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Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells

Nature volume 471, pages 68–73 (2011)Cite this article

A Corrigendum to this article was published on 01 October 2014

Abstract

Induced pluripotent stem cells (iPSCs) offer immense potential for regenerative medicine and studies of disease and development. Somatic cell reprogramming involves epigenomic reconfiguration, conferring iPSCs with characteristics similar to embryonic stem (ES) cells. However, it remains unknown how complete the reestablishment of ES-cell-like DNA methylation patterns is throughout the genome. Here we report the first whole-genome profiles of DNA methylation at single-base resolution in five human iPSC lines, along with methylomes of ES cells, somatic cells, and differentiated iPSCs and ES cells. iPSCs show significant reprogramming variability, including somatic memory and aberrant reprogramming of DNA methylation. iPSCs share megabase-scale differentially methylated regions proximal to centromeres and telomeres that display incomplete reprogramming of non-CG methylation, and differences in CG methylation and histone modifications. Lastly, differentiation of iPSCs into trophoblast cells revealed that errors in reprogramming CG methylation are transmitted at a high frequency, providing an iPSC reprogramming signature that is maintained after differentiation.

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Figure 1: Global trends of human iPSC and ES cell DNA methylomes.
Figure 2: Partially methylated domains become highly methylated on induction of pluripotency.
Figure 3: CG-DMRs identified between pluripotent cells.
Figure 4: Characterization of CG-DMRs in iPSCs.
Figure 5: Failure to restore megabase-scale regions of non-CG methylation is a hallmark of iPSC reprogramming.

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Primary accessions

Sequence Read Archive

Data deposits

Analysed datasets can be browsed and downloaded from http://neomorph.salk.edu/ips_methylomes. Sequence data for MethylC-Seq, RNA-Seq and Chip-Seq experiments have been submitted to the NCBI SRA database under the accession numbers SRA023829.2 and SRP000941.

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Acknowledgements

We thank L. Zhang and G. Schroth for assistance with MethylC-Seq library sequencing. R.L. is supported by a California Institute for Regenerative Medicine Training Grant. M.P. is supported by a Catharina Foundation postdoctoral fellowship. R.D.H. is supported by an American Cancer Society Postdoctoral Fellowship. Y.K. is supported by the Japan Society for the Promotion of Science. This work was supported by grants from the following: Mary K. Chapman Foundation, the National Science Foundation (NSF) (NSF 0726408), the National Institutes of Health (NIH) (U01 ES017166, U01 1U01ES017166-01, DK062434), the California Institute for Regenerative Medicine (RB2-01530), the Morgridge Institute for Research and the Howard Hughes Medical Institute. We thank the NIH Roadmap Reference Epigenome Consortium (http://www.roadmapepigenomics.org/). This study was carried out as part of the NIH Roadmap Epigenomics Program.

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Author notes

  1. Ryan Lister and Mattia Pelizzola: These authors contributed equally to this work.

Authors and Affiliations

  1. Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, 92037, California, USA

    Ryan Lister, Mattia Pelizzola, Joseph R. Nery, Ronan O’Malley, Rosa Castanon & Joseph R. Ecker

  2. Howard Hughes Medical Institute, Gene Expression laboratory, The Salk Institute for Biological Studies, La Jolla, 92037, California, USA

    Yasuyuki S. Kida, Michael Downes, Ruth Yu & Ronald M. Evans

  3. Ludwig Institute for Cancer Research, 9500 Gilman Drive, La Jolla, 92093, California, USA

    R. David Hawkins, Gary Hon, Sarit Klugman & Bing Ren

  4. Morgridge Institute for Research, Madison, 53707, Wisconsin, USA

    Jessica Antosiewicz-Bourget, Ron Stewart & James A. Thomson

  5. Genome Center of Wisconsin, Madison, 53706, Wisconsin, USA

    Jessica Antosiewicz-Bourget, Ron Stewart & James A. Thomson

  6. Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, 92093, California, USA

    Bing Ren

  7. Wisconsin National Primate Research Center, University of Wisconsin—Madison, Madison, 53715, Wisconsin, USA

    James A. Thomson

  8. Department of Anatomy, University of Wisconsin—Madison, Madison, 53706, Wisconsin, USA

    James A. Thomson

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  1. Ryan Lister
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Contributions

Experiments were designed by R.L., J.R.E., R.M.E., B.R., J.A.T., Y.S.K., R.Y., M.D. and R.D.H. Cells were grown by J.A.-B. and Y.S.K. MethylC-Seq and RNA-Seq experiments were conducted by R.L. and J.R.N. ChIP-Seq experiments were conducted by R.D.H. ChIP-Seq data analysis was performed by G.H., S.K. and R.D.H. Retroviral insertion site localization experiments were performed by R.O’M. and R.C. Sequencing data processing was performed by R.L. and G.H. Bioinformatic and statistical analyses were conducted by M.P., R.L. and G.H. R.S. performed data interpretation analyses. The manuscript was prepared by R.L., M.P. and J.R.E.

