Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling [DS]

We systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5' v1 and 3' v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Overall design: Various single-cell RNA-seq methods used to generate libraries using a 1:1:1:1 mixture of four lymphocyte cell lines (Jurkat, TALL-104, EL4, IVA12).

本研究系统评测了七种高通量单细胞RNA测序（high-throughput single-cell RNA-seq）方法。我们在完全一致的实验条件下，针对两种人类与两种小鼠淋巴细胞系的精准混合体系构建了21个测序文库（library），以此模拟免疫细胞类型及细胞大小层面的异质性。我们从细胞回收率、文库构建效率、检测灵敏度以及各细胞类型表达特征（expression signatures）的恢复能力四个维度，对各方法进行了全面评估。研究发现，10x Genomics的5' v1与3' v3测序方法具备更高的mRNA检测灵敏度。本研究证实，上述方法的基因测序脱落事件（drop-out events）更少，这可有效助力差异表达基因的鉴定，同时提升了单细胞表达谱与免疫细胞批量RNA测序（bulk RNA-seq）特征的一致性。总体实验设计：采用四种淋巴细胞系（Jurkat、TALL-104、EL4、IVA12）按1:1:1:1的比例混合制备文库，对多种单细胞RNA测序方法进行评测。