Functional Modules Distinguish Human Induced Pluripotent Stem Cells from Embryonic Stem Cells

Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, California 90095, USA.
Stem cells and development (Impact Factor: 3.73). 06/2011; 20(11):1937-50. DOI: 10.1089/scd.2010.0574
Source: PubMed


It has been debated whether human induced pluripotent stem cells (iPSCs) and embryonic stem cells (ESCs) express distinctive transcriptomes. By using the method of weighted gene co-expression network analysis, we showed here that iPSCs exhibit altered functional modules compared with ESCs. Notably, iPSCs and ESCs differentially express 17 modules that primarily function in transcription, metabolism, development, and immune response. These module activations (up- and downregulation) are highly conserved in a variety of iPSCs, and genes in each module are coherently co-expressed. Furthermore, the activation levels of these modular genes can be used as quantitative variables to discriminate iPSCs and ESCs with high accuracy (96%). Thus, differential activations of these functional modules are the conserved features distinguishing iPSCs from ESCs. Strikingly, the overall activation level of these modules is inversely correlated with the DNA methylation level, suggesting that DNA methylation may be one mechanism regulating the module differences. Overall, we conclude that human iPSCs and ESCs exhibit distinct gene expression networks, which are likely associated with different epigenetic reprogramming events during the derivation of iPSCs and ESCs.

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Available from: Anyou Wang, Sep 15, 2014
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    • "However, studies have reported the similarities and differences of various stem cell types in terms of genomic stability, transcriptomes [21], [22], [23], histone modifications [21], protein post-translational modifications [24] and DNA methylation [7], [10], [12], [13], [14], [25]. Genome-wide screens have been used to analyze epigenetic alterations in human pluripotent cells [26], [27]. "
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    ABSTRACT: Background Epigenetic regulation is critical for the maintenance of human pluripotent stem cells. It has been shown that pluripotent stem cells, such as embryonic stem cells and induced pluripotent stem cells, appear to have a hypermethylated status compared with differentiated cells. However, the epigenetic differences in genes that maintain stemness and regulate reprogramming between embryonic stem cells and induced pluripotent stem cells remain unclear. Additionally, differential methylation patterns of induced pluripotent stem cells generated using diverse methods require further study. Methodology Here, we determined the DNA methylation profiles of 10 human cell lines, including 2 ESC lines, 4 virally derived iPSC lines, 2 episomally derived iPSC lines, and the 2 parental cell lines from which the iPSCs were derived using Illumina's Infinium HumanMethylation450 BeadChip. The iPSCs exhibited a hypermethylation status similar to that of ESCs but with distinct differences from the parental cells. Genes with a common methylation pattern between iPSCs and ESCs were classified as critical factors for stemness, whereas differences between iPSCs and ESCs suggested that iPSCs partly retained the parental characteristics and gained de novo methylation aberrances during cellular reprogramming. No significant differences were identified between virally and episomally derived iPSCs. This study determined in detail the de novo differential methylation signatures of particular stem cell lines. Conclusions This study describes the DNA methylation profiles of human iPSCs generated using both viral and episomal methods, the corresponding somatic cells, and hESCs. Series of ss-DMRs and ES-iPS-DMRs were defined with high resolution. Knowledge of this type of epigenetic information could be used as a signature for stemness and self-renewal and provides a potential method for selecting optimal pluripotent stem cells for human regenerative medicine.
    PLoS ONE 09/2014; 9(9):e108350. DOI:10.1371/journal.pone.0108350 · 3.23 Impact Factor
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    • "Traditional approaches like those based on single antibody (e.g., OCT4) unlikely provide a satisfactory result due to their low sensitivity and the high similarity between iPSC and ESCs [2]. Cluster analyses based on global gene expression signatures have been developed to discriminate SCs from pluripotent cells (PCs) [3] [4] [5] [6], including iPSCs and ESCs, but this system cannot be used to discriminate iPSCs and ESCs because the gene signatures are not consistently expressed across different cell lines and conditions [5] [6] [7] and clustering is associated with the low sensitivity in determining classification [8]. "
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    ABSTRACT: Discriminating cell types is a daily request for stem cell biologists. However, there is not a user-friendly system available to date for public users to discriminate the common cell types, embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and somatic cells (SCs). Here, we develop WCTDS, a web-server of cell type discrimination system, to discriminate the three cell types and their subtypes like fetal versus adult SCs. WCTDS is developed as a top layer application of our recent publication regarding cell type discriminations, which employs DNA-methylation as biomarkers and machine learning models to discriminate cell types. Implemented by Django, Python, R, and Linux shell programming, run under Linux-Apache web server, and communicated through MySQL, WCTDS provides a friendly framework to efficiently receive the user input and to run mathematical models for analyzing data and then to present results to users. This framework is flexible and easy to be expended for other applications. Therefore, WCTDS works as a user-friendly framework to discriminate cell types and subtypes and it can also be expended to detect other cell types like cancer cells.
    The Scientific World Journal 01/2014; 2014:459064. DOI:10.1155/2014/459064 · 1.73 Impact Factor
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    • "Transcriptome deconvolution has been used to identify contributions of specific genes during the process of somatic cell reprogramming (37), and a variety of criteria unique to high throughput RNA analysis that derive from diverse RNA heterogeneity (38) play critical roles in elucidating transcriptome dynamics of differentiation. An essential characteristic of the transcriptome is (auto)regulation facilitated by subtle intra-RNA dynamics (39,40), however interactions of regulatory non-coding RNA with molecular targets can be parsed using specific bioinformatic resources. "
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    ABSTRACT: Development of innovative high throughput technologies has enabled a variety of molecular landscapes to be interrogated with an unprecedented degree of detail. Emergence of next generation nucleotide sequencing methods, advanced proteomic techniques, and metabolic profiling approaches continue to produce a wealth of biological data that captures molecular frameworks underlying phenotype. The advent of these novel technologies has significant translational applications, as investigators can now explore molecular underpinnings of developmental states with a high degree of resolution. Application of these leading-edge techniques to patient samples has been successfully used to unmask nuanced molecular details of disease vs healthy tissue, which may provide novel targets for palliative intervention. To enhance such approaches, concomitant development of algorithms to reprogram differentiated cells in order to recapitulate pluripotent capacity offers a distinct advantage to advancing diagnostic methodology. Bioinformatic deconvolution of several "-omic" layers extracted from reprogrammed patient cells, could, in principle, provide a means by which the evolution of individual pathology can be developmentally monitored. Significant logistic challenges face current implementation of this novel paradigm of patient treatment and care, however, several of these limitations have been successfully addressed through continuous development of cutting edge in silico archiving and processing methods. Comprehensive elucidation of genomic, transcriptomic, proteomic, and metabolomic networks that define normal and pathological states, in combination with reprogrammed patient cells are thus poised to become high value resources in modern diagnosis and prognosis of patient disease.
    Croatian Medical Journal 08/2013; 54(4):319-329. DOI:10.3325//cmj.2013.54.319 · 1.31 Impact Factor
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