br Materials and methods br Results br Discussion Our result

Materials and methods
Results
Discussion Our results show the potential of H2A.Z to provide a highly specific biomarker for identifying azidothymidine Supplier in tissues that are undergoing two tightly linked processes, deterministic asymmetric self-renewal and non-random segregation (Rambhatla et al., 2005; Huh et al., 2011), which specify DSCs. The key evidence for this conclusion is based on the cell culture data presented with expanded HFSCs. We have shown previously that these expanded cell strains exhibit many of the unique properties of DSCs (Huh et al., 2011; Huh and Sherley, 2011). These include long-term symmetric self-renewal; asymmetric self-renewal with production of multi-lineage differentiated cells based on loss of Lgr5 expression and expression of K5, K10, or filaggrin; expression of Lgr5, which is asymmetrically limited to the stem cell sister during asymmetric self-renewal divisions; and non-random chromosome segregation (Huh et al., 2011; Huh and Sherley, 2011). In addition, we show several additional examples of H2A.Z asymmetry detection in murine and human cell types, both in cell culture and in tissue sections, which are consistent with the specific detection of asymmetrically self-renewing DSCs. Supporting a general functional role for H2A.Z in DSC function in many human tissues, we found that in human cancer cell lines of diverse tissue origin higher expression of H2A.Z mRNA was significantly correlated with higher expression of IMPDH II mRNA (Supplementary Fig. S4). Up-regulation of IMPDH II is predicted to shift tissue DSCs from asymmetric self-renewal to symmetric self-renewal, with loss of non-random segregation, constituting an important carcinogenic mechanism (Rambhatla et al., 2005; Huh et al., 2011). Whereas H2A.Z is predicted to be a highly specific biomarker for DSCs, its sensitivity for detecting DSCs may vary from tissue to tissue because of differences in DSC self-renewal kinetics programs. DSCs undergoing symmetric self-renewal with random sister chromatid segregation would not be detected. Such self-renewal pattern excursions by deterministic asymmetrically self-renewing DSCs are predicted to be limited to periods of tissue mass expansion (e.g., during adult maturation) and repair of injuries. Quiescent DSCs might also go undetected, if they are arrested without H2A.Z asymmetry (Li and Clevers, 2010). This possibility might be evaluated in cell culture studies. A third possible cause of lower sensitivity awaits more extensive tissue investigations with the biomarker. An ongoing discussion in tissue stem cell biology is the balance of deterministic asymmetric self-renewal programs, reported here, compared to stochastic asymmetric self-renewal programs employed by DSCs in mammalian tissues (Loeffler and Potten, 1997; Enver et al., 1998; Klein and Simons, 2011). In stochastic programs for asymmetric self-renewal by DSCs, tissue renewal is accomplished with nearly equivalent frequencies of symmetric self-renewal divisions and divisions that give rise to one or two lineage-committed cells. If tissues were renewing primarily by stochastic asymmetric self-renewal programs, H2A.Z asymmetry might have low sensitivity for detecting DSCs. However, this detection shortcoming would still serve to advance knowledge of the cell kinetics properties of DSCs in diverse mammalian tissues. The identification of H2A.Z, a down-regulated gene, by the sparse feature approach underlines how this method can detect genes of unique biological significance that would often be overlooked. A down-regulated protein would hardly be considered as a practical expression biomarker candidate in the absence of any other distinguishing properties. Conceptually, the quantitative extraction of H2A.Z from the complex 16-dimensional data set by the sparse feature analysis reflects the detection of a consistent relationship of H2A.Z mRNA expression level and expression variance to two biologically linked cellular phenotypes (i.e., asymmetric self-renewal versus symmetric self-renewal) over an experimentally devised varying cellular landscape. Although conventional bioinformatics methods also identified H2A.Z, it was a relatively low priority member of a much longer list of candidate genes. Of the 930 genes identified as being up-regulated or down-regulated with respect to self-renewal pattern in the previous work (Noh et al., 2011), H2A.Z appeared towards the bottom of the list sorted by expression fold ratio and would normally be overlooked. In contrast, the sparse feature selection method identified just a handful of candidate genes among which H2A.Z was the most prominent. This capability will undoubtedly be very powerful in many other types of investigations in which gene expression is evaluated with respect to specific physiological conditions of biomedical interest. As an additional proof of concept, we very recently used the same sparse feature selection approach to identify genes implicated in a novel mechanism for the strontium-induced differentiation of mesenchymal stem cells down the osteogenic pathway (Autefage et al., 2015).