scholarly journals Embryonic stem cell-specific signatures in cancer: insights into genomic regulatory networks and implications for medicine

10.1186/gm291 ◽  
2011 ◽  
Vol 3 (11) ◽  
pp. 75 ◽  
Author(s):  
Jonghwan Kim ◽  
Stuart H Orkin
Keyword(s):  
Stem Cell ◽  
Embryonic Stem Cell ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Genomic Regulatory Networks

scholarly journals Analysis of the mouse embryonic stem cell regulatory networks obtained by ChIP-chip and ChIP-PET

2008 ◽  
Vol 9 (8) ◽  
pp. R126 ◽  
Author(s):  
Divya Mathur ◽  
Timothy W Danford ◽  
Laurie A Boyer ◽  
Richard A Young ◽  
David K Gifford ◽  
...  
Keyword(s):  
Stem Cell ◽  
Embryonic Stem Cell ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Mouse Embryonic Stem Cell ◽  
Chip Chip

scholarly journals Gene network reconstruction using single cell transcriptomic data reveals key factors for embryonic stem cell differentiation

2019 ◽  
Author(s):  
Junil Kim ◽  
Simon Toftholm Jakobsen ◽  
Kedar Nath Natarajan ◽  
Kyoung Jae Won
Keyword(s):  
Stem Cell ◽  
Cell Differentiation ◽  
Embryonic Stem Cell ◽  
Single Cell ◽  
Regulatory Networks ◽  
Target Genes ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation ◽  
Expression Data

ABSTRACTGene expression data has been widely used to infer gene regulatory networks (GRNs). Recent single-cell RNA sequencing (scRNAseq) data, containing the expression information of the individual cells (or status), are highly useful in blindly reconstructing regulatory mechanisms. However, it is still not easy to understand transcriptional cascade from large amount of expression data. Besides, the reconstructed networks may not capture the major regulatory rules.Here, we propose a novel approach called TENET to reconstruct the GRNs from scRNAseq data by calculating causal relationships between genes using transfer entropy (TE). We show that known target genes have significantly higher TE values. Genes with higher TE values were more affected by various perturbations. Comprehensive benchmarking showed that TENET outperformed other GRN prediction algorithms. More importantly, TENET is uniquely capable of identifying key regulators. Applying TENET to scRNAseq during embryonic stem cell differentiation to neural cells, we show that Nme2 is a critical factor for 2i condition specific stem cell self-renewal.

The expression of mesenchymal and embryonic stem cell markers by human limbal Stromal cells

2011 ◽  
Author(s):  
Moon Nian Lim ◽  
Umapathy Thiageswari ◽  
Othman Ainoon ◽  
P. J. N. Baharuddin ◽  
R. A. Jamal ◽  
...  
Keyword(s):  
Stem Cell ◽  
Embryonic Stem Cell ◽  
Stromal Cells ◽  
Embryonic Stem ◽  
Stem Cell Markers ◽  
Cell Markers ◽  
Embryonic Stem Cell Markers

Transplantation of embryonic stem cell-derived cardiomyocytes

2004 ◽  
Vol 52 (S 1) ◽  
Author(s):  
W R�ll ◽  
E Kolossov ◽  
O Rubentschik ◽  
L Rochlin ◽  
C B�ttinger ◽  
...  
Keyword(s):  
Stem Cell ◽  
Embryonic Stem Cell ◽  
Embryonic Stem

IGF1-mediated human embryonic stem cell self-renewal recapitulates the embryonic niche

2020 ◽  
Author(s):  
Wamaitha SE ◽  
Grybel KJ ◽  
Alanis-Lobato G ◽  
Gerri C ◽  
Ogushi S ◽  
...  
Keyword(s):  
Stem Cell ◽  
Embryonic Stem Cell ◽  
Human Embryonic Stem Cell ◽  
Embryonic Stem ◽  
Self Renewal
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