Blood formation is believed to occur through stepwise progression of haematopoietic stem cells (HSCs) following a tree-like hierarchy of oligo-, bi- and unipotent progenitors. However, this model is based on the analysis of predefined flow-sorted cell populations. Here we integrated flow cytometric, transcriptomic and functional data at single-cell resolution to quantitatively map early differentiation of human HSCs towards lineage commitment. During homeostasis, individual HSCs gradually acquire lineage biases along multiple directions without passing through discrete hierarchically organized progenitor populations. Instead, unilineage-restricted cells emerge directly from a ‘continuum of low-primed undifferentiated haematopoietic stem and progenitor cells’ (CLOUD-HSPCs). Distinct gene expression modules operate in a combinatorial manner to control stemness, early lineage priming and the subsequent progression into all major branches of haematopoiesis. These data reveal a continuous landscape of human steady-state haematopoiesis downstream of HSCs and provide a basis for the understanding of haematopoietic malignancies.
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We thank C. Drumm for help with 3D graphics, K. Hexel, S. Schmitt, C. Felbinger and M. Eich from the DKFZ flow cytometry facility for flow cytometry support, the EMBL Genomics Core Facility for sequencing and R. Aiyar, A. Jones, M. Milsom and all members of HI-STEM and the Steinmetz group for helpful discussions on the manuscript as well as T. Schroeder and D. Löffler for initial discussions. This work was supported by the SFB873 funded by the Deutsche Forschungsgemeinschaft (DFG) (to C.L., M.A.G.E. and A.T.), the Dietmar Hopp Foundation (to M.A.G.E. and A.T.) and the US National Institutes of Health (P01 HG000205 to L.M.S.).
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Integrated supplementary information
(a) Sorting was performed exclusively on the gates highlighted in red in the CD34 versus CD38 panel and all surface markers were indexed. Cells outside the green gate in the CD45RA versus CD90 panel were excluded retrospectively as they represented mature immune cells (not shown). Percentage of cells within each gate is indicated. Data from individual 1 is shown. (b) Distribution of cells subjected to single-cell RNA-Seq within classically defined gates1,2.
(a) Bioanalyzer traces of amplified cDNA generated from single human HSPCs with the default smart-seq2 protocol (upper panel), QUARTZ-Seq (middle panel, applied to individual 2) and a modified version of smart-seq2 (lower panel, applied to individual 1, see methods). (b,c) Filtering of cells based on total read counts and number of genes expressed. The use of the modified smart-seq2 protocol (b) strongly decreased the dropout rate compared to the QUARTZ-Seq protocol (c). The large dropout rate in the QUARTZ-Seq protocol was due to the small volume used. (d,e) The number of genes per cell (d) and cell-cell correlation (e) for the two individuals compared to two other recent single-cell RNA-Seq data sets from the haematology field3,4. Box plots display median bar, first–third quantile box and 5th–95th percentile whiskers. n = 379 cells individual 1, n = 1,034 cells individual 2, n = 218 cells Individual 1, HSCs; n = 2,730 cells Paul et al. n = 1,058 cells Kowalczyk et al.. (f,g) The mean read count and variance of spike-ins (large black dots) and genes (small dots) were compared to identify genes whose biological noise exceeded technical variability (cyan dots)5. (h) The total RNA content of Lin−CD34+cells varies widely. (i) Cartoon describing the hypothetical effect of large variations in RNA amount in homogeneous populations. Two cells from the same population (red) display identical RNA concentrations for two genes, but differ in RNA amount by 10-fold. A third cell from a different population expresses the two sample genes at a different ratio but absolute high number. Following sequencing, the genes are more likely to be lost in the smaller cell, which cannot be reverted by normalization. (j) PCA performed on lymphoid (CLP) specific genes6 should clearly separate cells expressing the lymphoid surface marker CD10. However, without normalization cells are only arranged by read count (i). Standard normalization using a harmonic mean estimator of library size does not solve the problem (ii). Following normalization by Posterior Odds Ratios (POR, see Online Methods) a PCA performed on CLP specific genes clearly separates CD10+ and CD10− cells (iii).
Supplementary Figure 3 indeXplorer, a web-based GUI for exploring single-cell index-omics and index-culture data.
indeXplorer combines the capabilities of a FACS software with tools for the analysis of single cell transcriptomics data in a single graphical user interface. FACS and transcriptomics modules are tightly linked, allowing for example the display of gene expression or transcriptomic clusters on FACS scatter plots (a), differential expression testing of arbitrarily gated populations (b), as well as hierarchical clustering (c) and principal component analysis (d). indeXplorer further provides tools for gene list management, allows the user to download plots as publication-quality pdfs, and to store & restore sessions. On http://steinmetzlab.embl.de/shiny/indexplorer/?demo=yes we provide a short interactive introduction into the use of indeXplorer.
