Incomplete DNA methylation underlies a transcriptional memory of somatic cells in human iPS cells
TL;DR: In this article, a systematic comparison of human induced pluripotent stem (iPS) cells generated from hepatocytes (representative of endoderm), skin fibroblasts (mesoderm) and melanocytes (ectoderm).
Abstract: Human induced pluripotent stem (iPS) cells are remarkably similar to embryonic stem (ES) cells, but recent reports indicate that there may be important differences between them. We carried out a systematic comparison of human iPS cells generated from hepatocytes (representative of endoderm), skin fibroblasts (mesoderm) and melanocytes (ectoderm). All low-passage iPS cells analysed retain a transcriptional memory of the original cells. The persistent expression of somatic genes can be partially explained by incomplete promoter DNA methylation. This epigenetic mechanism underlies a robust form of memory that can be found in iPS cells generated by multiple laboratories using different methods, including RNA transfection. Incompletely silenced genes tend to be isolated from other genes that are repressed during reprogramming, indicating that recruitment of the silencing machinery may be inefficient at isolated genes. Knockdown of the incompletely reprogrammed gene C9orf64 (chromosome 9 open reading frame 64) reduces the efficiency of human iPS cell generation, indicating that somatic memory genes may be functionally relevant during reprogramming.
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University of Düsseldorf1, Goethe University Frankfurt2, Vrije Universiteit Brussel3, University of Freiburg4, Leipzig University5, Dow Chemical Company6, University of Tübingen7, Durham University8, University of Turin9, Seoul National University10, University of Jena11, Charité12, University of Valencia13, Heidelberg University14, University of Paris15, French Institute for Research in Computer Science and Automation16, University of Liverpool17, University of Groningen18, Lund University19, University of Manchester20, South Valley University21, University Hospital Regensburg22, Merck KGaA23, Musashino University24, University of Kansas25, Ludwig Maximilian University of Munich26, Fresenius Medical Care27, University of Regensburg28, Merck & Co.29
TL;DR: This review encompasses the most important advances in liver functions and hepatotoxicity and analyzes which mechanisms can be studied in vitro and how closely hepatoma, stem cell and iPS cell–derived hepatocyte-like-cells resemble real hepatocytes.
Abstract: This review encompasses the most important advances in liver functions and hepatotoxicity and analyzes which mechanisms can be studied in vitro. In a complex architecture of nested, zonated lobules, the liver consists of approximately 80 % hepatocytes and 20 % non-parenchymal cells, the latter being involved in a secondary phase that may dramatically aggravate the initial damage. Hepatotoxicity, as well as hepatic metabolism, is controlled by a set of nuclear receptors (including PXR, CAR, HNF-4α, FXR, LXR, SHP, VDR and PPAR) and signaling pathways. When isolating liver cells, some pathways are activated, e.g., the RAS/MEK/ERK pathway, whereas others are silenced (e.g. HNF-4α), resulting in up- and downregulation of hundreds of genes. An understanding of these changes is crucial for a correct interpretation of in vitro data. The possibilities and limitations of the most useful liver in vitro systems are summarized, including three-dimensional culture techniques, co-cultures with non-parenchymal cells, hepatospheres, precision cut liver slices and the isolated perfused liver. Also discussed is how closely hepatoma, stem cell and iPS cell-derived hepatocyte-like-cells resemble real hepatocytes. Finally, a summary is given of the state of the art of liver in vitro and mathematical modeling systems that are currently used in the pharmaceutical industry with an emphasis on drug metabolism, prediction of clearance, drug interaction, transporter studies and hepatotoxicity. One key message is that despite our enthusiasm for in vitro systems, we must never lose sight of the in vivo situation. Although hepatocytes have been isolated for decades, the hunt for relevant alternative systems has only just begun.
1,085 citations
TL;DR: The ability to restore pluripotency to somatic cells through the ectopic co-expression of reprogramming factors has created powerful new opportunities for modelling human diseases and offers hope for personalized regenerative cell therapies.
Abstract: The field of stem-cell biology has been catapulted forward by the startling development of reprogramming technology. The ability to restore pluripotency to somatic cells through the ectopic co-expression of reprogramming factors has created powerful new opportunities for modelling human diseases and offers hope for personalized regenerative cell therapies. While the field is racing ahead, some researchers are pausing to evaluate whether induced pluripotent stem cells are indeed the true equivalents of embryonic stem cells and whether subtle differences between these types of cell might affect their research applications and therapeutic potential.
1,064 citations
TL;DR: EBSeq is developed, using the merits of empirical Bayesian methods, for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions and proves to be a robust approach for identifying De genes.
