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

• Published in: Nature biotechnology 2015-03, Vol.33 (3), p.269-276
• Main Authors: Moignard, Victoria, Woodhouse, Steven, Haghverdi, Laleh, Lilly, Andrew J, Tanaka, Yosuke, Wilkinson, Adam C, Buettner, Florian, Macaulay, Iain C, Jawaid, Wajid, Diamanti, Evangelia, Nishikawa, Shin-Ichi, Piterman, Nir, Kouskoff, Valerie, Theis, Fabian J, Fisher, Jasmin, Göttgens, Berthold
• Format: Article
• Language: English
• Subjects: 13; 13/106; 13/31; 38; 38/77; 631/136/232; 631/337/572; 631/553/2711; Agriculture; Animals; Base Sequence; Bioinformatics; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Blood; Blood Cells - metabolism; Blood flow; Cells; Computer Simulation; Diffusion; Embryos; Female; Gastrulation; Gene expression; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Genetic aspects; Genetic research; Life Sciences; Male; Mice, Inbred ICR; Models, Genetic; Molecular biology; Molecular Sequence Data; Single-Cell Analysis - methods; Transcription, Genetic
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/nbt.3154

An early stage in mouse blood development is reconstructed from gene expression data on thousands of single cells. 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.