We investigated the effects of low power laser irradiation on the proliferation of retinal pigment epithelial (RPE) cells. Adult human RPE cells were artificially pigmented by preincubation with sepia melanin, and exposed to a single sublethal laser pulse (590 nm, 1 µs, <200 mJ/cm2). DNA synthesis, cell number, and growth factor activity in irradiated RPE cells were subsequently monitored. The effect of sublethal laser irradiation on the “wound” healing response of an RPE monolayer in an in vitro scratch assay was also investigated. Single pulsed laser irradiation increased DNA synthesis in pigmented RPE cells measured 6 h post-treatment. In the scratch assay, laser irradiation increased the rates of cell proliferation and wound closure. Conditioned medium, collected 48 h following laser treatment, increased cell proliferation of unirradiated cells. Irradiation increased RPE cell secretion of platelet-derived growth factor (PDGF)-B chain, and increased mRNA levels of several growth factors and their receptors, including PDGF, transforming growth factor-β1, basic fibroblast growth factor, epidermal growth factor, insulin-like growth factor, as well as heat shock proteins. This demonstrates, for the first time, that low power single pulsed laser irradiation stimulates the proliferation of RPE cells, and upregulates growth factors that are mitogenic for RPE cells.
Photothermolysis; Wound healing; Heat shock; Macular degeneration
Early embryonic stem cell (ESC) differentiation is marked by the formation of 3 germ layers from which all tissues types will arise. While conventionally ESCs were differentiated by altering their chemical microenvironment, recently it was established that mechanical microenvironment can also contribute towards cellular phenotype commitment. In this study, we are reporting how the cellular mechanical microenvironment of soft substrates affects the differentiation and phenotypic commitment of ESCs. Mouse ESCs were cultured in a fibrin hydrogel matrix in two- and three-dimensional culture. The gelation characteristics of the substrate were modulated by systematically altering the fibrinogen concentration and the fibrinogen-thrombin cross-linking ratio. Analysis of the embryonic stem cells cultured on different substrate conditions clearly illustrate strong influence which substrate physical characteristics assert on cellular behaviors. Specifically it was found that ESCs have a higher proliferation rate in gels of lower stiffness. Early differentiation events were studied by analyzing the gene and protein expression levels of early germ layer markers. Our results revealed that lower substrate stiffness elicit stronger up-regulation of endoderm related genes Sox17, Afp and Hnf4 when compared to stiffer substrates. While both 2D and 3D cultures show a similar response, the effects were much stronger in 3-dimensional culture as compared to 2-dimensional one. These results suggest that physical cues can be used to modulate ESC differentiation into clinically relevant tissues such as liver and pancreas.
Soft substrates; Embryonic stem cells; Mechanical properties; Fibrin hydrogel; Endoderm; Differentiation
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.
Robustness is a critical feature of signaling pathways ensuring signal propagation with high fidelity in the event of perturbations. Here we present a detailed quantitative analysis of robustness in insulin mediated PI3K/AKT pathway, a critical signaling pathway maintaining self-renewal in human embryonic stem cells. Using global sensitivity analysis, we identified robustness promoting mechanisms that ensure (1) maintenance of a first order or overshoot dynamics of self-renewal molecule, p-AKT and (2) robust transfer of signals from oscillatory insulin stimulus to p-AKT in the presence of noise. Our results indicate that negative feedback controls the robustness to most perturbations. Faithful transfer of signal from the stimulating ligand to p-AKT occurs even in the presence of noise, albeit with signal attenuation and high frequency cut-off. Negative feedback contributes to signal attenuation, while positive regulators upstream of PIP3 contribute to signal amplification. These results establish precise mechanisms to modulate self-renewal molecules like p-AKT.
