Nestorowa Sonia, Hamey Fiona K, Pijuan Sala Blanca, Diamanti Evangelia, Shepherd Mairi, Laurenti Elisa, Wilson Nicola K, Kent David G, Göttgens Berthold
Department of Haematology and Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom.
Blood. 2016 Aug 25;128(8):e20-31. doi: 10.1182/blood-2016-05-716480. Epub 2016 Jun 30.
Maintenance of the blood system requires balanced cell fate decisions by hematopoietic stem and progenitor cells (HSPCs). Because cell fate choices are executed at the individual cell level, new single-cell profiling technologies offer exciting possibilities for mapping the dynamic molecular changes underlying HSPC differentiation. Here, we have used single-cell RNA sequencing to profile more than 1600 single HSPCs, and deep sequencing has enabled detection of an average of 6558 protein-coding genes per cell. Index sorting, in combination with broad sorting gates, allowed us to retrospectively assign cells to 12 commonly sorted HSPC phenotypes while also capturing intermediate cells typically excluded by conventional gating. We further show that independently generated single-cell data sets can be projected onto the single-cell resolution expression map to directly compare data from multiple groups and to build and refine new hypotheses. Reconstruction of differentiation trajectories reveals dynamic expression changes associated with early lymphoid, erythroid, and granulocyte-macrophage differentiation. The latter two trajectories were characterized by common upregulation of cell cycle and oxidative phosphorylation transcriptional programs. By using external spike-in controls, we estimate absolute messenger RNA (mRNA) levels per cell, showing for the first time that despite a general reduction in total mRNA, a subset of genes shows higher expression levels in immature stem cells consistent with active maintenance of the stem-cell state. Finally, we report the development of an intuitive Web interface as a new community resource to permit visualization of gene expression in HSPCs at single-cell resolution for any gene of choice.
血液系统的维持需要造血干细胞和祖细胞（HSPCs）做出平衡的细胞命运决定。由于细胞命运选择是在单个细胞水平上执行的，新的单细胞分析技术为描绘HSPC分化背后的动态分子变化提供了令人兴奋的可能性。在这里，我们使用单细胞RNA测序对1600多个单个HSPC进行了分析，深度测序能够平均检测到每个细胞6558个蛋白质编码基因。索引分选与宽泛的分选门相结合，使我们能够追溯性地将细胞分配到12种常见分选的HSPC表型，同时还能捕获通常被传统门控排除的中间细胞。我们进一步表明，独立生成的单细胞数据集可以投射到单细胞分辨率表达图谱上，以直接比较来自多个组的数据，并构建和完善新的假设。分化轨迹的重建揭示了与早期淋巴细胞、红细胞和粒细胞-巨噬细胞分化相关的动态表达变化。后两条轨迹的特征是细胞周期和氧化磷酸化转录程序的共同上调。通过使用外部掺入对照，我们估计了每个细胞的绝对信使RNA（mRNA）水平，首次表明尽管总mRNA普遍减少，但一部分基因在未成熟干细胞中显示出更高的表达水平，这与干细胞状态的积极维持一致。最后，我们报告了一个直观的网络界面的开发，作为一种新的社区资源，允许以单细胞分辨率可视化任何选择的基因在HSPC中的基因表达。