Abstract: Motivation: Messenger RNA expression is important in normal development and differentiation, as well as in manifestation of disease. RNA-seq experiments allow for the identification of differentially expressed (DE) genes and their corresponding isoforms on a genome-wide scale. However, statistical methods are required to ensure that accurate identifications are made. A number of methods exist for identifying DE genes, but far fewer are available for identifying DE isoforms. When isoform DE is of interest, investigators often apply gene-level (count-based) methods directly to estimates of isoform counts. Doing so is not recommended. In short, estimating isoform expression is relatively straightforward for some groups of isoforms, but more challenging for others. This results in estimation uncertainty that varies across isoform groups. Count-based methods were not designed to accommodate this varying uncertainty, and consequently, application of them for isoform inference results in reduced power for some classes of isoforms and increased false discoveries for others. Results: Taking advantage of the merits of empirical Bayesian methods, we have developed EBSeq for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions. Results demonstrate substantially improved power and performance of EBSeq for identifying DE isoforms. EBSeq also proves to be a robust approach for identifying DE genes. Availability and implementation: An R package containing examples and sample datasets is available at http://www.biostat.wisc.edu/ � kendzior/EBSEQ/.
1,048 citations
TL;DR: The progress in applications of iPSC technology that are particularly relevant to drug discovery and regenerative medicine are discussed, and the remaining challenges and the emerging opportunities in the field are considered.
Abstract: Since the advent of induced pluripotent stem cell (iPSC) technology a decade ago, human iPSCs have been widely used for disease modelling, drug discovery and cell therapy development. This article discusses progress in applications of iPSC technology that are particularly relevant to drug discovery and regenerative medicine, including the powerful combination of human iPSC technology with recent developments in gene editing.
985 citations
TL;DR: The development of iPSCs reflected the merging of three major scientific streams and has in turn led to additional new branches of investigation, but there is still debate about whetheriPSCs are functionally equivalent to ESCs.
748 citations
Cites background from "Incomplete DNA methylation underlie..."
...…making progress toward using iPSCs in regenerative medicine, for example for the treatment of Parkinson’s disease (Kriks et al., 2011), platelet deficiency (Takayama et al., 2010), spinal cord injury (Nori et al., 2011; Tsuji et al., 2010), and macular degeneration (Okamoto and Takahashi, 2011)....
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References
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TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
Abstract: SUMMARY In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip R � system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip R � arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth’s Genetics Institute involving 95 HG-U95A human GeneChip R � arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip R � arrays. We display some familiar features of the perfect match and mismatch probe ( PM and MM )v alues of these data, and examine the variance–mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix’s (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multiarray average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities. ∗ To whom correspondence should be addressed
10,711 citations
TL;DR: This paper proposed parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples.
Abstract: SUMMARY Non-biological experimental variation or “batch effects” are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
6,319 citations
TL;DR: The first genome-wide, single-base-resolution maps of methylated cytosines in a mammalian genome, from both human embryonic stem cells and fetal fibroblasts, along with comparative analysis of messenger RNA and small RNA components of the transcriptome, several histone modifications, and sites of DNA-protein interaction for several key regulatory factors were presented in this article.
Abstract: DNA cytosine methylation is a central epigenetic modification that has essential roles in cellular processes including genome regulation, development and disease. Here we present the first genome-wide, single-base-resolution maps of methylated cytosines in a mammalian genome, from both human embryonic stem cells and fetal fibroblasts, along with comparative analysis of messenger RNA and small RNA components of the transcriptome, several histone modifications, and sites of DNA-protein interaction for several key regulatory factors. Widespread differences were identified in the composition and patterning of cytosine methylation between the two genomes. Nearly one-quarter of all methylation identified in embryonic stem cells was in a non-CG context, suggesting that embryonic stem cells may use different methylation mechanisms to affect gene regulation. Methylation in non-CG contexts showed enrichment in gene bodies and depletion in protein binding sites and enhancers. Non-CG methylation disappeared upon induced differentiation of the embryonic stem cells, and was restored in induced pluripotent stem cells. We identified hundreds of differentially methylated regions proximal to genes involved in pluripotency and differentiation, and widespread reduced methylation levels in fibroblasts associated with lower transcriptional activity. These reference epigenomes provide a foundation for future studies exploring this key epigenetic modification in human disease and development.
4,266 citations
TL;DR: It is shown that this approach can reprogram multiple human cell types to pluripotency with efficiencies that greatly surpass established protocols and represents a safe, efficient strategy for somatic cell reprogramming and directing cell fate that has broad applicability for basic research, disease modeling, and regenerative medicine.
2,627 citations
TL;DR: Results demonstrate that reprograming human somatic cells does not require genomic integration or the continued presence of exogenous reprogramming factors and removes one obstacle to the clinical application of human iPS cells.
Abstract: Reprogramming differentiated human cells to induced pluripotent stem (iPS) cells has applications in basic biology, drug development, and transplantation. Human iPS cell derivation previously required vectors that integrate into the genome, which can create mutations and limit the utility of the cells in both research and clinical applications. We describe the derivation of human iPS cells with the use of nonintegrating episomal vectors. After removal of the episome, iPS cells completely free of vector and transgene sequences are derived that are similar to human embryonic stem (ES) cells in proliferative and developmental potential. These results demonstrate that reprogramming human somatic cells does not require genomic integration or the continued presence of exogenous reprogramming factors and removes one obstacle to the clinical application of human iPS cells.
2,425 citations