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Correspondence to Joseph R. Ecker.

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The authors declare no competing financial interests.

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Lister, R., Pelizzola, M., Kida, Y. et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471, 68–73 (2011). https://doi.org/10.1038/nature09798

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  1. Mark van de Wiel

    THE COMMENT BELOW WAS SUBMITTED TO NATURE FOR BRIEF COMMENTS ARISING, BUT DECLINED FOR PUBLICATION DUE TO TECHNICAL CONCERNS. HENCE, I POST IT HERE.

    Fictive replicates in epigenomic analysis

    Arising from Lister et al. Nature 471, 68-73 (2011)

    For genome sequencing studies on cell lines reseachers might be tempted to not spend resources on replication, given the presumably small variation between replicates. Replication, however, is an important condition for any statistical inference [need reference]. Such replicates are absent for the comparisons of epigenomes of several embryonic stem (ES) cell lines and induced pluripotent stem cell (iPSC) lines in Lister et al. Yet, the authors make ubiquitous use of (adjusted) p-values for the detection of differentially methylated regions (DMRs): Lister et al. create fictive replicates by scanning the genome using 10 consecutive genomic windows as independent samples and computing (adjusted) p-values from those ?replicates?. We argue that such practice is likely to lead to many false discoveries.

    Lister et al. perform a number of comparisons for both CG and non-CG methylation. We focus on the comparison between the ES cell line H1 to an iPSC line, ADS-iPSC , but the arguments are equally valid for other comparisons. Lister et al. first smooth the methylation levels across the genome, then average methylation levels within windows of 100 base pairs (5000 for non-CG) and compare nW = 10 consecutive windows using a Wilcoxon two-sample test. Resulting p-values are adjusted using Benjamini- Hochberg False Discovery Rate (BH- FDR). The author?s choice nW = 10 immediately raises the question: why not 5 consecutive windows, or 20? In addition, it seems evident that observations from consecutive genomic windows are inherently dependent (correlated), in particular after smoothing, which is similar to computing averages for overlapping windows. Smoothing is useful for visualization, but is certainly not appropriate when genomic windows are used as ?replicates?. The resulting underestimated variation between ?replicates? leads to too optimistic (adjusted) p-values. For example, if the standard deviation (sd) is underestimated by a factor 2 with respect to truly independent replicates, Wilcoxon p-values (based on 2x10 fictive replicates) smaller than 0.01 are 10-100 fold too low with respect to those corresponding to true replicates. Given that BH-FDR adjusted p-values are proportional to raw p-values, the bias of the reported FDR may be huge.

    Following the Methods in Lister et al. we re-analyzed the data using nW= 5, 10 and 20 consecutive windows and applied the analysis also to the non-smoothed data for nW = 10. The results (Table 1) justify two conclusions: a) the fictive sample size has a large effect on the coverage of the detected DMRs, and for CG and CHH no DMRs are detected for nW= 5; b) without smoothing no DMRs are detected when using nW=10. Given that smoothing is inappropriate here and the sample size is fictive why should we then believe that the reported FDR = 0.01 by Lister et al. is indeed anywhere near 1%? The true proportion of false discoveries is likely to be several orders of magnitudes larger.

    Lister et al. use additional fold change criteria, which may prevent some degree of over-selection. Note, however, that the applied criterion varies tremendously between comparisons: from 2-fold to 8-fold. Given the absence of a biological reason for this difference, one can only speculate that the fold change criterion is used to produce a list of DMRs that is of a convenient size.
    The consequences of the methodological errors are two-fold. First, many of the DMRs will not validate in independent studies. Some of these may have been validated in other studies, but note that invalidated results are often not published [add reference]. Second, others may be tempted to use the same analysis for other studies. We hope to prevent the latter and emphasize that there is only one solution for obtaining proper p-values: replication, replication, replication.

    Table 1
    nW=5 nW =10 nW =20 nW =10
    Smoothed&#009Yes Yes Yes No
    CG # &#009 0&#009 902918&#009 825187 &#009 0
    CG %&#009 0&#009 31.4&#009 57.4&#009 0
    CHH #&#009 0&#009 50823&#009 25453&#009 0
    CHH %&#009 0&#009 88.4&#009 88.5&#009 0
    CHG #&#009 98214&#009 52229&#009 26142&#009 0
    CHG %&#009 85.4&#009 90.8&#009 90.9&#009 0
    Number (#) of detected DMRs and genomic coverage of those (%) for CG, CHH and CHG methylation

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