Abstract

Human pluripotent stem cells hold potential for regenerative medicine, but available cell types have significant limitations. Although embryonic stem cells (ES cells) from in vitro fertilized embryos (IVF ES cells) represent the ‘gold standard’, they are allogeneic to patients. Autologous induced pluripotent stem cells (iPS cells) are prone to epigenetic and transcriptional aberrations. To determine whether such abnormalities are intrinsic to somatic cell reprogramming or secondary to the reprogramming method, genetically matched sets of human IVF ES cells, iPS cells and nuclear transfer ES cells (NT ES cells) derived by somatic cell nuclear transfer (SCNT) were subjected to genome-wide analyses. Both NT ES cells and iPS cells derived from the same somatic cells contained comparable numbers of de novo copy number variations. In contrast, DNA methylation and transcriptome profiles of NT ES cells corresponded closely to those of IVF ES cells, whereas iPS cells differed and retained residual DNA methylation patterns typical of parental somatic cells. Thus, human somatic cells can be faithfully reprogrammed to pluripotency by SCNT and are therefore ideal for cell replacement therapies.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Gene Expression Omnibus

Data deposits

Processed data sets can be downloaded from the NCBI GEO under accession GSE53096 for RNA-seq, SNP array and 450K methylation array, and accession GSE57179 for MethylC-seq data. Analysed MethylC-seq data sets can also be accessed at http://neomorph.salk.edu/SCNT/browser.html.

References

  1. 1.

    et al. Embryonic stem cell lines derived from human blastocysts. Science 282, 1145–1147 (1998)

  2. 2.

    et al. Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 131, 861–872 (2007)

  3. 3.

    et al. Deterministic direct reprogramming of somatic cells to pluripotency. Nature 502, 65–70 (2013)

  4. 4.

    et al. Copy number variation and selection during reprogramming to pluripotency. Nature 471, 58–62 (2011)

  5. 5.

    et al. Dynamic changes in the copy number of pluripotency and cell proliferation genes in human ESCs and iPSCs during reprogramming and time in culture. Cell Stem Cell 8, 106–118 (2011)

  6. 6.

    et al. Analysis of protein-coding mutations in hiPSCs and their possible role during somatic cell reprogramming. Nature Commun. 4, 1382 (2013)

  7. 7.

    et al. Recurrent variations in DNA methylation in human pluripotent stem cells and their differentiated derivatives. Cell Stem Cell 10, 620–634 (2012)

  8. 8.

    et al. Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells. Nature 471, 68–73 (2011)

  9. 9.

    et al. Incomplete DNA methylation underlies a transcriptional memory of somatic cells in human iPS cells. Nature Cell Biol. 13, 541–549 (2011)

  10. 10.

    et al. Identification of a specific reprogramming-associated epigenetic signature in human induced pluripotent stem cells. Proc. Natl Acad. Sci. USA 109, 16196–16201 (2012)

  11. 11.

    et al. Human embryonic stem cells derived by somatic cell nuclear transfer. Cell 153, 1228–1238 (2013)

  12. 12.

    et al. Generation of human induced pluripotent stem cells from dermal fibroblasts. Proc. Natl Acad. Sci. USA 105, 2883–2888 (2008)

  13. 13.

    , , , & Efficient induction of transgene-free human pluripotent stem cells using a vector based on Sendai virus, an RNA virus that does not integrate into the host genome. Proc. Jpn Acad. B 85, 348–362 (2009)

  14. 14.

    & Mitochondrial DNA mutations in human disease. Nature Rev. Genet. 6, 389–402 (2005)

  15. 15.

    et al. Reference maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines. Cell 144, 439–452 (2011)

  16. 16.

    & Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006)

  17. 17.

    et al. Genomic distribution and inter-sample variation of non-CpG methylation across human cell types. PLoS Genet. 7, e1002389 (2011)

  18. 18.

    et al. Identification of novel imprinted differentially methylated regions by global analysis of human-parthenogenetic-induced pluripotent stem cells. Stem Cell Rep. 1, 79–89 (2013)

  19. 19.

    , & Status of genomic imprinting in human embryonic stem cells as revealed by a large cohort of independently derived and maintained lines. Hum. Mol. Genet. 16, R243–R251 (2007)

  20. 20.

    , , & Open source clustering software. Bioinformatics 20, 1453–1454 (2004)

  21. 21.

    Java Treeview–extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004)

  22. 22.

    , , & X-chromosome inactivation and epigenetic fluidity in human embryonic stem cells. Proc. Natl Acad. Sci. USA 105, 4820–4825 (2008)

  23. 23.

    et al. XACT, a long noncoding transcript coating the active X chromosome in human pluripotent cells. Nature Genet. 45, 239–241 (2013)

  24. 24.

    & AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number. BMC Bioinformatics 11, 117 (2010)

  25. 25.

    et al. GREAT improves functional interpretation of cis-regulatory regions. Nature Biotechnol. 28, 495–501 (2010)

  26. 26.

    et al. DNA methylation dynamics in human induced pluripotent stem cells over time. PLoS Genet. 7, e1002085 (2011)

  27. 27.

    et al. Cell type of origin influences the molecular and functional properties of mouse induced pluripotent stem cells. Nature Biotechnol. 28, 848–855 (2010)

  28. 28.

    et al. Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell 153, 1134–1148 (2013)

  29. 29.

    et al. Dynamic changes in the human methylome during differentiation. Genome Res. 20, 320–331 (2010)

  30. 30.

    et al. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905 (2013)

  31. 31.

    et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009)

  32. 32.

    et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genet. 25, 25–29 (2000)

  33. 33.

    et al. Somatic coding mutations in human induced pluripotent stem cells. Nature 471, 63–67 (2011)

  34. 34.

    et al. Epigenetic memory in induced pluripotent stem cells. Nature 467, 285–290 (2010)

  35. 35.

    , & Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics 8, 118–127 (2007)

  36. 36.

    et al. Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin 6, 4 (2013)

  37. 37.

    Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, (2011)

  38. 38.

    et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)

  39. 39.

    , & Computing the confidence levels for a root-mean-square test of goodness-of-fit. Appl. Math. Comput. 217, 9072–9084 (2011)

  40. 40.

    , & Estimation of false discovery rate using sequential permutation p-values. Biometrics 69, 1–7 (2013)

  41. 41.

    , & ‘Leveling’ the playing field for analyses of single-base resolution DNA methylomes. Trends Genet. 28, 583–585 (2012)

  42. 42.

    , & Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 44–57 (2009)

  43. 43.

    , & Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009)

Download references

Acknowledgements

The authors acknowledge the OHSU Embryonic Stem Cell Research Oversight Committee and the Institutional Review Board for providing oversight and guidance. We thank oocyte and sperm donors and the Women’s Health Research Unit staff at the Center for Women’s Health, University Fertility Consultants and the Reproductive Endocrinology and Infertility Division in the Department of Obstetrics and Gynecology of Oregon Health and Science University for their support and procurement of human gametes. We are grateful to C. Penedo for microsatellite analysis and W. Sanger and D. Zaleski for karyotyping services. We are also indebted to Y. Li, H. Sritanaudomchai and D. Melguizo Sanchis for their technical support. We thank the staff at the Institute for Genomic Medicine Genomics Facility at UCSD for running the Infinium HumanMethylation450 BeadChips and sequencing of the RNA-seq libraries. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin (http://www.tacc.utexas.edu) and the San Diego Supercomputing Center (through an allocation from the eXtreme Science and Engineering Discovery Environment (XSEDE)) for providing HPC resources that have contributed to the research results reported within this paper. SCNT and iPS cell studies were supported by grants from the Leducq Foundation and OHSU institutional funds. R.M., K.S., R.T. and L.C.L. were supported by the UCSD Department of Reproductive Medicine. Methylome studies were supported by the Salk International Council Chair fund endowment and the Mary K. Chapman Foundation to J.R.E. J.R.E. is an investigator of the Howard Hughes Medical Institute and the Gordon and Betty Moore Foundation (GMBF3034). A.P. received a fellowship from the Swedish Research Council, Vetenskapsrådet. E.K. was partially funded by a fellowship from the Collins Medical Trust.

Author information

Author notes

    • Hong Ma
    •  & Robert Morey

    These authors contributed equally to this work.

    • Masahito Tachibana
    •  & Alim Polat

    Present addresses: Department of Obstetrics and Gynecology, South Miyagi Medical Center, Shibata-gun, Miyagi 989-1253, Japan (M.T.); Department of Cell and Molecular Biology, Karolinska Institutet, SE-17177 Stockholm, Sweden (A.P.).

