Droplet barcoding for single cell transcriptomics applied to embryonic stem cells

Recently, RNA sequencing has achieved single cell resolution, but what is limiting is an effective way to routinely isolate and process large numbers of individual cells for in-depth sequencing, and to do so quantitatively. We have developed a droplet-microfluidic approach for parallel barcoding thousands of individual cells for subsequent RNA profiling by next-generation sequencing. This high-throughput method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. Using this technique, we analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after LIF withdrawal. The reproducibility and low noise of this high-throughput single cell data allowed us to deconstruct cell populations and infer gene expression relationships. Overall design: A total of 8 single cell data sets are submitted: 3 for mouse embryonic stem (ES) cells (1 biological replicate, 2 technical replicates); 3 samples following LIF withdrawal (days 2,4, 7); one pure RNA data set (from human lymphoblast K562 cells); and one sample of single K562 cells.

近年来，RNA测序（RNA sequencing）已实现单细胞分辨率，但当前的核心瓶颈在于缺乏可常规分离、定量处理大量单个细胞以开展深度测序的有效方案。为此，我们开发了微流控液滴技术（droplet-microfluidic approach），可对数千个单个细胞进行并行条码标记（parallel barcoding），以便后续通过下一代测序（next-generation sequencing）开展RNA谱分析。该高通量方法展现出极低的噪声特征，且可轻松适配其他基于测序的检测实验。利用该技术，我们对小鼠胚胎干细胞（mouse embryonic stem cells）进行了分析，详细揭示了其群体结构，以及在撤除白血病抑制因子（LIF）后分化的异质性起始过程。这份高通量单细胞数据具备优异的可重复性与低噪声特性，使我们能够解构细胞群体并推断基因表达调控关系。整体实验设计：本次共提交8组单细胞数据集：3组小鼠胚胎干细胞（ES）数据（含1组生物学重复、2组技术重复）；3组撤除LIF后的样本数据（分别对应第2、4、7天）；1组纯RNA数据集（来源于人类淋巴母细胞K562细胞）；以及1组单个K562细胞的数据集。