Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.
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We thank J. Downing (St. Jude Children's Research Hospital, Memphis, TN, USA) for the Runx1-ires-GFP mouse. Research in the authors' laboratory is supported by the Medical Research Council, Biotechnology and Biological Sciences Research Council, Leukaemia and Lymphoma Research, the Leukemia and Lymphoma Society, Microsoft Research and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust - MRC Cambridge Stem Cell Institute. V.M. is supported by a Medical Research Council Studentship and Centenary Award and S.W. by a Microsoft Research PhD Scholarship.
The authors declare no competing financial interests.
Integrated supplementary information
(a) Gating strategy for Flk1+ cells at PS, NP and HF stages, and Runx1-ires-GFP+ cells (4SG) or Flk1+Runx1-ires-GFP− cells (4SFG−) at the 4S stage. (b) Total numbers of cells per embryo at each stage, estimated from FACS data. Each point represents one embryo and lines indicate the median.
(a) PCA of the 3,934 cells, coloured retrospectively according to the stage from which they were. Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−. (b) For each stage, the cells from different embryos are shown on the PCA as different colours (cells from other stages shown in grey). (c) PCA coloured according to the clusters cells belong to in Figure 1e. Green, I; Blue, II; Pink, III. (d) For each embryo, the percentage of cells in clusters 1, 2 and 3 was calculated. The mean and s.d. were then calculated for each cluster in each stage. Number of embryos shown in Fig. 1c.
Principal component analysis coloured according to stage of sorting (top) and whether cells express both Flk1 and Runx1 at the gene expression level (bottom). Cells not expressing both genes shown in grey. Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−.
Schematic of early embryonic development of the mesodermal lineage. The cardiac, smooth muscle, endothelial and blood lineages all diverge from Flk1+ mesoderm, while somatic muscle diverges earlier. (a-f) Populations of 50 cells were sorted from 5 embryos each at PS, NP and HF stages. FPKM values for three key genes are shown for each of the lineage decisions in the schematic. Points are the mean and s.d. of the 5 replicates.
Diffusion plots showing expression patterns of additional genes not shown in Figure 2.
Diffusion plots were coloured according to embryonic stage. Other cells shown in grey. Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG-.
The data were plotted using diffusion maps, principal component analysis, independent component analysis and t-SNE. Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG-.
State transition graph coloured by the number of times each binary state occurs in the 3,934 expression profiles. Grey, occurs once; blue, occurs twice; red, occurs more than twice.
Pools of 20 cells were simulated from single cell data and projected onto the principal component analysis for all 3,934 cells. Each plot shows one embryonic stage and the simulated pools of 20 cells for that population in black (normalized in the same way as single cells). Bottom right hand plot shows the head fold stage and simulated pools as well as actual pools of 20 cells sorted from embryos (pink).
(a) Motifs for DNA-binding TFs in the core network. Note that Eto2 and Lmo2 do not bind directly to the DNA, and the binding site of the Notch partner Rbpj was used for Notch1 as described previously. (b) To search for evidence that predicted regulatory interactions are direct, all haematopoietic TF ChIP-seq data in Codex2 were searched for peaks within the gene body or 50 kb up- and down-stream of the 20 network genes. Overlapping peaks were merged to identify putative regulatory sequences. Different colours indicate peaks from different experiments. A transcription factor binding site search (TFBS) was performed using TFBSsearch3 on the mouse genomic sequence of all regions to identify the consensus binding sites of the network TFs. Evolutionary conservation of TF binding motifs is a well-recognised feature of truly functional binding sites4. To identify conserved binding sites, PhyloP scores were calculated for each motif across 60 species using the tool provided by the UCSC Genome Browser. Evolutionarily conserved motifs previously validated in functional assays by us and other were characterised by PhyloP scores greater than 1, which was therefore used as the threshold. Results are summarised in Supplementary Table 2.
(a) UCSC Genome Browser tracks of re-analysed ChIP-Seq data for HoxB45 indicates that HoxB4 binds to the Erg+85 enhancer (shown in grey) during a 26-day differentiation time course that produces HSCs from ES cells, with HSCs able to contribute to long-term engraftment in recipient mice. Binding is absent at day 6, but becomes apparent by day 16. (b) Flow cytometric analysis of Flk1 and CD41 expression across a time-course of EB differentiation from Figure 4B indicates that different clones have similar kinetics of differentiation, with Flk1+ cells giving rise to Flk1+CD41+ and then Flk1−CD41+ cells. (c) Flow cytometric analysis of YFP expression in all live cells across a time-course of EB differentiation from Figure 4b illustrates reduction of YFP positive cells in ES cell clones where YFP expression is driven by mutant enhancer constructs. In (b) and (c), a representative experiment is shown from three biological repeats of 2-3 clones per construct.
(a) Hierarchical clustering of partial correlation coefficients between pairs of transcription factors for all 3,934 expression profiles. Pairwise coefficients were calculated while controlling for the effect of all other transcription factors. Erg does not correlate with Sox or Hox factors. (b) Network diagram showing putative undirected activating (red) and inhibiting (blue) relationships suggested by significant correlations (p-value < 1e-10).
Supplementary Figures 1–12, Supplementary Tables 1–4, 6 and Supplementary Note (PDF 14079 kb)
Summary of in silico perturbations (XLSX 89 kb)
Raw Ct values and normalised dCt values for the 3,934 cells presented in this paper (XLSX 3322 kb)
FPKM values for RNAseq of 50 cell populations (XLSX 5225 kb)
Initial and final states used in network synthesis (XLSX 40 kb)
Binary gene expression data (XLSX 204 kb)
List of cells with equivalent binary expression states (XLSX 46 kb)
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Moignard, V., Woodhouse, S., Haghverdi, L. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol 33, 269–276 (2015). https://doi.org/10.1038/nbt.3154
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