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

Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC, Buettner F, Macaulay IC, Jawaid W, Diamanti E, Nishikawa S, Piterman N, Kouskoff V, Theis FJ, Fisher J, Göttgens B - Nat. Biotechnol. (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. [2] Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.

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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Network analysis predicts transcriptional interactions(a) Alignment of mammalian Erg+85 enhancer. Hox sites, red. Sox sites, light blue. (b) Percentage of Flk1+CD41−, Flk1+CD41+ and Flk1−CD41+ cells on days 3-7 of differentiation expressing YFP. Data are mean and s.e.m of triplicate differentiations of 2-3 clones per construct. P-values are reported in Supplementary Table 6.
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Figure 4: Network analysis predicts transcriptional interactions(a) Alignment of mammalian Erg+85 enhancer. Hox sites, red. Sox sites, light blue. (b) Percentage of Flk1+CD41−, Flk1+CD41+ and Flk1−CD41+ cells on days 3-7 of differentiation expressing YFP. Data are mean and s.e.m of triplicate differentiations of 2-3 clones per construct. P-values are reported in Supplementary Table 6.

Mentions: We next asked whether links in our single-cell expression-derived network models can reveal direct regulatory interactions. To provide support for our model, we identified high-confidence gene regulatory regions in the gene loci of the 20 TFs in our network by interrogating a compendium of TF ChIP-seq data from haematopoietic cell types29, followed by identification of binding sites for the 20 TFs within these regions (Supplementary Fig. 10). 27 of the 39 Boolean rules (70%) are supported by the presence of evolutionarily highly conserved motifs for the upstream regulators in the target gene locus (Supplementary Table 2), with support for at least one Boolean rule for 16/20 TFs. This finding suggested that many of the regulatory interactions proposed in our model may be direct upstream regulator/ downstream target gene relationships. To provide further validation, we focused on Erg, which our models predicted is activated by Sox17, or by Hoxb4 in combination with Lyl1 or Scl (Tal1). By analyzing a Hoxb4 ChIP-Seq dataset30, we showed that Hoxb4 can bind to the Erg+85kb enhancer (Supplementary Fig. 11a), which we previously showed to be active in blood stem and progenitor cells31,32. Moreover, comparative sequence analysis revealed that the Erg+85kb contains highly conserved Hox and Sox binding sites (Fig. 4a).


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

Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC, Buettner F, Macaulay IC, Jawaid W, Diamanti E, Nishikawa S, Piterman N, Kouskoff V, Theis FJ, Fisher J, Göttgens B - Nat. Biotechnol. (2015)

Network analysis predicts transcriptional interactions(a) Alignment of mammalian Erg+85 enhancer. Hox sites, red. Sox sites, light blue. (b) Percentage of Flk1+CD41−, Flk1+CD41+ and Flk1−CD41+ cells on days 3-7 of differentiation expressing YFP. Data are mean and s.e.m of triplicate differentiations of 2-3 clones per construct. P-values are reported in Supplementary Table 6.
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4374163&req=5

Figure 4: Network analysis predicts transcriptional interactions(a) Alignment of mammalian Erg+85 enhancer. Hox sites, red. Sox sites, light blue. (b) Percentage of Flk1+CD41−, Flk1+CD41+ and Flk1−CD41+ cells on days 3-7 of differentiation expressing YFP. Data are mean and s.e.m of triplicate differentiations of 2-3 clones per construct. P-values are reported in Supplementary Table 6.
Mentions: We next asked whether links in our single-cell expression-derived network models can reveal direct regulatory interactions. To provide support for our model, we identified high-confidence gene regulatory regions in the gene loci of the 20 TFs in our network by interrogating a compendium of TF ChIP-seq data from haematopoietic cell types29, followed by identification of binding sites for the 20 TFs within these regions (Supplementary Fig. 10). 27 of the 39 Boolean rules (70%) are supported by the presence of evolutionarily highly conserved motifs for the upstream regulators in the target gene locus (Supplementary Table 2), with support for at least one Boolean rule for 16/20 TFs. This finding suggested that many of the regulatory interactions proposed in our model may be direct upstream regulator/ downstream target gene relationships. To provide further validation, we focused on Erg, which our models predicted is activated by Sox17, or by Hoxb4 in combination with Lyl1 or Scl (Tal1). By analyzing a Hoxb4 ChIP-Seq dataset30, we showed that Hoxb4 can bind to the Erg+85kb enhancer (Supplementary Fig. 11a), which we previously showed to be active in blood stem and progenitor cells31,32. Moreover, comparative sequence analysis revealed that the Erg+85kb contains highly conserved Hox and Sox binding sites (Fig. 4a).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK. [2] Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.

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.

Show MeSH
Related in: MedlinePlus