Jean-Jacques Kupiec

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

BackgroundA 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.ResultsFor 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.ConclusionsIn 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.

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.

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.

Toward a probabilistic biology

Stochastic gene expression (SGE) is now considered to be an established fact and has become an important subject of research (Vinuelas et al . 2012). During the last decade, the availability of new techniques has made possible the production of more precise and more spectacular data showing the extensive variability in gene expression occurring between individual cells. However, evidence supporting probabilistic models of cell behaviour and gene expression has been available for quite a long time – for example, see the reviews in Laforge et al . (2005) and Golubev (2010). It should be noted that the first model of stochastic cell differentiation was proposed in 1964 (Till et al . 1964), which is almost at the same time as the genetic programming theory (Jacob and Monod 1961), which is deterministic in nature. One can thus wonder why the determinist view of biology has remained dominant for such a long time, and what makes a probabilistic view more acceptable nowadays? In this short paper, I will briefly argue that there is a strong epistemological obstacle to the acceptance of probabilism in biology, and that even today SGE is not integrated into a fully probabilistic approach of cellular processes, but rather into a concept that I call ‘determinism with noise’. I will argue that a new fully probabilistic theoretical framework is needed to truly integrate the stochastic aspects of cell physiology.

Un mécanisme de hasard-sélection pourrait expliquer la différenciation de plusieurs types cellulaires.

Selon la theorie dite de hasard- selection, la differenciation cellulaire seffectue en deux phases a chaque stade du developpement. Durant la premiere phase, lexpression des genes est instable. Cette instabilite, provoquee par la diffusion des facteurs de regulation de lexpression des genes, produit differents types cellulaires. Durant la seconde phase, des interactions cellulaires activent des mecanismes qui limitent les possibilites de diffusion des facteurs de regulation et stabilisent ainsi lexpression des genes. Cependant, cette stabilisation ne peut se produire que si une combinaison adequate de types cellulaires est presente. Ce mecanisme de selection ordonne lexpression des genes et dirige lembryon vers le stade adulte. Un des mecanismes de stabilisation pourrait etre la phosphorylation de ces facteurs de regulation.