Distinct allelic patterns of nanog expression impart embryonic stem cell population heterogeneity

Stochastic reaction networks in dynamic compartment populations

Many biochemical processes in living systems take place in compartmentalized environments, where individual compartments can interact with each other and undergo dynamic remodeling. Studying such processes through mathematical models poses formidable challenges because the underlying dynamics involve a large number of states, which evolve stochastically with time. Here we propose a mathematical framework to study stochastic biochemical networks in compartmentalized environments. We develop a generic population model, which tracks individual compartments and their molecular composition. We then show how the time evolution of this system can be studied effectively through a small number of differential equations, which track the statistics of the population. Our approach is versatile and renders an important class of biological systems computationally accessible.

Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems, but a general and sufficiently effective approach remains lacking. In this work, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes, including subcellular compartmentalization and tissue homeostasis.

Nanog Dynamics in Mouse Embryonic Stem Cells: Results from Systems Biology Approaches

Mouse embryonic stem cells (mESCs), derived from the inner cell mass of the blastocyst, are pluripotent stem cells having self-renewal capability and the potential of differentiating into every cell type under the appropriate culture conditions. An increasing number of reports have been published to uncover the molecular mechanisms that orchestrate pluripotency and cell fate specification using combined computational and experimental methodologies. Here, we review recent systems biology approaches to describe the causes and functions of gene expression heterogeneity and complex temporal dynamics of pluripotency markers in mESCs under uniform culture conditions. In particular, we focus on the dynamics of Nanog, a key regulator of the core pluripotency network and of mESC fate. We summarize the strengths and limitations of different experimental and modeling approaches and discuss how various strategies could be used.

Increased culture density is linked to decelerated proliferation, prolonged G1 phase, and enhanced propensity for differentiation of self-renewing human pluripotent stem cells

Human pluripotent stem cells (hPSCs) display a very short G1 phase and rapid proliferation kinetics. Regulation of the cell cycle, which is linked to pluripotency and differentiation, is dependent on the stem cell environment, particularly on culture density. This link has been so far empirical and central to disparities in the growth rates and fractions of self-renewing hPSCs residing in different cycle phases. In this study, hPSC cycle progression in conjunction with proliferation and differentiation were comprehensively investigated for different culture densities. Cell proliferation decelerated significantly at densities beyond 50×10(4) cells/cm(2). Correspondingly, the G1 fraction increased from 25% up to 60% at densities greater than 40×10(4) cells/cm(2) while still hPSC pluripotency marker expression was maintained. In parallel, expression of the cycle inhibitor CDKN1A (p21) was increased, while that of p27 and p53 did not change significantly. After 4 days of culture in an unconditioned medium, greater heterogeneity was noted in the differentiation outcomes and was limited by reducing the density variation. A quantitative model was constructed for self-renewing and differentiating hPSC ensembles to gain a better understanding of the link between culture density, cycle progression, and stem cell state. Results for multiple hPSC lines and medium types corroborated experimental findings. Media commonly used for maintenance of self-renewing hPSCs exhibited the slowest kinetics of induction of differentiation (kdiff), while BMP4 supplementation led to 14-fold higher kdiff values. Spontaneous differentiation in a growth factor-free medium exhibited the largest variation in outcomes at different densities. In conjunction with the quantitative framework, our findings will facilitate rationalizing the selection of cultivation conditions for the generation of stem cell therapeutics.

Autocrine FGF feedback can establish distinct states of Nanog expression in pluripotent stem cells: a computational analysis

Background

The maintenance of stem cell pluripotency is controlled by a core cluster of transcription factors, NANOG, OCT4 and SOX2 – genes that jointly regulate each other’s expression. The expression of some of these genes, especially of Nanog, is heterogeneous in a population of undifferentiated stem cells in culture. Transient changes in expression levels, as well as heterogeneity of the population is not restricted to this core regulator, but involve a large number of other genes that include growth factors, transcription factors or signal transduction proteins.

Results

As the molecular mechanisms behind NANOG expression heterogeneity is not yet understood, we explore by computational modeling the core transcriptional regulatory circuit and its input from autocrine FGF signals that act through the MAP kinase cascade. We argue that instead of negative feedbacks within the core NANOG-OCT4-SOX2 transcriptional regulatory circuit, autocrine signaling loops such as the Esrrb - FGF - ERK feedback considered here are likely to generate distinct sub-states within the “ON” state of the core Nanog switch. Thus, the experimentally observed fluctuations in Nanog transcription levels are best explained as noise-induced transitions between negative feedback-generated sub-states. We also demonstrate that ERK phosphorilation is altered and being anti-correlated with fluctuating Nanog expression – in accord with model simulations. Our modeling approach assigns an empirically testable function to the transcriptional regulators Klf4 and Esrrb, and predict differential regulation of FGF family members.

Conclusions

We argue that slow fluctuations in Nanog expression likely reflect individual cell-specific changes in parameters of an autocrine feedback loop, such as changes in ligand capture efficiency, receptor numbers or the presence of crosstalks within the MAPK signal transduction pathway. We proposed a model that operates with binding affinities of multiple transcriptional regulators of pluripotency, and the activity of an autocrine signaling pathway. The resulting model produces varied expression levels of several components of pluripotency regulation, largely consistent with empirical observations reported previously and in this present work.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0112-4) contains supplementary material, which is available to authorized users.

Transcription factor heterogeneity in pluripotent stem cells: a stochastic advantage

When pluripotent cells are exposed to a uniform culture environment they routinely display heterogeneous gene expression. Aspects of this heterogeneity, such as Nanog expression, are linked to differences in the propensity of individual cells to either self-renew or commit towards differentiation. Recent findings have provided new insight into the underlying causes of this heterogeneity, which we summarise here using Nanog, a key regulator of pluripotency, as a model gene. We discuss the role of transcription factor heterogeneity in facilitating the intrinsically dynamic and stochastic nature of the pluripotency network, which in turn provides a potential benefit to a population of cells that needs to balance cell fate decisions.

Deconstructing stem cell population heterogeneity: single-cell analysis and modeling approaches

Isogenic stem cell populations display cell-to-cell variations in a multitude of attributes including gene or protein expression, epigenetic state, morphology, proliferation and proclivity for differentiation. The origins of the observed heterogeneity and its roles in the maintenance of pluripotency and the lineage specification of stem cells remain unclear. Addressing pertinent questions will require the employment of single-cell analysis methods as traditional cell biochemical and biomolecular assays yield mostly population-average data. In addition to time-lapse microscopy and flow cytometry, recent advances in single-cell genomic, transcriptomic and proteomic profiling are reviewed. The application of multiple displacement amplification, next generation sequencing, mass cytometry and spectrometry to stem cell systems is expected to provide a wealth of information affording unprecedented levels of multiparametric characterization of cell ensembles under defined conditions promoting pluripotency or commitment. Establishing connections between single-cell analysis information and the observed phenotypes will also require suitable mathematical models. Stem cell self-renewal and differentiation are orchestrated by the coordinated regulation of subcellular, intercellular and niche-wide processes spanning multiple time scales. Here, we discuss different modeling approaches and challenges arising from their application to stem cell populations. Integrating single-cell analysis with computational methods will fill gaps in our knowledge about the functions of heterogeneity in stem cell physiology. This combination will also aid the rational design of efficient differentiation and reprogramming strategies as well as bioprocesses for the production of clinically valuable stem cell derivatives.