Robust dynamics; Signal transfer efficiency; PI3K/AKT pathway; Self-renewal; Global sensitivity analysis
The pluripotent property of human embryonic stem cells (hESCs) makes them attractive for treatment of degenerative diseases such as diabetes. We have developed a stage-wise directed differentiation protocol to produce alginate-encapsulated islet-like cells derived from hESCs, which can be directly implanted for diabetes therapy. The advantage of alginate encapsulation lies in its capability to immunoisolate, along with the added possibility of scalable culture. We have evaluated the possibility of encapsulating hESCs at different stages of differentiation. Encapsulation of predifferentiated cells resulted in insufficient cellular yield and differentiation. On the other hand, encapsulation of undifferentiated hESCs followed by differentiation induction upon encapsulation resulted in the highest viability and differentiation. More striking was that alginate encapsulation resulted in a much stronger differentiation compared to parallel two-dimensional cultures, resulting in 20-fold increase in c-peptide protein synthesis. To elucidate the mechanism contributing to encapsulation-mediated enhancement in hESC maturation, investigation of the signaling pathways revealed interesting insight. While the phospho-protein levels of all the tested signaling molecules were lower under encapsulation, the ratio of pSMAD/pAKT was significantly higher, indicating a more efficient signal transduction under encapsulation. These results clearly demonstrate that alginate encapsulation of hESCs and differentiation to islet-cell types provides a potentially translatable treatment option for type 1 diabetes.
The precise inflammatory role of the cytokine interleukin (IL)-6 and its utility as a biomarker or therapeutic target have been the source of much debate, presumably due to the complex pro- and anti-inflammatory effects of this cytokine. We previously developed a nonlinear ordinary differential equation (ODE) model to explain the dynamics of endotoxin (lipopolysaccharide; LPS)-induced acute inflammation and associated whole-animal damage/dysfunction (a proxy for the health of the organism), along with the inflammatory mediators tumor necrosis factor (TNF)-α, IL-6, IL-10, and nitric oxide (NO). The model was partially calibrated using data from endotoxemic C57Bl/6 mice. Herein, we investigated the sensitivity of the area under the damage curve (AUCD) to the 51 rate parameters of the ODE model for different levels of simulated LPS challenges using a global sensitivity approach called Random Sampling High Dimensional Model Representation (RS-HDMR). We explored sufficient parametric Monte Carlo samples to generate the variance-based Sobol' global sensitivity indices, and found that inflammatory damage was highly sensitive to the parameters affecting the activity of IL-6 during the different stages of acute inflammation. The AUCIL6 showed a bimodal distribution, with the lower peak representing healthy response and the higher peak representing sustained inflammation. Damage was minimal at low AUCIL6, giving rise to a healthy response. In contrast, intermediate levels of AUCIL6 resulted in high damage, and this was due to the insufficiency of damage recovery driven by anti-inflammatory responses and the activation of positive feedback sustained by IL-6. At high AUCIL6, damage recovery was interestingly restored in some population of simulated animals due to the NO-mediated anti-inflammatory responses. These observations suggest that the host's health status during acute inflammation depends in a nonlinear fashion on the magnitude of the inflammatory stimulus, on the host's propensity to produce IL-6, and on NO-mediated downstream responses.
Inflammation Dynamics; Meta-Modeling; Endotoxin; Cytokines; Nitric oxide
Motivation: Maintenance of the self-renewal state in human embryonic stem cells (hESCs) is the foremost critical step for regenerative therapy applications. The insulin-mediated PI3K/AKT pathway is well appreciated as being the central pathway supporting hESC self-renewal; however, the regulatory interactions in the pathway that maintain cell state are not yet known. Identification of these regulatory pathway components will be critical for designing targeted interventions to facilitate a completely defined platform for hESC propagation and differentiation. Here, we have developed a systems analysis approach to identify regulatory components that control PI3K/AKT pathway in self-renewing hESCs.
Results: A detailed mathematical model was adopted to explain the complex regulatory interactions in the PI3K/AKT pathway. We evaluated globally sensitive processes of the pathway in a computationally efficient manner by replacing the detailed model by a surrogate meta-model. Our mathematical analysis, supported by experimental validation, reveals that negative regulators of the molecules IRS1 and PIP3 primarily govern the steady state of the pathway in hESCs. Among the regulators, negative feedback via IRS1 reduces the sensitivity of various reactions associated with direct trunk of the pathway and also constraints the propagation of parameter uncertainty to the levels of post receptor signaling molecules. Furthermore, our results suggest that inhibition of negative feedback can significantly increase p-AKT levels and thereby, better support hESC self-renewal. Our integrated mathematical modeling and experimental workflow demonstrates the significant advantage of computationally efficient meta-model approaches to detect sensitive targets from signaling pathways.
