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WIREs Syst Biol Med
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Integrating omics into the cardiac differentiation of human pluripotent stem cells

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Abstract Time‐dependent extracellular manipulations of human pluripotent stem cells can yield as much as 90% pure populations of cardiomyocytes. While the extracellular control of differentiation generally entails dynamic regulation of well‐known pathways such as Wnt, BMP, and Nodal signaling, the underlying genetic networks are far more complex and are poorly understood. Notably, the identification of these networks holds promise for understanding heart disease and regeneration. The availability of genome‐wide experimentation, such as RNA and DNA sequencing, as well as high throughput surveying with small molecule and small interfering RNA libraries, now enables us to map the genetic interactions underlying cardiac differentiation on a global scale. Initial studies demonstrate the complexity of the genetic regulation of cardiac differentiation, exposing unanticipated novel mechanisms. However, the large datasets generated tend to be overwhelming and systematic approaches are needed to process the vast amount of data to improve our mechanistic understanding of the complex biology. Systems biology methods spur high hopes for parsing vast amounts of data into genetic interaction models that can be verified experimentally and ultimately yield functional networks that expose the genetic connections underlying biological processes. WIREs Syst Biol Med 2014, 6:247–264. doi: 10.1002/wsbm.1268 This article is categorized under: Biological Mechanisms > Cell Signaling Laboratory Methods and Technologies > Genetic/Genomic Methods Developmental Biology > Stem Cell Biology and Regeneration
Overview of cardiac differentiation in human pluripotent stem cells (PSC). Schematic representation of the sequential steps and their associated markers during PSC differentiation to cardiomyocytes. Pathways that need to be modulated to increase cardiac yields are indicated.
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Functional genomics to validate network interactions. (a) High throughput screening allows large scale functional testing of nodes and edges identified through integration of the various omics datasets. microRNAs (miRNA), siRNA, or small molecule pathway modulators can be implemented in assays to functionally validate or identify nodes and edges in an assay of choice. We use cardiac specific reporters (illustrated) to identify functional nodes that are important for cardiac differentiation. In a screening campaign, small RNAs or small molecules that downregulate cardiac differentiation, are identified as key nodes (indicated in red, green indicates increase in cardiac differentiation), which are then incorporated in the interaction network to yield functionally validated genetic cascades. (b) Schematic overview of functional genomics results during cardiac differentiation. Functional screens have indicated that early differentiation steps are dependent on signaling factors to drive fate specification, while later stages are not. Genetic interactions are, however, important throughout the differentiation process, initially downstream of extracellular factors, and later downstream of other transcription factors.
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Integration of omics data into functional networks. (a) Signaling cascades and the downstream genetic events can be profiled at several levels on a genome‐wide scale (indicated with red dashed lines). The various datasets can then be integrated into functional networks that can be validated experimentally. (b) Flow chart of the steps needed to reconstruct the genetic interactions downstream of extracellular signals through integration of omics data.
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microRNA (miRNA) target identification using target sensors. (a) Schematic illustration of a miRNA target sensor (SERCA2a is shown as an example). The 3′ untranslated region (3′UTR) of the gene of interest is cloned downstream of the eGFP sequence under the control of the PGK promoter. Upon miRNA binding the hybrid GFP‐3′UTR RNA strand is degraded, resulting in the loss of GFP. (b) Workflow of a miRNA target sensor screen and examples of a GFP‐based miRNA target sensor in the presence of negative controls and active miRNAs. GFP intensity and area are quantified through a GFP thresholding algorithm to convert images into data. (c) Example result of a whole genome miRNA screen run against a target sensor for Serca2a. It plots significance versus GFP fold change for each of the 875 miRNAs tested. (d) Venn diagram indicating the overlap between miRNA target prediction algorithms and experimental data from a target sensor screen for SERCA2a.
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Example of extracellular control of transcriptional regulation. Jmjd3 is a histone demethylase enzyme that removes PRC2‐placed methyl groups in histones to allow transcription. The example illustrates how active Nodal signaling controls transcriptional regulation by the induction of Jmjd3, which then requires interaction with a transcription factor (T‐box) to achieve specificity for certain promoters (Brachyury/T in this example). An extra level of complexity is present as indicated by the need of Tcf (downstream of a Wnt signal) to be bound to the promoter as well to allow transcription.
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Overview of extracellular inputs and their intracellular effectors that regulate cardiac differentiation. Most known extracellular factors at the different stages of cardiac differentiation are mapped into one diagram, and their receptors are located on the cell membrane (red line). The arrows indicate connections to the intracellular signaling proteins that are the effectors of the induced signals (key indicates the type of regulation for each connection). Key cardiac genes downstream (directly or indirectly) are also indicated.
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Laboratory Methods and Technologies > Genetic/Genomic Methods
Biological Mechanisms > Cell Signaling
Developmental Biology > Stem Cell Biology and Regeneration

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