The transcriptional landscape of mouse blood stem/progenitor cell transitions at single cell resolution

Current gene-expression databases for the haematopoietic system provide information on gene expression profiles present in bulk populations. Although informative, these studies lack the resolution that can be gained at a single-cell level. In particular, population-average data assumes homogeneity within the population and may as such obscure the ability to detect the heterogeneity of decision-making processes in individual cells. Here we report 1656 single cell transcriptomes analysed by single-cell RNA sequencing. Cells were FACS sorted on broad gates encompassing haematopoietic stem and progenitor populations (HSPCs), with index sorting data collected to permit retrospective identification of populations by surface marker expression. Our dataset thus represents the gene expression landscape of HSPCs at single-cell resolution, capturing the heterogeneity in and between cell populations. Pseudotime analysis visualized haematopoietic stem (HSC) to progenitor transitions, identified HSC as well as lineage-specific transcriptional programs, and also highlighted putative lineage branching points. To provide access to the wider scientific community, a user-friendly website was developed with intuitive search and display functionality. Overall design: Single cell RNA sequencing of haematopoeitic stem and progenitor cells

当前用于造血系统的基因表达数据库，仅提供群体水平的基因表达谱信息。尽管此类数据集具备一定参考价值，但无法获得单细胞分辨率下的分析精度。
具体而言，群体平均表达数据默认群体内部具有均一性，因此可能掩盖检测单个细胞内决策过程异质性的能力。
本研究通过单细胞RNA测序（single-cell RNA sequencing）分析了1656个单细胞转录组。
实验中通过宽泛的分选门圈定造血干细胞及祖细胞（haematopoietic stem and progenitor cells, HSPCs）群体并进行荧光激活细胞分选（fluorescence-activated cell sorting, FACS），同时收集了索引分选数据，以便通过表面标志物表达对细胞群体进行回溯性鉴定。
因此本数据集以单细胞分辨率呈现了HSPCs的基因表达图谱，捕捉到了细胞群体内部及群体之间的异质性。
拟时序分析（pseudotime analysis）可视化了造血干细胞（haematopoietic stem cell, HSC）向祖细胞的分化过程，不仅鉴定出了HSC以及谱系特异性转录程序，还明确了潜在的谱系分支节点。
为便于更广泛的科研群体使用，本研究开发了一款具备直观搜索与展示功能的友好型用户网站。
整体实验设计：造血干细胞及祖细胞的单细胞RNA测序