Pluripotent stem cells are able to self-renew, and to differentiate into all adult cell types. Many studies report data describing these cells, and characterize them in molecular terms. Machine learning yields classifiers that can accurately identify pluripotent stem cells, but there is a lack of studies yielding minimal sets of best biomarkers (genes/features). We assembled gene expression data of pluripotent stem cells and non-pluripotent cells from the mouse. After normalization and filtering, we applied machine learning, classifying samples into pluripotent and non-pluripotent with high cross-validated accuracy. Furthermore, to identify minimal sets of best biomarkers, we used three methods: information gain, random forests and a wrapper of genetic algorithm and support vector machine (GA/SVM). We demonstrate that the GA/SVM biomarkers work best in combination with each other; pathway and enrichment analyses show that they cover the widest variety of processes implicated in pluripotency. The GA/SVM wrapper yields best biomarkers, no matter which classification method is used. The consensus best biomarker based on the three methods is Tet1, implicated in pluripotency just recently. The best biomarker based on the GA/SVM wrapper approach alone is Fam134b, possibly a missing link between pluripotency and some standard surface markers of unknown function processed by the Golgi apparatus.

Much of embryonic stem cell biology has focused on transcriptional expression and regulation of genes that could mediate its unique potential in self-renewal or pluripotency. In alignment with our present understanding on the genetic, protein, and epigenetic factors that may direct cell fate, we present a short overview of the often overlooked contribution of alternative splice variants to regulatory diversity. Progressing beyond the limitations of a fixed genomic sequence, alternative splicing offers an additional layer of complexity to produce protein variants that may differ in function and localization that can direct embryonic stem cells to specific differentiation pathways. In light of the number of variants that can be produced at key ES cell genes alone, it is challenging to consider how much more multifaceted transcriptional regulation truly is, and if this can be captured more fully in future works.