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. 2016 Aug 25;128(8):e20-31.
doi: 10.1182/blood-2016-05-716480. Epub 2016 Jun 30.

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

Sonia Nestorowa  1 , Fiona K Hamey  1 , Blanca Pijuan Sala  1 , Evangelia Diamanti  1 , Mairi Shepherd  1 , Elisa Laurenti  1 , Nicola K Wilson  1 , David G Kent  1 , Berthold Göttgens  1
Affiliations

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

Sonia Nestorowa et al. Blood. .

Abstract

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.

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Figures

Figure 1
Figure 1
Generating linked transcriptional and surface marker profiles for more than 1600 single HSPCs. (A) Schematic of the sorting strategy that was used paired with index sorting data. Bone marrow cells were stained with 9 antibodies against various cell surface markers to isolate HSPCs (Lin−c-Kit+Sca1+ [L−S+K+]) and progenitors (Lin−c-Kit+Sca1− [L−S−K+]). Almost all cells in the Flk2-CD34 gate and the CD16/32-Flk2 gate were collected for HSPCs and progenitors, respectively, within broad, all-encompassing gates. In addition, LT-HSCs (Lin–c-Kit+Sca1+CD34–Flk2–) were collected separately to ensure that adequate numbers were collected. Each cell population retrospectively identified is shown in the table; colors and names remain consistent throughout the text. Letters indicate populations in the flow cytometry diagrams. (B) Unsupervised hierarchical clustering of gene expression data for all cells. Clustering was performed by using all 4773 variable genes except Ly6a/Sca-1 to avoid bias in clustering. The cells split into 4 major clusters (cluster 1, purple; cluster 2, turquoise; cluster 3, gold; cluster 4, pink). The top 10 genes enriched in each cluster are displayed in the heat map, showing gene expression on a log2 scale from blue to orange (low to high). The clusters were also compared by cell type composition, following both broad and narrow gating strategies. Broad gating involved the classification of all cells into a cell type category, whereas narrow gating included only cells that are more likely to fit the predefined HSPC classification, gated around the greatest density of cells within the population gating strategy. Cell type is colored on the basis of the scheme used in Figure 1A. Gray cells in the narrow gating strategy represent cells unassigned to any population. FACS, fluorescence-activated cell sorting; FSC-H, forward-scattered light-height; Prog, progenitor.
Figure 2
Figure 2
Multidimensional analysis can be used to visualize gene expression across HSPC differentiation. (A) Schematic explaining how diffusion maps are used as a dimensionality reduction procedure. (B) Diffusion map of all cells was colored on the basis of previously defined clusters (cluster 1, purple; cluster 2, turquoise; cluster 3, gold; cluster 4, pink). Diffusion components 1, 2, and 3 are shown. (C) Diffusion map of all cells was colored according to the expression of selected genes. The genes were chosen on the basis of published literature or were identified computationally as highly expressed in specific cell populations. The color corresponds to a log2 scale of expression ranging between 0 and the maximum value for each gene. (D) Diffusion map of all cells was colored by surface marker expression from the normalized index data. The majority of these markers were used for cell selection, with the exception of CD48, CD150, and EPCR. The color corresponds to a linear scale of expression ranging between the minimum and maximum value for each marker.
Figure 3
Figure 3
The single-cell HSPC transcriptional landscape can be used to visualize HSPC populations and their relationships. (A) Diffusion map of all cells was colored on the basis of cell population using narrow gating. All populations were identified retrospectively by using the index sorting data. Populations were identified by using normalized index data. The cells of interest for each population are colored purple and enlarged for easier visibility. (B) Diffusion map of all cells with projection of data from recently published data sets. Data collected by Kowalczyk et al (C57BL/6, DBA/2) and Grover et al (Vwf-EGFP) is displayed. Both groups collected HSCs from mice 2 to 3 months (orange) and 20 to 25 months (blue) old. HSCs were defined as Lin–c-Kit+Sca1+CD150+CD48–.
Figure 4
Figure 4
Pseudotime analysis reveals trends in surface marker and gene expression for differentiation trajectories. (A) Diffusion map colored by pseudotime trajectories to E, GM, and L fates. Each trajectory starts from an HSC (blue) and ends with a progenitor (red). (B) Changes in surface marker expression and FSC-H through pseudotime for each of the 3 trajectories obtained from the normalized index data. For each trajectory, it is possible to see what cell types are passed through to reach the final cell fate. (C) Normalized expression of genes positively (up) or negatively (down) correlated with the pseudotemporal ordering for each trajectory. Mean normalized expression is plotted with standard deviation. (D) Most significant relevant terms from gene set enrichment analysis for all the trajectories, performed in Enrichr. Terms with an adjusted P value <.05 (using Benjamini-Hochberg correction for multiple testing) were considered significant. The full tables of results can be found in the supplemental Data. MGI, mouse genome informatics.
Figure 5
Figure 5
Analysis of cell cycle activation during HSPC differentiation at single-cell resolution. (A) Diffusion map of all cells colored by computationally assigned cell cycle category. There is no assignment for G0 separately because of limitations of the method. (B) Proportion of E-SLAMs, LMPPs, GMPs, and MEPs in each of the cell cycle categories. The cell types displayed are based on the narrow gating strategy. (C) Gene set enrichment analysis was performed for the 3 trajectories after the removal of cell cycle genes. The most relevant significant terms for genes positively correlated with pseudotime analysis are shown. Terms with an adjusted P value <.05 (using Benjamini-Hochberg correction for multiple testing) were considered significant. The full tables of results can be found in the supplemental Data. (D) Average expression of hydrogen ion transmembrane transport genes and cell cycle genes across pseudotime. Each gene was normalized across the median of all 3 trajectories for plotting. The average expression is colored by trajectory, and means are shown with standard deviations. tRNA, transfer RNA.
Figure 6
Figure 6
Single-cell analysis can be used to estimate absolute differences in total mRNA content across cell types. (A) Schematic explanation of how plate composition and ERCC spike-ins are used to estimate absolute RNA levels. The plate organization for this study included cells from multiple sorting gates (HSPC, Prog, LT-HSCs) and each well contained ERCC spike-ins. The sequencing depth varies across lanes and cell types; therefore, ERCC spike-ins are used to normalize across cell types within a lane, in which the spike-in content becomes level within a lane but cell mRNA content may still vary. After this step, RNA content can be normalized across lanes. (B) Diffusion map of all cells was colored by RNA content. Estimates of total RNA content were calculated by summing the absolute normalized counts per cell. The scale ranges from blue to green to yellow to red with increasing RNA content. (C) Sum of normalized counts for E-SLAMs, LMPPs, GMPs, and MEPs colored by the scheme used in Figure 1A. Significance in differences in RNA content between cell types was calculated by using a 1-way analysis of variance test (**P < .001; ***P < .0001). (D) FSC-H for E-SLAMs, LMPPs, GMPs, and MEPs, colored by the scheme used in Figure 1A. FSC-H is used as an indicator of cell size. Significance in differences in FSC-H between cell types was calculated by using a 1-way analysis of variance test (**P < .001; ***P < .0001). (E) Most relevant significant terms from gene enrichment expression analysis on genes downregulated in absolute terms in E-only, GM-only, and E and GM trajectories. The numbers of genes showing downregulation along pseudotime in absolute terms is displayed in the Venn diagram. Terms with an adjusted P value <.05 (using Benjamini-Hochberg correction for multiple testing) were considered significant. The full tables of results can be found in the supplemental Data.
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