Meta-Analysis of Differentiating Mouse Embryonic Stem Cell Gene Expression Kinetics Reveals Early Change of a Small Gene Set

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Abstract
Stem cell differentiation involves critical changes in gene expression. Identification of these should provide endpoints useful for optimizing stem cell propagation as well as potential clues about mechanisms governing stem cell maintenance. Here we describe the results of a new meta-analysis methodology applied to multiple gene expression datasets from three mouse embryonic stem cell (ESC) lines obtained at specific time points during the course of their differentiation into various lineages. We developed methods to identify genes with expression changes that correlated with the altered frequency of functionally defined, undifferentiated ESC in culture. In each dataset, we computed a novel statistical confidence measure for every gene which captured the certainty that a particular gene exhibited an expression pattern of interest within that dataset. This permitted a joint analysis of the datasets, despite the different experimental designs. Using a ranking scheme that favored genes exhibiting patterns of interest, we focused on the top 88 genes whose expression was consistently changed when ESC were induced to differentiate. Seven of these (103728_at, 8430410A17Rik, Klf2, Nr0b1, Sox2, Tcl1, and Zfp42) showed a rapid decrease in expression concurrent with a decrease in frequency of undifferentiated cells and remained predictive when evaluated in additional maintenance and differentiating protocols. Through a novel meta-analysis, this study identifies a small set of genes whose expression is useful for identifying changes in stem cell frequencies in cultures of mouse ESC. The methods and findings have broader applicability to understanding the regulation of self-renewal of other stem cell types. Stem cells are able to develop into many specialized cell types and thus have the potential to be used to repair or replace damaged cells. One of the challenges that scientists face is learning how to multiply these cells in vitro without loss of their stem cell properties. The development of more rapid assays for stem cells in cultured populations would significantly aid the optimization of culture conditions for stem cells. The authors propose an assay for mouse embryonic stem cells based on the expression change of seven marker genes and show that it can detect both increases and decreases in the frequency of stem cells. The assay was developed by analyzing three independent microarray datasets that ask similar biological questions but use different experimental designs. Gene expression profiles were identified within each dataset that exhibited patterns consistent with loss of stem cell properties, and, using a novel statistical measure, these profiles were compared between datasets in an unbiased fashion. A similar experimental design could be used to develop other stem cell population assays, and the analytical methods are adaptable to unrelated biological questions where analysis of a diverse set of microarray experiments is useful.