To understand stem cell differentiation along multiple lineages, it is necessary to resolve heterogeneous cellular states and the ancestral relationships between them. We developed a robotic miniaturized CEL-Seq2 implementation to carry out deep single-cell RNA-seq of ∼2,000 mouse hematopoietic progenitors enriched for lymphoid lineages, and used an improved clustering algorithm, RaceID3, to identify cell types. To resolve subtle transcriptome differences indicative of lineage biases, we developed FateID, an iterative supervised learning algorithm for the probabilistic quantification of cell fate bias in progenitor populations. Here we used FateID to delineate domains of fate bias and enable the derivation of high-resolution differentiation trajectories, thereby revealing a common progenitor population of B cells and plasmacytoid dendritic cells, which we validated by in vitro differentiation assays. We expect that FateID will improve understanding of the process of cell fate choice in complex multi-lineage differentiation systems.

要了解沿多个谱系的干细胞分化，必须解决异质细胞状态及其之间的祖先关系。我们开发了一种机器人小型化的CEL-Seq2实施方案，以进行〜的深层单细胞RNA-seq2,000个小鼠造血祖细胞富含淋巴样谱系，并使用改进的聚类算法RaceID3来识别细胞类型。为了解决指示谱系偏倚的细微转录组差异，我们开发了FateID，这是一种用于监督祖细胞中细胞命运偏倚的概率量化的迭代监督学习算法。在这里，我们使用FateID描绘了命运偏倚的域，并启用了高分辨率的分化轨迹，从而揭示了B细胞和浆细胞样树突状细胞的共同祖细胞群，我们通过体外分化测定法对其进行了验证。我们希望FateID将增进对复杂多系分化系统中细胞命运选择过程的了解。