英国伦敦大学学院Franca Fraternali等研究人员合作发现，sciCSR利用单细胞转录组数据推断B细胞状态转变并预测类别变换重组动态。2023年11月6日，《自然—方法学》杂志在线发表了这项成果。

研究人员报道了sciCSR（读作“scissor”，即类别变换重组的单细胞推断），它是一个计算管道，可从单细胞RNA测序（scRNA-seq）实验中分析B细胞的类别变换重组（CSR）事件和动态。sciCSR在模拟数据和真实数据上都得到了验证，它重新分析了scRNA-seq的排列，以区分生产性重链免疫球蛋白转录本和生殖“不育”转录本。根据B细胞scRNA-seq数据快照，可建立马尔可夫状态模型，推断CSR的动态和方向。

将sciCSR应用于严重急性呼吸系统综合征冠状病毒2疫苗接种时间序列scRNA-seq数据，研究人员发现sciCSR可以利用所收集时间序列中较早时间点的数据，预测后续时间点B细胞受体复合物的同种型分布，准确率很高（余弦相似度约为0.9）。利用B细胞特有的过程，sciCSR可以识别传统RNA速度分析经常忽略的转变，并揭示免疫反应过程中B细胞CSR的动态。

附：英文原文

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

Author: Ng, Joseph C. F., Montamat Garcia, Guillem, Stewart, Alexander T., Blair, Paul, Mauri, Claudia, Dunn-Walters, Deborah K., Fraternali, Franca

Issue&Volume: 2023-11-06

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. sciCSR is a computational workflow that leverages single-cell RNA sequencing data to predict B cell dynamics and class-switch recombination events.

DOI: 10.1038/s41592-023-02060-1

Source: https://www.nature.com/articles/s41592-023-02060-1