PLoS Computational Biology (Dec 2009)

Dissecting early differentially expressed genes in a mixture of differentiating embryonic stem cells.

  • Feng Hong,
  • Fang Fang,
  • Xuming He,
  • Xiaoyi Cao,
  • Hiram Chipperfield,
  • Dan Xie,
  • Wing H Wong,
  • Huck H Ng,
  • Sheng Zhong

DOI
https://doi.org/10.1371/journal.pcbi.1000607
Journal volume & issue
Vol. 5, no. 12
p. e1000607

Abstract

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The differentiation of embryonic stem cells is initiated by a gradual loss of pluripotency-associated transcripts and induction of differentiation genes. Accordingly, the detection of differentially expressed genes at the early stages of differentiation could assist the identification of the causal genes that either promote or inhibit differentiation. The previous methods of identifying differentially expressed genes by comparing different cell types would inevitably include a large portion of genes that respond to, rather than regulate, the differentiation process. We demonstrate through the use of biological replicates and a novel statistical approach that the gene expression data obtained without prior separation of cell types are informative for detecting differentially expressed genes at the early stages of differentiation. Applying the proposed method to analyze the differentiation of murine embryonic stem cells, we identified and then experimentally verified Smarcad1 as a novel regulator of pluripotency and self-renewal. We formalized this statistical approach as a statistical test that is generally applicable to analyze other differentiation processes.