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Decoding the regulatory network of early blood development from single-cell gene expression measurements

Nature Biotechnology volume 33, pages 269–276 (2015)Cite this article

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Abstract

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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Figure 1: Single-cell gene expression analysis of early blood development.
Figure 2: Diffusion plots identify developmental trajectories.
Figure 3: Regulatory network synthesis from single-cell expression profiles.
Figure 4: Network analysis predicts transcriptional interactions.
Figure 5: In silico perturbations predict key regulators of blood development.

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Acknowledgements

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.

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Author notes

  1. Victoria Moignard and Steven Woodhouse: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK

    Victoria Moignard, Steven Woodhouse, Yosuke Tanaka, Adam C Wilkinson, Wajid Jawaid, Evangelia Diamanti & Berthold Göttgens

  2. Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Victoria Moignard, Steven Woodhouse, Yosuke Tanaka, Adam C Wilkinson, Evangelia Diamanti & Berthold Göttgens

  3. Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany

    Laleh Haghverdi, Florian Buettner & Fabian J Theis

  4. Department of Mathematics, Technische Universität München, Garching, Germany

    Laleh Haghverdi & Fabian J Theis

  5. Cancer Research UK Stem Cell Haematopoiesis Group, Paterson Institute for Cancer Research, University of Manchester, Manchester, UK

    Andrew J Lilly & Valerie Kouskoff

  6. Laboratory for Stem Cell Biology, RIKEN Center for Developmental Biology, Chuo-ku, Kobe, Japan.,

    Yosuke Tanaka & Shin-Ichi Nishikawa

  7. Sanger Institute-EBI Single Cell Genomics Centre, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.,

    Iain C Macaulay

  8. Department of Computer Science, University of Leicester, Leicester, UK

    Nir Piterman

  9. Microsoft Research Cambridge, Cambridge, UK

    Jasmin Fisher

  10. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Jasmin Fisher

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  1. Victoria Moignard
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Contributions

V.M., A.J.L., Y.T., A.C.W. and I.C.M. performed experiments and analyzed data. S.W., L.H., F.B. and N.P. developed computational tools. W.J. and E.D. analyzed data. S.-I.N., V.K., F.J.T., J.F. and B.G. conceived the study. V.M., S.W., J.F. and B.G. wrote the paper with help from all co-authors.

Corresponding authors

Correspondence to Fabian J Theis, Jasmin Fisher or Berthold Göttgens.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Fluorescence activated cell sorting of cells with blood potential.

(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.

Supplementary Figure 2 Development is asynchronous.

(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.

Supplementary Figure 3 Flk1+Runx1+ cells are found in all anatomical stages.

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−.

Supplementary Figure 4 RNA sequencing of populations of 50 cells.

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.

Supplementary Figure 5 Diffusion plots.

Diffusion plots showing expression patterns of additional genes not shown in Figure 2.

Supplementary Figure 6 Diffusion plots highlighting each stage.

Diffusion plots were coloured according to embryonic stage. Other cells shown in grey. Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG-.

Supplementary Figure 7 Comparison of multivariate analysis techniques.

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-.

Supplementary Figure 8 Some states occur in multiple cells.

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.

Supplementary Figure 9 Populations mask variation and asynchrony.

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).

Supplementary Figure 10 Analysis of transcription factor binding sites in network gene loci.

(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.

Supplementary Figure 11 The Erg+85 enhancer is active during haematopoietic development

(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.

Supplementary Figure 12 Partial correlation analysis.

(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 information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Tables 1–4, 6 and Supplementary Note (PDF 14079 kb)

Supplementary Table 5

Summary of in silico perturbations (XLSX 89 kb)

Supplementary Table 7

Raw Ct values and normalised dCt values for the 3,934 cells presented in this paper (XLSX 3322 kb)

Supplementary Table 8

FPKM values for RNAseq of 50 cell populations (XLSX 5225 kb)

Supplementary Table 9

Initial and final states used in network synthesis (XLSX 40 kb)

Supplementary Table 10

Binary gene expression data (XLSX 204 kb)

Supplementary Table 11

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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