Affiliations

  1. Center for Embryonic Cell and Gene Therapy, Oregon Health & Science University, 3303 Southwest Bond Avenue, Portland, Oregon 97239, USA

    • Hong Ma
    • , Brittany Daughtry
    • , Eunju Kang
    • , Rebecca Tippner-Hedges
    • , Riffat Ahmed
    • , Nuria Marti Gutierrez
    • , Crystal Van Dyken
    •  & Shoukhrat Mitalipov
  2. Division of Reproductive and Developmental Sciences, Oregon National Primate Research Center, Oregon Health & Science University, 505 Northwest 185th Avenue, Beaverton, Oregon 97006, USA

    • Hong Ma
    • , Brittany Daughtry
    • , Masahito Tachibana
    • , Eunju Kang
    • , Rebecca Tippner-Hedges
    • , Riffat Ahmed
    • , Nuria Marti Gutierrez
    • , Crystal Van Dyken
    • , Alim Polat
    • , Atsushi Sugawara
    • , Michelle Sparman
    • , Don P.Wolf
    •  & Shoukhrat Mitalipov
  3. Department of Reproductive Medicine, University of California, San Diego, Sanford Consortium for Regenerative Medicine, 2880 Torrey Pines Scenic Drive, La Jolla, California 92037, USA

    • Robert Morey
    • , Karen Sabatini
    • , Rathi D. Thiagarajan
    •  & Louise C. Laurent
  4. Genomic Analysis Laboratory, the Salk Institute for Biological Studies, La Jolla, California 92037, USA

    • Ryan C. O'Neil
    • , Yupeng He
    • , Matthew D. Schultz
    • , Manoj Hariharan
    • , Joseph R. Nery
    • , Rosa Castanon
    •  & Joseph R. Ecker
  5. Bioinformatics Program, University of California at San Diego, La Jolla, California 92093, USA

    • Ryan C. O'Neil
    •  & Yupeng He
  6. University Pathologists LLC, Boston University School of Medicine, Roger Williams Medical Center, Providence, Rhode Island 02118, USA

    • Sumita Gokhale
  7. Division of Reproductive Endocrinology, Department of Obstetrics and Gynecology, Oregon Health & Science University, 3181 Southwest Sam Jackson Park Road, Portland, Oregon 97239, USA

    • Paula Amato
    •  & Shoukhrat Mitalipov
  8. Howard Hughes Medical Institute, the Salk Institute for Biological Studies, La Jolla, California 92037, USA

    • Joseph R. Ecker

Authors

  1. Search for Hong Ma in:

  2. Search for Robert Morey in:

  3. Search for Ryan C. O'Neil in:

  4. Search for Yupeng He in:

  5. Search for Brittany Daughtry in:

  6. Search for Matthew D. Schultz in:

  7. Search for Manoj Hariharan in:

  8. Search for Joseph R. Nery in:

  9. Search for Rosa Castanon in:

  10. Search for Karen Sabatini in:

  11. Search for Rathi D. Thiagarajan in:

  12. Search for Masahito Tachibana in:

  13. Search for Eunju Kang in:

  14. Search for Rebecca Tippner-Hedges in:

  15. Search for Riffat Ahmed in:

  16. Search for Nuria Marti Gutierrez in:

  17. Search for Crystal Van Dyken in:

  18. Search for Alim Polat in:

  19. Search for Atsushi Sugawara in:

  20. Search for Michelle Sparman in:

  21. Search for Sumita Gokhale in:

  22. Search for Paula Amato in:

  23. Search for Don P.Wolf in:

  24. Search for Joseph R. Ecker in:

  25. Search for Louise C. Laurent in:

  26. Search for Shoukhrat Mitalipov in:

Contributions

H.M., R.M., L.C.L. and S.M. conceived the study and designed the experiments. P.A., M.S. and N.M.G. coordinated recruitment of gamete donors. P.A. performed ovarian stimulations and oocyte retrievals. M.T., M.S., N.M.G. and S.M. conducted SCNT, IVF and embryo culture experiments. R.T.-H., S.M., M.T., M.S., N.M.G., H.M., A.P., B.D., E.K., A.S. and R.A. derived and cultured IVF ES cells, NT ES cells and iPS cells. S.G. performed teratoma analysis. H.M., M.T. and C.V.D. performed the DNA and RNA extractions, mtDNA amplification refractory mutation system qPCR analyses, and qPCR. R.M., K.S., R.D.T. and L.C.L. performed SNP, DNA methylation and RNA-seq studies and bioinformatic analysis of the data. R.C.O., Y.H., M.D.S., M.H., J.R.N., R.C. and J.R.E. conducted MethylC-seq studies. H.M., R.M., R.C.O., Y.H., J.R.E., L.C.L., D.P.W. and S.M. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Joseph R. Ecker or Louise C. Laurent or Shoukhrat Mitalipov.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Tables 1-4.

Excel files

  1. 1.

    Supplementary Table 5

    List of CD DMRs

  2. 2.

    Supplementary Table 6

    List of non-CG mega-DMRs

  3. 3.

    Supplementary Table 7

    GO analysis results for genes within non-CG mega-DMRs

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nature13551

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.