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Dissecting direct reprogramming through integrative genomic analysis

Nature volume 454, pages 49–55 (2008)Cite this article

A Corrigendum to this article was published on 07 August 2008

Abstract

Somatic cells can be reprogrammed to a pluripotent state through the ectopic expression of defined transcription factors. Understanding the mechanism and kinetics of this transformation may shed light on the nature of developmental potency and suggest strategies with improved efficiency or safety. Here we report an integrative genomic analysis of reprogramming of mouse fibroblasts and B lymphocytes. Lineage-committed cells show a complex response to the ectopic expression involving induction of genes downstream of individual reprogramming factors. Fully reprogrammed cells show gene expression and epigenetic states that are highly similar to embryonic stem cells. In contrast, stable partially reprogrammed cell lines show reactivation of a distinctive subset of stem-cell-related genes, incomplete repression of lineage-specifying transcription factors, and DNA hypermethylation at pluripotency-related loci. These observations suggest that some cells may become trapped in partially reprogrammed states owing to incomplete repression of transcription factors, and that DNA de-methylation is an inefficient step in the transition to pluripotency. We demonstrate that RNA inhibition of transcription factors can facilitate reprogramming, and that treatment with DNA methyltransferase inhibitors can improve the overall efficiency of the reprogramming process.

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Figure 1: Gene expression profiling.
Figure 2: Chromatin state maps.
Figure 3: DNA methylation analysis.
Figure 4: Inhibition of Dnmt1 accelerates the transition to pluripotency.
Figure 5: Transcription factor knockdown facilitates reprogramming.

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Gene Expression Omnibus

Data deposits

All analysed data sets can be obtained from http://www.broad.mit.edu/seq_platform/chip/. Microarray and sequence data have been submitted to the NCBI GEO database under accession numbers GSE10871 and GSE11074, respectively.

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Acknowledgements

We thank the staff of the Broad Institute Genome Sequencing Platform, Genetic Analysis Platform and RNAi Platform for assistance with reagents and data generation. This research was supported by funds from the National Institutes of Health, the National Human Genome Research Institute, the National Cancer Institute, and the Broad Institute of MIT and Harvard.

Author information

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA ,

    Tarjei S. Mikkelsen, Xiaolan Zhang, Bradley E. Bernstein, Eric S. Lander & Alexander Meissner

  2. Division of Health Sciences and Technology,,

    Tarjei S. Mikkelsen

  3. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA,

    Rudolf Jaenisch & Eric S. Lander

  4. Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, Massachusetts 02142, USA ,

    Jacob Hanna, Marius Wernig, Patrick Schorderet, Rudolf Jaenisch & Eric S. Lander

  5. Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA ,

    Manching Ku & Bradley E. Bernstein

  6. Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA,

    Bradley E. Bernstein

  7. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02114, USA,

    Eric S. Lander

  8. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA,

    Alexander Meissner

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  1. Tarjei S. Mikkelsen
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Corresponding author

Correspondence to Alexander Meissner.

Supplementary information

Supplementary information

The file contains Supplementary Figures S1-S15 with Legends. (PDF 10919 kb)

Supplementary information

The file contains Supplementary Data S1 with all microarray data analyzed in the manuscript (RMA normalized and post-processed). (XLS 13923 kb)

Supplementary information

The file contains Supplementary Data S2 with positions, CpG classifications and chromatin states for all promoters analyzed in the manuscript. (XLS 3872 kb)

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Mikkelsen, T., Hanna, J., Zhang, X. et al. Dissecting direct reprogramming through integrative genomic analysis. Nature 454, 49–55 (2008). https://doi.org/10.1038/nature07056

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