Availability and implementation:
FORTRAN codes for the PI3K/AKT pathway and the RS-HDMR implementation are available from the authors upon request.
Supplementary data are available at Bioinformatics online.
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.
systems level modelling; embryonic stem cells; differentiation; fibrin substrate; microstructural topology; regression analysis
Approximately 285 million people worldwide suffer from diabetes, with insulin supplementation as the most common treatment measure. Regenerative medicine approaches such as a bioengineered pancreas has been proposed as potential therapeutic alternatives. A bioengineered pancreas will benefit from the development of a bioscaffold that supports and enhances cellular function and tissue development. Perfusion-decellularized organs are a likely candidate for use in such scaffolds since they mimic compositional, architectural and biomechanical nature of a native organ. In this study, we investigate perfusion-decellularization of whole pancreas and the feasibility to recellularize the whole pancreas scaffold with pancreatic cell types. Our result demonstrates that perfusion-decellularization of whole pancreas effectively removes cellular and nuclear material while retaining intricate three-dimensional microarchitecture with perfusable vasculature and ductal network and crucial extracellular matrix (ECM) components. To mimic pancreatic cell composition, we recellularized the whole pancreas scaffold with acinar and beta cell lines and cultured up to 5 days. Our result shows successful cellular engraftment within the decellularized pancreas, and the resulting graft gave rise to strong up-regulation of insulin gene expression. These findings support biological utility of whole pancreas ECM as a biomaterials scaffold for supporting and enhancing pancreatic cell functionality and represent a step toward bioengineered pancreas using regenerative medicine approaches.
Whole organ decellularization; Extracellular matrix scaffold; Tissue and organ engineering; Pancreatic β-cells
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.
Embryonic stem cells (ESCs) have emerged as potential cell sources for tissue engineering and regeneration owing to its virtually unlimited replicative capacity and the potential to differentiate into a variety of cell types. Current differentiation strategies primarily involve various growth factor/inducer/repressor concoctions with less emphasis on the substrate. Developing biomaterials to promote stem cell proliferation and differentiation could aid in the realization of this goal. Extracellular matrix (ECM) components are important physiological regulators, and can provide cues to direct ESC expansion and differentiation. ECM undergoes constant remodeling with surrounding cells to accommodate specific developmental event. In this study, using ESC derived aggregates called embryoid bodies (EB) as a model, we characterized the biological nature of ECM in EB after exposure to different treatments: spontaneously differentiated and retinoic acid treated (denoted as SPT and RA, respectively). Next, we extracted this treatment-specific ECM by detergent decellularization methods (Triton X-100, DOC and SDS are compared). The resulting EB ECM scaffolds were seeded with undifferentiated ESCs using a novel cell seeding strategy, and the behavior of ESCs was studied. Our results showed that the optimized protocol efficiently removes cells while retaining crucial ECM and biochemical components. Decellularized ECM from SPT EB gave rise to a more favorable microenvironment for promoting ESC attachment, proliferation, and early differentiation, compared to native EB and decellularized ECM from RA EB. These findings suggest that various treatment conditions allow the formulation of unique ESC-ECM derived scaffolds to enhance ESC bioactivities, including proliferation and differentiation for tissue regeneration applications.
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.
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.
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.
Diabetes; AFM; Pluripotent
Lineage specific differentiation of human embryonic stem cells (hESCs) is largely mediated by specific growth factors and extracellular matrix molecules. Growth factors initiate a cascade of signals which control gene transcription and cell fate specification. There is a lot of interest in inducing hESCs to an endoderm fate which serves as a pathway towards more functional cell types like the pancreatic cells. Research over the past decade has established several robust pathways for deriving endoderm from hESCs, with the capability of further maturation. However, in our experience, the functional maturity of these endoderm derivatives, specifically to pancreatic lineage, largely depends on specific pathway of endoderm induction. Hence it will be of interest to understand the underlying mechanism mediating such induction and how it is translated to further maturation. In this work we analyze the regulatory interactions mediating different pathways of endoderm induction by identifying co-regulated transcription factors.