(a) Cluster stability analysis7 of the Lin−CD34+CD38− and Lin−CD34+CD38+ populations. For n = 500 repetitions, 66% of cells were randomly selected, clustering was performed and a consensus clustering was computed. The probability that clusterings obtained from random subsets of the data agree with the consensus is plotted on the y axis (box plot with median bar, first–third quantile box and 5th–95th percentile whiskers). (b) Gap-statistic (Gapk) of Lin−CD34+CD38− and Lin−CD34+CD38+ compartments. A maximum of Gapk indicates the statistically optimal cluster number8. (c) clustering obtained using ICGS9. 4 outlier cells in the Lin−CD34+CD38− compartment (left panel, blue bar) were characterized by a lower number of genes detected, but no coherent differences in gene expression (not shown). (d)–(f) Transcriptomic heterogeneity in the Lin−CD34+CD38− compartment. (d) > 10 principal components in Lin−CD34+CD38− exceed noise. (e) Principal components 2 and 5 of a PCA performed on combined data from both individuals. Loadings of all genes with annotated cell-cycle phase dependent gene expression patterns10 are shown in the right panel. Cell cycle associated genes are shifted compared to other genes on PC2 and arranged by peak time of gene expression on PC5. Scores of all Lin−CD34+CD38− cells are shown in the left panel. (f) Principal components 3 and 4. Loadings of all genes annotated as CD38+CD10+ ‘CLP’ or CD41+CD42+GP6+ ‘Mk’ specific6 are shown, demonstrating that PC3 and PC4 correlate with lymphoid versus megakaryocytic priming. Scores of all Lin−CD34+CD38− cells are shown in the left panel. (g) Principal components of Lin−CD34+CD38− cells are significantly correlated to surface marker expression. Data from individual 1 are shown. (h) Expression of neutrophil marker genes in relation to CD45RA and CD135. See also Main Fig. 4c. (i) Expression of cell cycle genes suggests that the CD10midFSC-Ahigh population is more actively cycling. (j) Ki67-Hoechst cell cycle analyses of IL7R−CD9+ and IL7R+CD9− populations, corresponding to sB and lB respectively. (k) Cells from the transcriptomic Im cluster have intermediate CD38 expression and group with Lin−CD34+CD38− HSPCs in t-SNE analysis.
(a) The similarity of every cell to each of the progenitor classes was computed by STEMNET (see methods), projected on a unit circle, and used to quantify the degree and direction of transcriptomic priming. Data from individual 2 is shown. (b) immunophenotypes highlighted on the STEMNET plot for individual 2. (c) CD38 surface marker expression highlighted on the STEMNET plot for individual 1. (d,e) Dual lineage primed cells, defined as cells with more than 25% priming in two directions, were highlighted on the STEMNET plot (d) or in a ternary plot depicting only priming in the Mk, Neutro, and Eo/Baso/Mast directions (e). (f) Rare IRF8+GFI1+ progenitors9 are not a typical intermediate stage between granulocytes and monocytes but appear displaced from developmental trajectories or are fully primed towards individual lineages. (g) Distribution of colony types observed in the index-culture experiment. Functionally bipotent cells are highlighted. (h,i) The transcriptomic lineage priming of immunophenotypic CMPs depends strongly on the gating strategy. Cells from the CMP gate (Lin−CD34+CD38+CD45RA−CD135+) were highlighted on the STEMNET plot (upper panels) or as ternary plots (lower panels). The effect of variations in the CD135 (h) and CD38 (i) gates are shown. P-values were calculated by kernel-density based tests comparing each population to CD49f+ HSCs. For CD49f + HSCs, n = 101 single cells; CMPs, default gate, n = 64; CMPs, relaxed CD38 gate, n = 164; CMPs, stringent CD38 gate, n = 24; CMPs, relaxed CD135 gate, n = 180.
(a) To demonstrate the ability of STEMNET to identify subsequent binary branching events, we assumed a scenario where cells locate on developmental trajectories between universally defined branching points (left panel, see also methods). For each gene used by STEMNET as a marker specific to a given developmental endpoint, we reordered the expression data to parallel the developmental distance from that endpoint. The middle panel depicts exemplary the reordered expression of CSF3R, a neutrophil marker. Finally, we apply STEMNET to the reordered data set (right panel). (b) To simulate data using a more realistic noise level, we estimated the correlation between developmental distance and gene expression from the data for each gene (upper panels). We then reshuffled the expression values such that the correlation between marker gene expression and (simulated) developmental distance approximates the correlation estimated from the data (lower panels). (c) STEMNET on reshuffled data. (d) To simulate a scenario where HSCs pass through discrete progenitor cell types, cells were placed near branching points, data was simulated as described for panel (b), and STEMNET was applied to the reshuffled data. (e) Projection of single cell expression data into diffusion map space11.
(a) Regression models used to estimate transcriptomic quantities from FACS surface marker expression. Model coefficients and the fraction of variance explained in a 10-fold cross validation scheme (R2) are shown. For genes marked with an asterisk, regression models were constructed on mRNA expression and applied to FACS surface marker expression. b, Linkage of the exact predicted direction of transcriptomic priming (for the cell types with robust colony forming abilities; Neutro, Ery, Mk) to the actual cell type composition of the ex vivo colonies. Illustration (left panel) and quantitative linkage (right panel) are shown. The exact direction of transcriptomic priming was estimated for each founder cell from index-culture based on regression models constructed on all surface markers and compared to the observed colony composition. (c) CD71 and KEL FACS marker and mRNA expression in relation to the degree of transcriptomic Ery/Mk priming and the percentage of Ery/Mk cells in the colony. (d,e) As an additional experimental measure of developmental plasticity, we cultured single HSPCs for 1 week, split the colony in four and determined the lineage outcome of the daughter colonies two weeks later. For several colonies, the lineage output varied significantly across daughters (e, P-values are from a chi-square test for independence). These colonies tended to derive from developmentally more primitive cells (d). P-value is from a Pearson product moment correlation test with n = 96 split-in-four experiments.
(a,b) Averaged gene expression of all gene modules, including those omitted from the main figure, was smoothened and plotted against the degree of lineage-specific priming. Data is shown for individual 1 (a) and 2 (b). (c) Comparison between gene modules from individual 1 and 2. For each module from individual 1, the overlap with each module from individual 2 is shown. Due to the higher number of cells analysed, gene modules from individual 2 split up into multiple modules from individual 1, while modules from individual 1 overlap only with a single module from individual 2. Only genes discovered in both individuals were included in this analysis.
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Velten, L., Haas, S., Raffel, S. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat Cell Biol 19, 271–281 (2017). https://doi.org/10.1038/ncb3493
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