Bipotent mesendoderm that can give rise to both endoderm and mesoderm is an established entity from C. elegans to zebrafish. Although previous studies in mouse embryo indicated the presence of bi-potent mesendoderm cells in the organizer region, characterization of mesendoderm and its differentiation processes are still unclear. As bi-potent mesendoderm is implicated as the major precursor of definitive endoderm, its identification is also essential for exploring the differentiation of definitive endoderm. In this study, we have established embryonic stem (ES) cell lines that carry GFP gene in the goosecoid (Gsc) gene locus and have investigated the differentiation course of mesendodermal cells using Gsc expression as a marker. Our results show that mesendoderm is represented as a Gsc-GFP⁺E-cadherin(ECD)⁺PDGFRα(αR)⁺ population and is selectively induced from ES cells under defined conditions containing either activin or nodal. Subsequently, it diverges to Gsc⁺ECD⁺αR^- and Gsc⁺ECD^-αR⁺ intermediates that eventually differentiate into definitive endoderm and mesodermal lineages, respectively. The presence of mesendodermal cells in nascent Gsc⁺ECD⁺αR⁺ population was also confirmed by single cell analysis. Finally, we show that the defined culture condition and surface markers developed in this study are applicable for obtaining pure mesendodermal cells and their immediate progenies from genetically unmanipulated ES cells.

Although many mutations contributing to antibiotic resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to be fully elucidated. To better characterize phenotype–genotype mapping for drug resistance, here we analyse phenotypic and genotypic changes of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution. We demonstrate that the resistances can be quantitatively predicted by the expression changes of a small number of genes. Several candidate mutations contributing to the resistances are identified, while phenotype–genotype mapping is suggested to be complex and includes various mutations that cause similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances.

Using data from gene expression databases on various organisms and tissues, including yeast, nematodes, human normal and cancer tissues, and embryonic stem cells, we found that the abundances of expressed genes exhibit a power-law distribution with an exponent close to $-1$; i.e., they obey Zipf’s law. Furthermore, by simulations of a simple model with an intracellular reaction network, we found that Zipf’s law of chemical abundance is a universal feature of cells where such a network optimizes the efficiency and faithfulness of self-reproduction. These findings provide novel insights into the nature of the organization of reaction dynamics in living cells.

Stem cells do not all respond the same way, but the mechanisms underlying this heterogeneity are not well understood. Here, we found that expression of Hes1 and its downstream genes oscillate in mouse embryonic stem (ES) cells. Those expressing low and high levels of Hes1 tended to differentiate into neural and mesodermal cells, respectively. Furthermore, inactivation of Hes1 facilitated neural differentiation more uniformly at earlier time. Thus, Hes1-null ES cells display less heterogeneity in both the differentiation timing and fate choice, suggesting that the cyclic gene Hes1 contributes to heterogeneous responses of ES cells even under the same environmental conditions.

The discovery of two fundamental laws concerning cellular dynamics with recursive growth is reported. Firstly, the chemical abundances measured over many cells were found to obey a log-normal distribution and secondly, the relationship between the average and standard deviation of the abundances was found to be linear. The ubiquity of these laws was explored both theoretically and experimentally. By means of a model with a catalytic reaction network, the laws were shown to exist near a critical state with efficient self-reproduction. Additionally, by measuring distributions of fluorescent proteins in bacteria cells, the ubiquity of log-normal distribution of protein abundances was confirmed. Relevance of these findings to cellular function and biological plasticity is briefly discussed.

During development, cells undergo a unidirectional course of differentiation that progressively decreases the number of cell types they can potentially become. Stem cells, however, keep their potential to both proliferate and differentiate. A very important issue then is to understand the characteristics that distinguish stem cells from other cell types and allow them to conduct stable proliferation and differentiation. Here, we review relevant dynamical-systems approaches to describe the state transition between stem and differentiated cells, with an emphasis on fluctuating and oscillatory gene expression levels, as these represent the specific properties of stem cells. Relevance between recent experimental results and dynamical-systems descriptions of stem cell differentiation is also discussed.