hESCs were induced towards endoderm using activin A and 4 different growth factors (FGF2 (F), BMP4 (B), PI3KI (P), and WNT3A (W)) and their combinations thereof, resulting in 15 total experimental conditions. At the end of differentiation each condition was analyzed by qRT-PCR for 12 relevant endoderm related transcription factors (TFs). As a first approach, we used hierarchical clustering to identify which growth factor combinations favor up-regulation of different genes. In the next step we identified sets of co-regulated transcription factors using a biclustering algorithm. The high variability of experimental data was addressed by integrating the biclustering formulation with bootstrap re-sampling to identify robust networks of co-regulated transcription factors. Our results show that the transition from early to late endoderm is favored by FGF2 as well as WNT3A treatments under high activin. However, induction of late endoderm markers is relatively favored by WNT3A under high activin.
Use of FGF2, WNT3A or PI3K inhibition with high activin A may serve well in definitive endoderm induction followed by WNT3A specific signaling to direct the definitive endoderm into late endodermal lineages. Other combinations, though still feasible for endoderm induction, appear less promising for pancreatic endoderm specification in our experiments.
Human embryonic stem cells; Endoderm; Hierarchical clustering; Biclustering; Bootstrap
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.
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.
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.
Gene regulatory networks; Non-linear dynamics; S-system; Robust network identification; Bootstrapping; Integer programming; Optimization algorithm
Embryonic stem cells are conventionally differentiated by modulating specific growth factors in the cell culture media. Recently the effect of cellular mechanical microenvironment in inducing phenotype specific differentiation has attracted considerable attention. We have shown the possibility of inducing endoderm differentiation by culturing the stem cells on fibrin substrates of specific stiffness . Here, we analyze the regulatory network involved in such mechanically induced endoderm differentiation under two different experimental configurations of 2-dimensional and 3-dimensional culture, respectively. Mouse embryonic stem cells are differentiated on an array of substrates of varying mechanical properties and analyzed for relevant endoderm markers. The experimental data set is further analyzed for identification of co-regulated transcription factors across different substrate conditions using the technique of bi-clustering. Overlapped bi-clusters are identified following an optimization formulation, which is solved using an evolutionary algorithm. While typically such analysis is performed at the mean value of expression data across experimental repeats, the variability of stem cell systems reduces the confidence on such analysis of mean data. Bootstrapping technique is thus integrated with the bi-clustering algorithm to determine sets of robust bi-clusters, which is found to differ significantly from corresponding bi-clusters at the mean data value. Analysis of robust bi-clusters reveals an overall similar network interaction as has been reported for chemically induced endoderm or endodermal organs but with differences in patterning between 2-dimensional and 3-dimensional culture. Such analysis sheds light on the pathway of stem cell differentiation indicating the prospect of the two culture configurations for further maturation.
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.
Motivation: Primary purpose of modeling gene regulatory networks for developmental process is to reveal pathways governing the cellular differentiation to specific phenotypes. Knowledge of differentiation network will enable generation of desired cell fates by careful alteration of the governing network by adequate manipulation of cellular environment.
Results: We have developed a novel integer programming-based approach to reconstruct the underlying regulatory architecture of differentiating embryonic stem cells from discrete temporal gene expression data. The network reconstruction problem is formulated using inherent features of biological networks: (i) that of cascade architecture which enables treatment of the entire complex network as a set of interconnected modules and (ii) that of sparsity of interconnection between the transcription factors. The developed framework is applied to the system of embryonic stem cells differentiating towards pancreatic lineage. Experimentally determined expression profile dynamics of relevant transcription factors serve as the input to the network identification algorithm. The developed formulation accurately captures many of the known regulatory modes involved in pancreatic differentiation. The predictive capacity of the model is tested by simulating an in silico potential pathway of subsequent differentiation. The predicted pathway is experimentally verified by concurrent differentiation experiments. Experimental results agree well with model predictions, thereby illustrating the predictive accuracy of the proposed algorithm.
Supplementary information: Supplementary data are available at Bioinformatics online.