scholarly journals Stage prediction of embryonic stem cell differentiation from genome-wide expression data

2011 ◽  
Vol 27 (18) ◽  
pp. 2546-2553 ◽  
Author(s):  
Lan Zagar ◽  
Francesca Mulas ◽  
Silvia Garagna ◽  
Maurizio Zuccotti ◽  
Riccardo Bellazzi ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cell Differentiation ◽  
Embryonic Stem Cell ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation ◽  
Expression Data ◽  
Genome Wide ◽  
Genome Wide Expression

scholarly journals A Genome-Wide RNAi Screen Reveals MAP Kinase Phosphatases as Key ERK Pathway Regulators during Embryonic Stem Cell Differentiation

PLoS Genetics ◽  
2012 ◽  
Vol 8 (12) ◽  
pp. e1003112 ◽  
Author(s):  
Shen-Hsi Yang ◽  
Tuzer Kalkan ◽  
Claire Morrisroe ◽  
Austin Smith ◽  
Andrew D. Sharrocks
Keyword(s):  
Stem Cell ◽  
Cell Differentiation ◽  
Embryonic Stem Cell ◽  
Map Kinase ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation ◽  
Erk Pathway ◽  
Genome Wide ◽  
A Genome

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.

scholarly journals Genome-wide reduction in H3K9 acetylation during human embryonic stem cell differentiation

2009 ◽  
Vol 219 (3) ◽  
pp. 677-687 ◽  
Author(s):  
Jana Krejčí ◽  
Radka Uhlířová ◽  
Gabriela Galiová ◽  
Stanislav Kozubek ◽  
Jana Šmigová ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cell Differentiation ◽  
Embryonic Stem Cell ◽  
Human Embryonic Stem Cell ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation ◽  
Genome Wide ◽  
H3k9 Acetylation

scholarly journals Identification of cardiac stem cells with FLK1, CD31, and VE‐cadherin expression during embryonic stem cell differentiation

2005 ◽  
Vol 19 (3) ◽  
pp. 371-378 ◽  
Author(s):  
Midori Iida ◽  
Toshio Heike ◽  
Momoko Yoshimoto ◽  
Shiro Baba ◽  
Hiraku Doi ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Cell Differentiation ◽  
Embryonic Stem Cell ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation ◽  
Cardiac Stem Cells
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