Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells

By Liying Yan, Ming Yang, Hongshan Guo, Lu Yang, Jun Wu, Rong Li, Ping Liu, Y. Lian, Xiao-ying Zheng, Jie Yan and 8 more. Nature Structural &Molecular Biology, 2013

1,581 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/173b1fbce0c93e0afd20226751a00d113154f864

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Citations in the corpus

Identification of cell types from single-cell transcriptomes using a novel clustering method (2015, methodology): “We tested SNN-Cliq for such capability using three single-cell RNA-Seq datasets generated by different techniques in a variety of cell types in human and mouse (Deng et al. , 2014; Ramsko¨ld et al. , 2012; Yan et al. , 2013).”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, background): “Therefore, a major use of scRNA-seq has been to assess transcriptional similarities and differences within a population of cells, with early reports revealing previously unappreciated levels of heterogeneity, for example in embryonic and immune cells [9, 10, 17].”
Proliferation, survival and metabolism: the role of PI3K/AKT/mTOR signalling in pluripotency and cell fate determination (2016, background): “…periods beyond implantation (Deglincerti et al., 2016; Shahbazi et al., 2016) and single-cell gene expression profiling (Petropoulos et al., 2016; Yan et al., 2013) represent exciting avenues through which we may further improve defined culture conditions and explore the species-specific impact…”
Single-cell RNA-seq: advances and future challenges (2014, positive): “…major changes in mRNA isoform abundance and defined patterns of allele-specific gene expression during murine blastomere development (93,142), functional modules of co-regulated genes (141), and the first lncRNA expression maps of embryonic stem cells (ESCs) and human preimplantation embryos (140).”
Computational assignment of cell-cycle stage from single-cell transcriptome data. (2015, neutral): “The application of scRNA-Seq to a wide range of different systems has already resulted in new insights in important areas such as embryogenesis [3] and tissue heterogeneity [4].”
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (2015, methodology): “We also applied scLVM to other single-cell RNA-seq data sets, including 34 human embryonic stem cells and a set of 90 cells from human preimplantation embryos 15 , which confirmed that the cell cycle explains substantial proportions of the variability in other contexts.”
KRAB zinc finger proteins (2017, background): “…the tightly orchestrated transcription of TEcontaining loci during this period (Corsinotti et al., 2013; Theunissen et al., 2016; Macfarlan et al., 2012; Fort et al., 2014; Gifford et al., 2013; Goke et al., 2015; Grow et al., 2015; Kunarso et al., 2010; Xue et al., 2013; Yan et al., 2013).”