Supporting data for "Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design"

Pooling cells from multiple biological samples prior to library preparation within the same single-cell RNA sequencing experiment provides several advantages, including lower library preparation costs and reduced unwanted technological variation, such as batch effects. Computational demultiplexing tools based on natural genetic variation between individuals provide a simple approach to demultiplex samples, which does not require complex additional experimental procedures. However, these tools have not been evaluated in cancer, where somatic variants, which could differ between cells from the same sample, may obscure the signal in natural genetic variation. Here, we performed <i>in silico</i> benchmark evaluations by combining raw sequencing reads from multiple single-cell samples in high-grade serous ovarian cancer, which has a high copy number burden, and lung adenocarcinoma, which has a high tumor mutational burden. Our results confirm that genetic demultiplexing tools can be effectively deployed on cancer tissue using a pooled experimental design, although high proportions of ambient RNA from cell debris reduce performance. This strategy provides significant cost savings through pooled library preparation. To facilitate similar analyses at the experimental design phase, we provide freely accessible code and a reproducible Snakemake workflow built around the best-performing tools found in our <i>in silico</i> benchmark evaluations, available from our GitHub repository.

在同一单细胞RNA测序（single-cell RNA sequencing）实验中，将多份生物学样本的细胞于文库制备步骤前进行混合，可带来多项优势，包括降低文库制备成本，减少不必要的技术变异（如批次效应（batch effects））。基于个体间自然遗传变异的计算去混样工具（computational demultiplexing tools）可为样本拆分提供简便方案，无需复杂的额外实验流程。然而，此类工具尚未在癌症样本中得到评估：癌症样本中，同一样本的细胞间可能存在体细胞变异（somatic variants），这类变异会掩盖自然遗传变异的信号。本研究针对拷贝数负荷（copy number burden）较高的高级别浆液性卵巢癌，以及肿瘤突变负荷（tumor mutational burden）较高的肺腺癌，将多份单细胞样本的原始测序读段进行合并，以此开展计算机模拟（in silico）基准评估。本研究结果证实，采用混合实验设计可将遗传去混样工具有效应用于癌症组织样本，不过细胞碎片产生的高比例背景RNA（ambient RNA）会降低工具的性能。该混合文库制备策略可显著降低实验成本。为便于研究人员在实验设计阶段开展同类分析，本研究提供了可免费获取的代码，以及基于本次计算机模拟基准评估中表现最优的工具构建的可复现Snakemake工作流（Snakemake workflow），相关资源可从本研究的GitHub代码仓库（GitHub repository）获取。