Superloaded Multiplexed scRNA-seq Data Preserves Primary Immune Cell Heterogeneity but Necessitates Stringent Doublet Removal

Single-cell RNA sequencing (scRNA-seq) has improved our ability to characterize rare cell populations. In practice, cells from different tissues or donors are simultaneously loaded onto the instrument (multiplexed) at the recommended (standard loading) or higher (superloading) numbers to save time and money. Although cost-effective, superloading can stymie computational analyses owing to high multiplet rates and sample complexity. We compared the effects of superloading on multiplexed single-cell gene expression and T cell receptor (TCR) data generated from human thymus and blood samples from different donors. Minimal transcriptomic differences were observed between the data generated by either standard or superloading. Irrespective of the loading cell number, we found that over 50% of the T cells expressing multiple TCR chains were doublets. Multiple samples can be run simultaneously without compromising data quality and subsequent analyses. However, an additional doublet removal step based on TCR configuration may improve the accuracy of T cell analysis.

单细胞RNA测序（single-cell RNA sequencing，scRNA-seq）显著提升了稀有细胞群体的表征能力。实际研究中，为节约时间与成本，通常将来自不同组织或供体的细胞以推荐上样量（标准上样）或更高上样量（超上样）同时上机测序，即采用多重化策略。尽管超上样策略具备成本效益，但由于多细胞率升高、样本复杂度提升，其可能会阻碍后续计算分析。本研究对比了超上样对来自不同供体的人胸腺与血液样本的多重化单细胞基因表达及T细胞受体（T cell receptor，TCR）数据的影响。结果显示，标准上样与超上样所获数据之间仅存在极细微的转录组学差异。无论上样细胞数量多少，研究团队均发现超过50%表达多种TCR链的T细胞为双细胞。研究表明，可同时运行多个样本而不损害数据质量及后续分析，但基于TCR构型的额外双细胞去除步骤，或可提升T细胞分析的准确性。