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

Modeling the Impact of Alternative Immunization Strategies: Using Matrices as Memory Lanes

Existing modeling approaches are divided between a focus on the constitutive (micro) elements of systems or on higher (macro) organization levels. Micro-level models enable consideration of individual histories and interactions, but can be unstable and subject to cumulative errors. Macro-level models focus on average population properties, but may hide relevant heterogeneity at the micro-scale. We present a framework that integrates both approaches through the use of temporally structured matrices that can take large numbers of variables into account. Matrices are composed of several bidimensional (time×age) grids, each representing a state (e.g. physiological, immunological, socio-demographic). Time and age are primary indices linking grids. These matrices preserve the entire history of all population strata and enable the use of historical events, parameters and states dynamically in the modeling process. This framework is applicable across fields, but particularly suitable to simulate the impact of alternative immunization policies. We demonstrate the framework by examining alternative strategies to accelerate measles elimination in 15 developing countries. The model recaptured long-endorsed policies in measles control, showing that where a single routine measles-containing vaccine is employed with low coverage, any improvement in coverage is more effective than a second dose. It also identified an opportunity to save thousands of lives in India at attractively low costs through the implementation of supplementary immunization campaigns. The flexibility of the approach presented enables estimating the effectiveness of different immunization policies in highly complex contexts involving multiple and historical influences from different hierarchical levels.

Stochasticity and Spatial Interaction Govern Stem Cell Differentiation Dynamics

Stem cell differentiation underlies many fundamental processes such as development, tissue growth and regeneration, as well as disease progression. Understanding how stem cell differentiation is controlled in mixed cell populations is an important step in developing quantitative models of cell population dynamics. Here we focus on quantifying the role of cell-cell interactions in determining stem cell fate. Toward this, we monitor stem cell differentiation in adherent cultures on micropatterns and collect statistical cell fate data. Results show high cell fate variability and a bimodal probability distribution of stem cell fraction on small (80–140 μm diameter) micropatterns. On larger (225–500 μm diameter) micropatterns, the variability is also high but the distribution of the stem cell fraction becomes unimodal. Using a stochastic model, we analyze the differentiation dynamics and quantitatively determine the differentiation probability as a function of stem cell fraction. Results indicate that stem cells can interact and sense cellular composition in their immediate neighborhood and adjust their differentiation probability accordingly. Blocking epithelial cadherin (E-cadherin) can diminish this cell-cell contact mediated sensing. For larger micropatterns, cell motility adds a spatial dimension to the picture. Taken together, we find stochasticity and cell-cell interactions are important factors in determining cell fate in mixed cell populations.

Productive Infection of Human Embryonic Stem Cell-Derived NKX2.1+ Respiratory Progenitors with Human Rhinovirus

Airway epithelial cells generated from pluripotent stem cells (PSCs) represent a resource for research into a variety of human respiratory conditions, including those resulting from infection with common human pathogens. Using an NKX2.1-GFP reporter human embryonic stem cell line, we developed a serum-free protocol for the generation of NKX2.1(+) endoderm that, when transplanted into immunodeficient mice, matured into respiratory cell types identified by expression of CC10, MUC5AC, and surfactant proteins. Gene profiling experiments indicated that day 10 NKX2.1(+) endoderm expressed markers indicative of early foregut but lacked genes associated with later stages of respiratory epithelial cell differentiation. Nevertheless, NKX2.1(+) endoderm supported the infection and replication of the common respiratory pathogen human rhinovirus HRV1b. Moreover, NKX2.1(+) endoderm upregulated expression of IL-6, IL-8, and IL-1B in response to infection, a characteristic of human airway epithelial cells. Our experiments provide proof of principle for the use of PSC-derived respiratory epithelial cells in the study of cell-virus interactions.

SIGNIFICANCE

This report provides proof-of-principle experiments demonstrating, for the first time, that human respiratory progenitor cells derived from stem cells in the laboratory can be productively infected with human rhinovirus, the predominant cause of the common cold.

