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

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    Authors
    Moignard, V
    Woodhouse, S
    Haghverdi, L
    Lilly, A J
    Tanaka, Y
    Wilkinson, A
    Buettner, F
    Macaulay, I
    Jawaid, W
    Diamanti, E
    Nishikawa, S
    Piterman, N
    Kouskoff, Valerie
    Theis, F
    Fisher, J
    Göttgens, B
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    Affiliation
    Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
    Issue Date
    2015-02-09
    
    Metadata
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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.
    Citation
    Decoding the regulatory network of early blood development from single-cell gene expression measurements. 2015: Nat Biotechnol
    Journal
    Nature Biotechnology
    URI
    http://hdl.handle.net/10541/345309
    DOI
    10.1038/nbt.3154
    PubMed ID
    25664528
    Type
    Article
    Language
    en
    ISSN
    1546-1696
    ae974a485f413a2113503eed53cd6c53
    10.1038/nbt.3154
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research

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