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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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Diffusion plots identify developmental trajectoriesDiffusion plot of all 3934 cells calculated from the expression of 33 TFs and seven marker genes (top left). Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−. The expression levels of individual genes were then overlaid onto the diffusion plot to highlight patterns of expression (see Supplementary Fig. 5 for additional genes). Circle, PS; diamond, NP; triangle, HF; cross, 4SG; square, 4SFG− (visible in high resolution version of figure).
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Figure 2: Diffusion plots identify developmental trajectoriesDiffusion plot of all 3934 cells calculated from the expression of 33 TFs and seven marker genes (top left). Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−. The expression levels of individual genes were then overlaid onto the diffusion plot to highlight patterns of expression (see Supplementary Fig. 5 for additional genes). Circle, PS; diamond, NP; triangle, HF; cross, 4SG; square, 4SFG− (visible in high resolution version of figure).

Mentions: To identify and visualize putative developmental trajectories from the PS to 4S stages in the single-cell gene expression data, we developed a computational approach for dimension reduction (Materials and Methods). Our method is based on the concept of diffusion distances which can be interpreted as a metric for objects (here: cells) which are related to each other via a gradual but stochastic diffusion-like process such as cellular differentiation. In brief, similarities between all 3,934 cells are calculated based on their gene expression patterns, and then visualized globally in a 3D map (Fig. 2, Supplementary Fig. 5). The resultant components span a low-dimensional diffusion-space in which distance reflects how similar cells are in terms of their diffusion distance and can be inferred to represent developmental time.


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)

Diffusion plots identify developmental trajectoriesDiffusion plot of all 3934 cells calculated from the expression of 33 TFs and seven marker genes (top left). Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−. The expression levels of individual genes were then overlaid onto the diffusion plot to highlight patterns of expression (see Supplementary Fig. 5 for additional genes). Circle, PS; diamond, NP; triangle, HF; cross, 4SG; square, 4SFG− (visible in high resolution version of figure).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Diffusion plots identify developmental trajectoriesDiffusion plot of all 3934 cells calculated from the expression of 33 TFs and seven marker genes (top left). Blue, PS; green, NP; orange, HF; red, 4SG; purple, 4SFG−. The expression levels of individual genes were then overlaid onto the diffusion plot to highlight patterns of expression (see Supplementary Fig. 5 for additional genes). Circle, PS; diamond, NP; triangle, HF; cross, 4SG; square, 4SFG− (visible in high resolution version of figure).
Mentions: To identify and visualize putative developmental trajectories from the PS to 4S stages in the single-cell gene expression data, we developed a computational approach for dimension reduction (Materials and Methods). Our method is based on the concept of diffusion distances which can be interpreted as a metric for objects (here: cells) which are related to each other via a gradual but stochastic diffusion-like process such as cellular differentiation. In brief, similarities between all 3,934 cells are calculated based on their gene expression patterns, and then visualized globally in a 3D map (Fig. 2, Supplementary Fig. 5). The resultant components span a low-dimensional diffusion-space in which distance reflects how similar cells are in terms of their diffusion distance and can be inferred to represent developmental time.

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