Endothelial cells mediate islet-specific maturation of human embryonic stem cell-derived pancreatic progenitor cells

It is well recognized that in vitro differentiation of embryonic stem cells (ESC) can be best achieved by closely recapitulating the in vivo developmental niche. Thus, implementation of directed differentiation strategies has yielded encouraging results in the area of pancreatic islet differentiation. These strategies have concentrated on direct addition of chemical signals, however, other aspect of the developmental niche are yet to be explored. During development, pancreatic progenitor (PP) cells grow as an epithelial sheet, which aggregates with endothelial cells (ECs) during the final stages of maturation. Several findings suggest that the interactions with EC play a role in pancreatic development. In this study, we recapitulated this phenomenon in an in vitro environment by maturing the human ESC (hESC)-derived PP cells in close contact with ECs. We find that co-culture with different ECs (but not fibroblast) alone results in pancreatic islet-specific differentiation of hESC-derived PP cells even in the absence of additional chemical induction. The differentiated cells responded to exogenous glucose levels by enhanced C-peptide synthesis. The co-culture system aligned well with endocrine development as determined by comprehensive analysis of involved signaling pathways. By recapitulating cell-cell interaction aspects of the developmental niche we achieved a differentiation model that aligns closely with islet organogenesis.

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.

Improving efficiency of human pluripotent stem cell differentiation platforms using an integrated experimental and computational approach

Human pluripotent stem cells (hPSCs) have an unparalleled potential for tissue engineering applications including regenerative therapies and in vitro cell-based models for studying normal and diseased tissue morphogenesis, or drug and toxicological screens. While numerous hPSC differentiation methods have been developed to generate various somatic cell types, the potential of hPSC-based technologies is hinged on the ability to translate these established lab-scale differentiation systems to large-scale processes to meet the industrial and clinical demands for these somatic cell types. Here, we demonstrate a strategy for investigating the efficiency and scalability of hPSC differentiation platforms. Using two previously reported epithelial differentiation systems as models, we fit an ODE-based kinetic model to data representing dynamics of various cell subpopulations present in our culture. This fit was performed by estimating rate constants of each cell subpopulation's cell fate decisions (self-renewal, differentiation, death). Sensitivity analyses on predicted rate constants indicated which cell fate decisions had the greatest impact on overall epithelial cell yield in each differentiation process. In addition, we found that the final cell yield was limited by the self-renewal rate of either the progenitor state or the final differentiated state, depending on the differentiation protocol. Also, the relative impact of these cell fate decision rates was highly dependent on the maximum capacity of the cell culture system. Overall, we outline a novel approach for quantitative analysis of established laboratory-scale hPSC differentiation systems and this approach may ease development to produce large quantities of cells for tissue engineering applications.

Spatial pattern dynamics of 3D stem cell loss of pluripotency via rules-based computational modeling

Pluripotent embryonic stem cells (ESCs) have the unique ability to differentiate into cells from all germ lineages, making them a potentially robust cell source for regenerative medicine therapies, but difficulties in predicting and controlling ESC differentiation currently limit the development of therapies and applications from such cells. A common approach to induce the differentiation of ESCs in vitro is via the formation of multicellular aggregates known as embryoid bodies (EBs), yet cell fate specification within EBs is generally considered an ill-defined and poorly controlled process. Thus, the objective of this study was to use rules-based cellular modeling to provide insight into which processes influence initial cell fate transitions in 3-dimensional microenvironments. Mouse embryonic stem cells (D3 cell line) were differentiated to examine the temporal and spatial patterns associated with loss of pluripotency as measured through Oct4 expression. Global properties of the multicellular aggregates were accurately recapitulated by a physics-based aggregation simulation when compared to experimentally measured physical parameters of EBs. Oct4 expression patterns were analyzed by confocal microscopy over time and compared to simulated trajectories of EB patterns. The simulations demonstrated that loss of Oct4 can be modeled as a binary process, and that associated patterns can be explained by a set of simple rules that combine baseline stochasticity with intercellular communication. Competing influences between Oct4+ and Oct4- neighbors result in the observed patterns of pluripotency loss within EBs, establishing the utility of rules-based modeling for hypothesis generation of underlying ESC differentiation processes. Importantly, the results indicate that the rules dominate the emergence of patterns independent of EB structure, size, or cell division. In combination with strategies to engineer cellular microenvironments, this type of modeling approach is a powerful tool to predict stem cell behavior under a number of culture conditions that emulate characteristics of 3D stem cell niches.