sciCSR 可以识别传统 RNA 速度分析经常遗漏的转变，并可以揭示免疫反应期间 B 细胞 CSR 动态的见解。