Systematic Search for Recipes to Generate Induced Pluripotent Stem Cells

Chang, Rui; Shoemaker, Robert; Wang, Wei

doi:10.1371/journal.pcbi.1002300

Cited by 66 publications

(91 citation statements)

References 50 publications

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“…A strategy to find reprogramming recipes was suggested based on DBN methods [31]. Some of the reprogramming recipes found are consistent with experiments.…”

Section: Rsifroyalsocietypublishingorg J R Soc Interface 10: 20130787mentioning

confidence: 57%

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Quantifying Waddington landscapes and paths of non-adiabatic cell fate decisions for differentiation, reprogramming and transdifferentiation

Wang

2013

J. R. Soc. Interface.

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Cellular differentiation, reprogramming and transdifferentiation are determined by underlying gene regulatory networks. Non-adiabatic regulation via slow binding/unbinding to the gene can be important in these cell fate decisionmaking processes. Based on a stem cell core gene network, we uncovered the stem cell developmental landscape. As the binding/unbinding speed decreases, the landscape topography changes from bistable attractors of stem and differentiated states to more attractors of stem and other different cell states as well as substates. Non-adiabaticity leads to more differentiated cell types and provides a natural explanation for the heterogeneity observed in the experiments. We quantified Waddington landscapes with two possible cell fate decision mechanisms by changing the regulation strength or regulation timescale (nonadiabaticity). Transition rates correlate with landscape topography through barrier heights between different states and quantitatively determine global stability. We found the optimal speeds of these cell fate decision-making processes. We quantified biological paths and predict that differentiation and reprogramming go through an intermediate state (IM1), whereas transdifferentiation goes through another intermediate state (IM2). Some predictions are confirmed by recent experimental studies.

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“…A strategy to find reprogramming recipes was suggested based on DBN methods [31]. Some of the reprogramming recipes found are consistent with experiments.…”

Section: Rsifroyalsocietypublishingorg J R Soc Interface 10: 20130787mentioning

confidence: 57%

“…An underlying gene network of human ES cells was explored [31] via a literature search. The underlying gene regulatory wiring is the basis for quantifying the landscape and paths for differentiation, reprogramming and transdifferentiation.…”

Section: Epigenetic Landscapementioning

confidence: 99%

Quantifying Waddington landscapes and paths of non-adiabatic cell fate decisions for differentiation, reprogramming and transdifferentiation

Wang

2013

J. R. Soc. Interface.

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“…Realistic models of the core transcriptional regulatory network for the embryonic stem cell have already been constructed by others (16,32,33). We illustrate the network used in this study in Fig.…”

Section: Cell Types As Steady States Of Gene Networkmentioning

confidence: 99%

Stem cell differentiation as a many-body problem

Zhang

Wolynes

2014

Proc. Natl. Acad. Sci. U.S.A.

108

129

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Stem cell differentiation has been viewed as coming from transitions between attractors on an epigenetic landscape that governs the dynamics of a regulatory network involving many genes. Rigorous definition of such a landscape is made possible by the realization that gene regulation is stochastic, owing to the small copy number of the transcription factors that regulate gene expression and because of the single-molecule nature of the gene itself. We develop an approximation that allows the quantitative construction of the epigenetic landscape for large realistic model networks. Applying this approach to the network for embryonic stem cell development explains many experimental observations, including the heterogeneous distribution of the transcription factor Nanog and its role in safeguarding the stem cell pluripotency, which can be understood by finding stable steady-state attractors and the most probable transition paths between those attractors. We also demonstrate that the switching rate between attractors can be significantly influenced by the gene expression noise arising from the fluctuations of DNA occupancy when binding to a specific DNA site is slow.gene network | most probable path | master equation

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“…Furthermore, quantitative information to constrain model parameters [11,15,[19][20][21] is generally also lacking. There are proposals of using alternative approaches such as the statistical graphical models [22]. It may be fruitful to make connections of these different approaches.…”

Section: Introductionmentioning

confidence: 99%

Epigenetic state network approach for describing cell phenotypic transitions

et al. 2014

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Recent breakthroughs of cell phenotype reprogramming impose theoretical challenges on unravelling the complexity of large circuits maintaining cell phenotypes coupled at many different epigenetic and gene regulation levels, and quantitatively describing the phenotypic transition dynamics. A popular picture proposed by Waddington views cell differentiation as a ball sliding down a landscape with valleys corresponding to different cell types separated by ridges. Based on theories of dynamical systems, we establish a novel ‘epigenetic state network’ framework that captures the global architecture of cell phenotypes, which allows us to translate the metaphorical low-dimensional Waddington epigenetic landscape concept into a simple-yet-predictive rigorous mathematical framework of cell phenotypic transitions. Specifically, we simplify a high-dimensional epigenetic landscape into a collection of discrete states corresponding to stable cell phenotypes connected by optimal transition pathways among them. We then apply the approach to the phenotypic transition processes among fibroblasts (FBs), pluripotent stem cells (PSCs) and cardiomyocytes (CMs). The epigenetic state network for this case predicts three major transition pathways connecting FBs and CMs. One goes by way of PSCs. The other two pathways involve transdifferentiation either indirectly through cardiac progenitor cells or directly from FB to CM. The predicted pathways and multiple intermediate states are supported by existing microarray data and other experiments. Our approach provides a theoretical framework for studying cell phenotypic transitions. Future studies at single-cell levels can directly test the model predictions.

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Systematic Search for Recipes to Generate Induced Pluripotent Stem Cells

Cited by 66 publications

References 50 publications

Quantifying Waddington landscapes and paths of non-adiabatic cell fate decisions for differentiation, reprogramming and transdifferentiation

Quantifying Waddington landscapes and paths of non-adiabatic cell fate decisions for differentiation, reprogramming and transdifferentiation

Stem cell differentiation as a many-body problem

Epigenetic state network approach for describing cell phenotypic transitions

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