Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process.

In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a "simple" program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.

Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts.

BACKGROUND: A number of studies have established that stochasticity in gene expression may play an important role in many biological phenomena. This therefore calls for further investigations to identify the molecular mechanisms at stake, in order to understand and manipulate cell-to-cell variability. In this work, we explored the role played by chromatin dynamics in the regulation of stochastic gene expression in higher eukaryotic cells. RESULTS: For this purpose, we generated isogenic chicken-cell populations expressing a fluorescent reporter integrated in one copy per clone. Although the clones differed only in the genetic locus at which the reporter was inserted, they showed markedly different fluorescence distributions, revealing different levels of stochastic gene expression. Use of chromatin-modifying agents showed that direct manipulation of chromatin dynamics had a marked effect on the extent of stochastic gene expression. To better understand the molecular mechanism involved in these phenomena, we fitted these data to a two-state model describing the opening/closing process of the chromatin. We found that the differences between clones seemed to be due mainly to the duration of the closed state, and that the agents we used mainly seem to act on the opening probability. CONCLUSIONS: In this study, we report biological experiments combined with computational modeling, highlighting the importance of chromatin dynamics in stochastic gene expression. This work sheds a new light on the mechanisms of gene expression in higher eukaryotic cells, and argues in favor of relatively slow dynamics with long (hours to days) periods of quiet state.

Modeling embryogenesis and cancer: an approach based on an equilibrium between the autostabilization of stochastic gene expression and the interdependence of cells for proliferation.

A large amount of data demonstrating the stochastic nature of gene expression and cell differentiation has accumulated during the last 40 years. These data suggest that a gene in a cell always has a certain probability of being activated at any time and that instead of leading to on and off switches in an all-or-nothing fashion, the concentration of transcriptional regulators increases or decreases this probability. In order to integrate these data in an appropriate theoretical frame, we have tested the relevance of the selective model of cell differentiation by computer simulation experiments. This model is based on stochastic gene expression controlled by cellular interactions. Our results show that it is readily able to produce tissue organization. A model involving only two cells generated a bi-layer cellular structure of finite growth. Cell death was not a drawback but an advantage because it improved the viability of this bi-layer structure. However, our results also show that cellular interactions cannot be simply based on raw selection between cells. Instead, tissue coordination includes at least two basic components: phenotypic autostabilization (differentiated cells stabilize their own phenotype) and interdependence for proliferation (differentiated cells stimulate the proliferation of alien phenotypes). In this modified autostabilization-selection model, cellular organization and growth arrest result from a quantitative equilibrium between the parameters controlling these two processes. An imbalance leads to tissue disorganization and invasive cancer-like growth. These findings suggest that cancer does not result solely from mutations in the cancerous cell but from the progressive addition of several small alterations of the equilibrium between autostabilization and interdependence for proliferation. In this frame, it is not solely the cancerous cell that is abnormal. The whole organism is involved. Tumor growth is a local effect of an imbalance between all the factors involved in tissue organization.

Stochastic fluctuations and distributed control of gene expression impact cellular memory.

Despite the stochastic noise that characterizes all cellular processes the cells are able to maintain and transmit to their daughter cells the stable level of gene expression. In order to better understand this phenomenon, we investigated the temporal dynamics of gene expression variation using a double reporter gene model. We compared cell clones with transgenes coding for highly stable mRNA and fluorescent proteins with clones expressing destabilized mRNA-s and proteins. Both types of clones displayed strong heterogeneity of reporter gene expression levels. However, cells expressing stable gene products produced daughter cells with similar level of reporter proteins, while in cell clones with short mRNA and protein half-lives the epigenetic memory of the gene expression level was completely suppressed. Computer simulations also confirmed the role of mRNA and protein stability in the conservation of constant gene expression levels over several cell generations. These data indicate that the conservation of a stable phenotype in a cellular lineage may largely depend on the slow turnover of mRNA-s and proteins.

On the lack of specificity of proteins and its consequences for a theory of biological organization.

It is now widely recognized that gene expression and cellular processes include a probabilistic component. However, this does not essentially modify the theory of genetic programming. This stochastic aspect, which is called noise, is usually conceived as a margin of fluctuation in the way the genetic program functions and the latter remains understood as a specific mechanism guided by genetic information. In contrast, recent data show that proteins do not possess a high level of specificity. They can interact with numerous molecular partners. As a consequence molecular interactions are not simply "noisy". Because they are subject to large combinatorial interaction possibilities, they are also intrinsically stochastic and must be sorted out by the cell structure. This contradicts the genetic programming theory which is based on the idea that protein interactions are directed by their stereospecificity and genetic information. Taking into account the lack of protein specificity leads to a new theory. Natural selection acts not only in evolution but also in ontogenesis by sorting stochastic molecular interactions. In this frame, the making up of an organism, instead of being a simple bottom-top process in which information flows from genes to phenotypes, is both a bottom-top and top-bottom process. Genes provide proteins, but their stochastic interactions are sorted by selective constraints arising from the cell and multi-cellular structures, which are themselves subject to the action of natural selection.

Dynamic CpG and non-CpG methylation of the Peg1/Mest gene in the mouse oocyte and preimplantation embryo.

In somatic tissues, the CpG island of the imprinted Peg1/Mest gene is methylated on the maternal allele. We have examined the methylation of CpG and non-CpG sites of this differentially methylated CpG island in freshly ovulated oocytes, in vitro aged oocytes, and preimplantation embryos. The CpG methylation pattern was heterogeneous in freshly ovulated oocytes, despite the fact that they all were arrested in metaphase II. After short in vitro culture, Peg1/Mest became hypermethylated, whereas prolonged in vitro culture resulted in demethylation in a fraction of oocytes. Non-CpG methylation also occurred in a stage-specific manner. On alleles that were fully methylated at CpG sites, this modification was found, and it became reduced in two-cell stage embryos and blastocysts. These observations suggest that the process of establishment of the methylation imprint at this locus is more dynamic than previously thought.