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A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation

机译:小鼠造血干和祖细胞分化的单细胞分辨率图

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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 intuitiveWeb interface as a new community resource to permit visualization of gene expression in HSPCs at single-cell resolution for any gene of choice.
机译:维持血液系统需要由造血干细胞和祖细胞(HSPC)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,因此新的单细胞谱分析技术为绘制HSPC分化基础的动态分子变化提供了令人兴奋的可能性。在这里,我们已经使用单细胞RNA测序来分析超过1600个单个HSPC,并且深度测序已使每个细胞平均可检测6558个蛋白质编码基因。索引排序与广泛的排序门相结合,使我们能够将细胞追溯分配给12种常见排序的HSPC表型,同时还捕获了常规门控通常排除的中间细胞。我们进一步表明,可以将独立生成的单细胞数据集投影到单细胞分辨率表达图上,以直接比较来自多个组的数据并建立和完善新的假设。分化轨迹的重建揭示了与早期淋巴样,红系和粒细胞-巨噬细胞分化相关的动态表达变化。后两个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。通过使用外部刺入型对照,我们估计了每个细胞的绝对信使RNA(mRNA)水平,这首次表明尽管总mRNA普遍下降,但是一部分基因在未成熟干细胞中显示出更高的表达水平,这与主动维持干细胞状态最后,我们报告了作为新的社区资源的直观Web界面的开发,以允许以单细胞分辨率对HSPC中的基因表达进行可视化,以选择任何基因。

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