Cell Population Model to Track Stochastic Cellular Decision-Making During Differentiation.

Human pluripotent stem cells are defined by their potential to give rise to all of the lineages of an embryo proper. Guiding the differentiation of embryonic stem cells or induced pluripotent stem cells can be achieved by exposing them to a succession of signaling conditions meant to mimic developmental milieus. However, achieving a quantitative understanding of the relationship between proliferation, cell death, and commitment has been difficult due to the inherent heterogeneity of pluripotent stem cells and their differentiation. Here, we describe a computational modeling approach to track the dynamics of germ layer commitment of human embryonic stem cells. We demonstrate that simulations using this model yield specific hypotheses regarding proliferation, cell death, and commitment and that these predictions are consistent with experimental measurements.

Alginate encapsulation of chitosan nanoparticles: a viable alternative to soluble chemical signaling in definitive endoderm induction of human embryonic stem cells.

Human embryonic stem cells (hESCs) are characterized by both their pluripotency and ability to self-renew, rendering them attractive for treatment of degenerative diseases. The cues necessary to induce hESC differentiation to a desired lineage can be categorized as either chemical or biophysical in nature. While chemical cues are used as primary inducers of differentiation, biophysical cues have been found to augment the process. In this regard, we have designed a novel chitosan nanoparticle augmented encapsulated alginate (CNPEA) platform which can not only augment, but induce differentiation of hESCs into the definitive endoderm (DE) lineage in the absence of specific soluble chemical inducers. These endoderm cells were comparable in phenotype with chemically driven DE cells and remained amenable to further maturation into pancreatic lineages. This study demonstrates the feasibility of carefully designed and tailored nanomaterials inducing differentiation, and moreover demonstrating the possibility of replacing growth factors by material cues.

Potential for pancreatic maturation of differentiating human embryonic stem cells is sensitive to the specific pathway of definitive endoderm commitment.

This study provides a detailed experimental and mathematical analysis of the impact of the initial pathway of definitive endoderm (DE) induction on later stages of pancreatic maturation. Human embryonic stem cells (hESCs) were induced to insulin-producing cells following a directed-differentiation approach. DE was induced following four alternative pathway modulations. DE derivatives obtained from these alternate pathways were subjected to pancreatic progenitor (PP) induction and maturation and analyzed at each stage. Results indicate that late stage maturation is influenced by the initial pathway of DE commitment. Detailed quantitative analysis revealed WNT3A and FGF2 induced DE cells showed highest expression of insulin, are closely aligned in gene expression patterning and have a closer resemblance to pancreatic organogenesis. Conversely, BMP4 at DE induction gave most divergent differentiation dynamics with lowest insulin upregulation, but highest glucagon upregulation. Additionally, we have concluded that early analysis of PP markers is indicative of its potential for pancreatic maturation.

Systems level approach reveals the correlation of endoderm differentiation of mouse embryonic stem cells with specific microstructural cues of fibrin gels.

Stem cells receive numerous cues from their associated substrate that help to govern their behaviour. However, identification of influential substrate characteristics poses difficulties because of their complex nature. In this study, we developed an integrated experimental and systems level modelling approach to investigate and identify specific substrate features influencing differentiation of mouse embryonic stem cells (mESCs) on a model fibrous substrate, fibrin. We synthesized a range of fibrin gels by varying fibrinogen and thrombin concentrations, which led to a range of substrate stiffness and microstructure. mESCs were cultured on each of these gels, and characterization of the differentiated cells revealed a strong influence of substrate modulation on gene expression patterning. To identify specific substrate features influencing differentiation, the substrate microstructure was quantified by image analysis and correlated with stem cell gene expression patterns using a statistical model. Significant correlations were observed between differentiation and microstructure features, specifically fibre alignment. Furthermore, this relationship occurred in a lineage-specific manner towards endoderm. This systems level approach allows for identification of specific substrate features from a complex material which are influential to cellular behaviour. Such analysis may be effective in guiding the design of scaffolds with specific properties for tissue engineering applications.

Early differentiation patterning of mouse embryonic stem cells in response to variations in alginate substrate stiffness.

BACKGROUND: Embryonic stem cells (ESCs) have been implicated to have tremendous impact in regenerative therapeutics of various diseases, including Type 1 Diabetes. Upon generation of functionally mature ESC derived islet-like cells, they need to be implanted into diabetic patients to restore the loss of islet activity. Encapsulation in alginate microcapsules is a promising route of implantation, which can protect the cells from the recipient's immune system. While there has been a significant investigation into islet encapsulation over the past decade, the feasibility of encapsulation and differentiation of ESCs has been less explored. Research over the past few years has identified the cellular mechanical microenvironment to play a central role in phenotype commitment of stem cells. Therefore it will be important to design the encapsulation material to be supportive to cellular functionality and maturation. RESULTS: This work investigated the effect of stiffness of alginate substrate on initial differentiation and phenotype commitment of murine ESCs. ESCs grown on alginate substrates tuned to similar biomechanical properties of native pancreatic tissue elicited both an enhanced and incrementally responsive differentiation towards endodermal lineage traits. CONCLUSIONS: The insight into these biophysical phenomena found in this study can be used along with other cues to enhance the differentiation of embryonic stem cells toward a specific lineage fate.

An integer optimization algorithm for robust identification of non-linear gene regulatory networks.

BACKGROUND: Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. RESULTS: We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters. Furthermore, in both the in silico and experimental case studies, the predicted gene expression profiles are in very close agreement with the dynamics of the input data. CONCLUSIONS: Our integer programming algorithm effectively utilizes bootstrapping to identify robust gene regulatory networks from noisy, non-linear time-series gene expression data. With significant noise and non-linearities being inherent to biological systems, the present formulism, with the incorporation of network sparsity, is extremely relevant to gene regulatory networks, and while the formulation has been validated against in silico and E. Coli data, it can be applied to any biological system.

Population based model of human embryonic stem cell (hESC) differentiation during endoderm induction.

The mechanisms by which human embryonic stem cells (hESC) differentiate to endodermal lineage have not been extensively studied. Mathematical models can aid in the identification of mechanistic information. In this work we use a population-based modeling approach to understand the mechanism of endoderm induction in hESC, performed experimentally with exposure to Activin A and Activin A supplemented with growth factors (basic fibroblast growth factor (FGF2) and bone morphogenetic protein 4 (BMP4)). The differentiating cell population is analyzed daily for cellular growth, cell death, and expression of the endoderm proteins Sox17 and CXCR4. The stochastic model starts with a population of undifferentiated cells, wherefrom it evolves in time by assigning each cell a propensity to proliferate, die and differentiate using certain user defined rules. Twelve alternate mechanisms which might describe the observed dynamics were simulated, and an ensemble parameter estimation was performed on each mechanism. A comparison of the quality of agreement of experimental data with simulations for several competing mechanisms led to the identification of one which adequately describes the observed dynamics under both induction conditions. The results indicate that hESC commitment to endoderm occurs through an intermediate mesendoderm germ layer which further differentiates into mesoderm and endoderm, and that during induction proliferation of the endoderm germ layer is promoted. Furthermore, our model suggests that CXCR4 is expressed in mesendoderm and endoderm, but is not expressed in mesoderm. Comparison between the two induction conditions indicates that supplementing FGF2 and BMP4 to Activin A enhances the kinetics of differentiation than Activin A alone. This mechanistic information can aid in the derivation of functional, mature cells from their progenitors. While applied to initial endoderm commitment of hESC, the model is general enough to be applicable either to a system of adult stem cells or later stages of ESC differentiation.