Approaches to in vitro tissue regeneration with application for human disease modeling and drug development

Reliable in vitro human disease models that capture the complexity of in vivo tissue behaviors are crucial to gain mechanistic insights into human disease and enable the development of treatments that are effective across broad patient populations. The integration of stem cell technologies, tissue engineering, emerging biomaterials strategies and microfabrication processes, as well as computational and systems biology approaches, is enabling new tools to generate reliable in vitro systems to study the molecular basis of human disease and facilitate drug development. In this review, we discuss these recently developed tools and emphasize opportunities and challenges involved in combining these technologies toward regenerative science.National Institute for Biomedical Imaging and Bioengineering (U.S.) (Grant 5R01EB010246-02)National Center for Advancing Translational Sciences (U.S.) (Grant 1UH2TR000496)United States. Defense Advanced Research Projects Agency (Cooperative Agreement W911NF-12-2-0039

Using mechanistic Bayesian networks to identify downstream targets of the Sonic Hedgehog pathway

Background: The topology of a biological pathway provides clues as to how a pathway operates, but rationally using this topology information with observed gene expression data remains a challenge. Results: We introduce a new general-purpose analytic method called Mechanistic Bayesian Networks (MBNs) that allows for the integration of gene expression data and known constraints within a signal or regulatory pathway to predict new downstream pathway targets. The MBN framework is implemented in an open-source Bayesian network learning package, the Python Environment for Bayesian Learning (PEBL). We demonstrate how MBNs can be used by modeling the early steps of the sonic hedgehog pathway using gene expression data from different developmental stages and genetic backgrounds in mouse. Using the MBN approach we are able to automatically identify many of the known downstream targets of the hedgehog pathway such as Gas1 and Gli1, along with a short list of likely targets such as Mig12. Conclusions: The MBN approach shown here can easily be extended to other pathways and data types to yield a more mechanistic framework for learning genetic regulatory models.Molecular and Cellular BiologyStem Cell and Regenerative Biolog

OCT4 induces embryonic pluripotency via STAT3 signaling and metabolic mechanisms.

OCT4 is a fundamental component of the molecular circuitry governing pluripotency in vivo and in vitro. To determine how OCT4 establishes and protects the pluripotent lineage in the embryo, we used comparative single-cell transcriptomics and quantitative immunofluorescence on control and OCT4 null blastocyst inner cell masses at two developmental stages. Surprisingly, activation of most pluripotency-associated transcription factors in the early mouse embryo occurs independently of OCT4, with the exception of the JAK/STAT signaling machinery. Concurrently, OCT4 null inner cell masses ectopically activate a subset of trophectoderm-associated genes. Inspection of metabolic pathways implicates the regulation of rate-limiting glycolytic enzymes by OCT4, consistent with a role in sustaining glycolysis. Furthermore, up-regulation of the lysosomal pathway was specifically detected in OCT4 null embryos. This finding implicates a requirement for OCT4 in the production of normal trophectoderm. Collectively, our findings uncover regulation of cellular metabolism and biophysical properties as mechanisms by which OCT4 instructs pluripotency.This work was supported by the University of Cambridge, BBSRC project grant RG74277, BB/R018588/1 and MR/R017735/1 to HS and LB respectively, MRC PhD studentship for AK and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute

A stochastic framework of neurogenesis underlies the assembly of neocortical cytoarchitecture

The cerebral cortex contains multiple areas with distinctive cytoarchitectonical patterns, but the cellular mechanisms underlying the emergence of this diversity remain unclear. Here, we have investigated the neuronal output of individual progenitor cells in the developing mouse neocortex using a combination of methods that together circumvent the biases and limitations of individual approaches. Our experimental results indicate that progenitor cells generate pyramidal cell lineages with a wide range of sizes and laminar configurations. Mathematical modelling indicates that these outcomes are compatible with a stochastic model of cortical neurogenesis in which progenitor cells undergo a series of probabilistic decisions that lead to the specification of very heterogeneous progenies. Our findings support a mechanism for cortical neurogenesis whose flexibility would make it capable to generate the diverse cytoarchitectures that characterize distinct neocortical areas

Mapping Differentiation under Mixed Culture Conditions Reveals a Tunable Continuum of T Cell Fates

Cell differentiation is typically directed by external signals that drive opposing regulatory pathways. Studying differentiation under polarizing conditions, with only one input signal provided, is limited in its ability to resolve the logic of interactions between opposing pathways. Dissection of this logic can be facilitated by mapping the system's response to mixtures of input signals, which are expected to occur in vivo, where cells are simultaneously exposed to various signals with potentially opposing effects. Here, we systematically map the response of naïve T cells to mixtures of signals driving differentiation into the Th1 and Th2 lineages. We characterize cell state at the single cell level by measuring levels of the two lineage-specific transcription factors (T-bet and GATA3) and two lineage characteristic cytokines (IFN-γ and IL-4) that are driven by these transcription regulators. We find a continuum of mixed phenotypes in which individual cells co-express the two lineage-specific master regulators at levels that gradually depend on levels of the two input signals. Using mathematical modeling we show that such tunable mixed phenotype arises if autoregulatory positive feedback loops in the gene network regulating this process are gradual and dominant over cross-pathway inhibition. We also find that expression of the lineage-specific cytokines follows two independent stochastic processes that are biased by expression levels of the master regulators. Thus, cytokine expression is highly heterogeneous under mixed conditions, with subpopulations of cells expressing only IFN-γ, only IL-4, both cytokines, or neither. The fraction of cells in each of these subpopulations changes gradually with input conditions, reproducing the continuous internal state at the cell population level. These results suggest a differentiation scheme in which cells reflect uncertainty through a continuously tuneable mixed phenotype combined with a biased stochastic decision rather than a binary phenotype with a deterministic decision