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Article

Cell-Cycle Control of Developmentally Regulated Transcription Factors Accounts for Heterogeneity in Human Pluripotent Cells

by Amar M. Singh; James Chappell; Robert Trost; Li Lin; Tao Wang; Jie Tang; Hao Wu; Shaying Zhao; Peng Jin; Stephen Dalton

2013

Subjects
  • Chemistry, Biochemistry
  • Biology, Genetics
  • Biology, Bioinformatics
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Abstract:Close

Heterogeneity within pluripotent stem cell (PSC) populations is indicative of dynamic changes that occur when cells drift between different states. Although the role of metastability in PSCs is unclear, it appears to reflect heterogeneity in cell signaling. Using the Fucci cell-cycle indicator system, we show that elevated expression of developmental regulators in G1 is a major determinant of heterogeneity in human embryonic stem cells. Although signaling pathways remain active throughout the cell cycle, their contribution to heterogeneous gene expression is restricted to G1. Surprisingly, we identify dramatic changes in the levels of global 5-hydroxymethylcytosine, an unanticipated source of epigenetic heterogeneity that is tightly linked to cell-cycle progression and the expression of developmental regulators. When we evaluated gene ex pression in differentiating cells, we found that cell-cycle regulation of developmental regulators was maintained during lineage specification. Cell-cycle regulation of developmentally regulated transcription factors is therefore an inherent feature of the mechanisms underpinning differentiation.

Article

Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates

by Hao Wu; Tianlei Xu; Hao Feng; Li Chen; Ben Li; Bing Yao; Zhaohui Qin; Peng Jin; Karen Conneely

2015

Subjects
  • Biology, Biostatistics
  • Biology, Bioinformatics
  • Biology, Genetics
  • File Download
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Abstract:Close

DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Recent developments in whole genome bisulfite sequencing (WGBS) technology have enabled genome-wide measurements of DNA methylation at single base pair resolution. Many experiments have been conducted to compare DNA methylation profiles under different biological contexts, with the goal of identifying differentially methylated regions (DMRs). Due to the high cost of WGBS experiments, many studies are still conducted without biological replicates. Methods and tools available for analyzing such data are very limited.We develop a statistical method, DSS-single, for detecting DMRs from WGBS data without replicates. We characterize the count data using a rigorous model that accounts for the spatial correlation of methylation levels, sequence depth and biological variation. We demonstrate that using information from neighboring CG sites, biological variation can be estimated accurately even without replicates. DMR detection is then carried out via a Wald test procedure. Simulations demonstrate that DSS-single has greater sensitivity and accuracy than existing methods, and an analysis of H1 versus IMR90 cell lines suggests that it also yields the most biologically meaningful results. DSS-single is implemented in the Bioconductor package DSS.
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