Integrated analysis of single-cell embryo data yields a unified transcriptome signature for the human pre-implantation epiblast.
Development (Cambridge, England)
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Stirparo, G., Boroviak, T., Guo, G., Nichols, J., Smith, A., & Bertone, P. (2018). Integrated analysis of single-cell embryo data yields a unified transcriptome signature for the human pre-implantation epiblast.. Development (Cambridge, England), 145 (3)https://doi.org/10.1242/dev.158501
Single-cell profiling techniques create opportunities to delineate cell fate progression in mammalian development. Recent studies provide transcriptome data from human preimplantation embryos, in total comprising nearly 2000 individual cells. Interpretation of these data is confounded by biological factors such as variable embryo staging and cell-type ambiguity, as well as technical challenges in the collective analysis of datasets produced with different sample preparation and sequencing protocols. Here we address these issues to assemble a complete gene expression time course spanning human preimplantation embryogenesis. We identify key transcriptional features over developmental time and elucidate lineage-specific regulatory networks. We resolve post hoc cell-type assignment in the blastocyst, and define robust transcriptional prototypes that capture epiblast and primitive endoderm lineages. Examination of human pluripotent stem cell transcriptomes in this framework identifies culture conditions that sustain a naïve state pertaining to the inner cell mass. Our approach thus clarifies understanding both of lineage segregation in the early human embryo and of in vitro stem cell identity, and provides an analytical resource for comparative molecular embryology.
Cell Line, Pluripotent Stem Cells, Blastocyst, Germ Layers, Animals, Primates, Humans, Genetic Markers, Embryo Culture Techniques, Chromosome Mapping, Gene Expression Profiling, Cell Lineage, Embryonic Development, Blastocyst Inner Cell Mass, Single-Cell Analysis
This work was supported by UK Biotechnology and Biological Sciences Research Council (BBSRC) research grant RG53615, UK Medical Research Council (MRC) programme grant G1001028, and institutional funding from the MRC and Wellcome Trust. AS is an MRC Professor.
Wellcome Trust (097922/Z/11/Z)
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External DOI: https://doi.org/10.1242/dev.158501
This record's URL: https://www.repository.cam.ac.uk/handle/1810/273850
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/
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