Gene expression variability as a unifying element of the pluripotency network

Mason, Elizabeth, Mar, Jessica C., Laslett, Andrew, Pera, Martin F., Quackenbush, John, Wolvetang, Ernst and Wells C. (2014) Gene expression variability as a unifying element of the pluripotency network. Stem Cell Reports, 3 2: 365-377. doi:10.1016/j.stemcr.2014.06.008

Author Mason, Elizabeth
Mar, Jessica C.
Laslett, Andrew
Pera, Martin F.
Quackenbush, John
Wolvetang, Ernst
Wells C.
Title Gene expression variability as a unifying element of the pluripotency network
Journal name Stem Cell Reports   Check publisher's open access policy
ISSN 2213-6711
Publication date 2014-08-12
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.stemcr.2014.06.008
Open Access Status DOI
Volume 3
Issue 2
Start page 365
End page 377
Total pages 13
Place of publication Cambridge, United States
Publisher Cell Press
Language eng
Abstract Heterogeneity is a hallmark of stem cell populations, in part due to the molecular differences between cells undergoing self-renewal and those poised to differentiate. We examined phenotypic and molecular heterogeneity in pluripotent stem cell populations, using public gene expression data sets. A high degree of concordance was observed between global gene expression variability and the reported heterogeneity of different human pluripotent lines. Network analysis demonstrated that low-variability genes were the most highly connected, suggesting that these are the most stable elements of the gene regulatory network and are under the highest regulatory constraints. Known drivers of pluripotency were among these, with lowest expression variability of POU5F1 in cells with the highest capacity for self-renewal. Variability of gene expression provides a reliable measure of phenotypic and molecular heterogeneity and predicts those genes with the highest degree of regulatory constraint within the pluripotency network. Gene expression variability is a simple measure of heterogeneity in stem cell populations and is informative about the behavior of pluripotency genes. Network analysis demonstrates that genes within the pluripotency network have distinct and characteristic variability profiles, such that genes highly connected in the network have the lowest variability. Wells and colleagues propose that this identifies genes that may be under the highest degree of regulatory constraint.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
Australian Institute for Bioengineering and Nanotechnology Publications
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