The pluripotency and self-renewal capabilities, which define the "stemness" state, of mouse embryonic stem (ES) cells, are usually investigated by functional assays or quantitative measurements of the expression levels of known ES cell markers. Strong correlations between these expression levels and functional assays, particularly at the early stage of cell differentiation, have usually not been observed. An effective molecular diagnostic to properly identify the differentiation state of mouse ES cells, prior to further experimentation, is needed.
A novel molecular pattern recognition procedure has been developed to diagnose the differentiation state of ES cells. This is based on mRNA transcript levels of genes differentially expressed between ES cells and their differentiating progeny. Large publicly available ES cell data sets from various platforms were used to develop and test the diagnostic model. Signature patterns consisting of five gene expression levels achieved high accuracy at determining the cell state (sensitivity and specificity > 97%).
The effective ES cell state diagnostic scheme described here can be implemented easily to assist researchers in identifying the differentiation state of their cultures. It also provides a step towards standardization of experiments relying on cells being in the stem cell or differentiating state.