Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-08

AUTHORS

Zhigang Xue, Kevin Huang, Chaochao Cai, Lingbo Cai, Chun-yan Jiang, Yun Feng, Zhenshan Liu, Qiao Zeng, Liming Cheng, Yi E. Sun, Jia-yin Liu, Steve Horvath, Guoping Fan

ABSTRACT

Mammalian pre-implantation development is a complex process involving dramatic changes in the transcriptional architecture. We report here a comprehensive analysis of transcriptome dynamics from oocyte to morula in both human and mouse embryos, using single-cell RNA sequencing. Based on single-nucleotide variants in human blastomere messenger RNAs and paternal-specific single-nucleotide polymorphisms, we identify novel stage-specific monoallelic expression patterns for a significant portion of polymorphic gene transcripts (25 to 53%). By weighted gene co-expression network analysis, we find that each developmental stage can be delineated concisely by a small number of functional modules of co-expressed genes. This result indicates a sequential order of transcriptional changes in pathways of cell cycle, gene regulation, translation and metabolism, acting in a step-wise fashion from cleavage to morula. Cross-species comparisons with mouse pre-implantation embryos reveal that the majority of human stage-specific modules (7 out of 9) are notably preserved, but developmental specificity and timing differ between human and mouse. Furthermore, we identify conserved key members (or hub genes) of the human and mouse networks. These genes represent novel candidates that are likely to be key in driving mammalian pre-implantation development. Together, the results provide a valuable resource to dissect gene regulatory mechanisms underlying progressive development of early mammalian embryos. More... »

PAGES

593

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nature12364

DOI

http://dx.doi.org/10.1038/nature12364

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043470764

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23892778


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