The equivalence of human induced pluripotent stem cells (hiPSCs) and human embryonic stem cells (hESCs) remains controversial. Here we use genetically matched hESC and hiPSC lines to assess the contribution of cellular origin (hESC vs. hiPSC), the Sendai virus (SeV) reprogramming method and genetic background to transcriptional and DNA methylation patterns while controlling for cell line clonality and sex. We find that transcriptional and epigenetic variation originating from genetic background dominates over variation due to cellular origin or SeV infection. Moreover, the 49 differentially expressed genes we detect between genetically matched hESCs and hiPSCs neither predict functional outcome nor distinguish an independently derived, larger set of unmatched hESC and hiPSC lines. We conclude that hESCs and hiPSCs are molecularly and functionally equivalent and cannot be distinguished by a consistent gene expression signature. Our data further imply that genetic background variation is a major confounding factor for transcriptional and epigenetic comparisons of pluripotent cell lines, explaining some of the previously observed differences between genetically unmatched hESCs and hiPSCs.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


Primary accessions

Gene Expression Omnibus


  1. 1.

    & Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).

  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. Disease-specific induced pluripotent stem cells. Cell 134, 877–886 (2008).

  4. 4.

    et al. Induced pluripotent stem cell lines derived from human somatic cells. Science 318, 1917–1920 (2007).

  5. 5.

    et al. Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures. Cell Stem Cell 5, 111–123 (2009).

  6. 6.

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

  7. 7.

    , , & Molecular analyses of human induced pluripotent stem cells and embryonic stem cells. Cell Stem Cell 7, 263–269 (2010).

  8. 8.

    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).

  9. 9.

    , & Suppression of the imprinted gene NNAT and X-chromosome gene activation in isogenic human iPS cells. PLoS One 6, e23436 (2011).

  10. 10.

    et al. Proteomic and phosphoproteomic comparison of human ES and iPS cells. Nat. Methods 8, 821–827 (2011).

  11. 11.

    et al. Parkinson's disease patient-derived induced pluripotent stem cells free of viral reprogramming factors. Cell 136, 964–977 (2009).

  12. 12.

    et al. Ascorbic acid prevents loss of Dlk1-Dio3 imprinting and facilitates generation of all-iPS cell mice from terminally differentiated B cells. Nat. Genet. 44, 398–405, S1–S2 (2012).

  13. 13.

    & Lab-specific gene expression signatures in pluripotent stem cells. Cell Stem Cell 7, 258–262 (2010).

  14. 14.

    et al. Genetic background drives transcriptional variation in human induced pluripotent stem cells. PLoS Genet. 10, e1004432 (2014).

  15. 15.

    et al. Epigenetic instability in ES cells and cloned mice. Science 293, 95–97 (2001).

  16. 16.

    et al. Female human iPSCs retain an inactive X chromosome. Cell Stem Cell 7, 329–342 (2010).

  17. 17.

    et al. Molecular signatures of human induced pluripotent stem cells highlight sex differences and cancer genes. Cell Stem Cell 11, 75–90 (2012).

  18. 18.

    et al. Aberrant silencing of imprinted genes on chromosome 12qF1 in mouse induced pluripotent stem cells. Nature 465, 175–181 (2010).

  19. 19.

    , , , & 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., Ser. B. Phys. Biol. Sci. 85, 348–362 (2009).

  20. 20.

    et al. Derivation of embryonic stem-cell lines from human blastocysts. N. Engl. J. Med. 350, 1353–1356 (2004).

  21. 21.

    et al. Comparison of the molecular profiles of human embryonic and induced pluripotent stem cells of genetically matched origin. Stem Cell Res. (Amst.) 12, 376–386 (2014).

  22. 22.

    et al. Chromatin structure and gene expression programs of human embryonic and induced pluripotent stem cells. Cell Stem Cell 7, 249–257 (2010).

  23. 23.

    et al. A high-efficiency system for the generation and study of human induced pluripotent stem cells. Cell Stem Cell 3, 340–345 (2008).

  24. 24.

    & Lactate dehydrogenases: structure and function. Adv. Enzymol. 37, 61–133 (1973).

  25. 25.

    , & Attenuation of LDH-A expression uncovers a link between glycolysis, mitochondrial physiology, and tumor maintenance. Cancer Cell 9, 425–434 (2006).

  26. 26.

    et al. Sequence and structure of a human glucose transporter. Science 229, 941–945 (1985).

  27. 27.

    et al. Modulation of glucose transporter 1 (GLUT1) expression levels alters mouse mammary tumor cell growth in vitro and in vivo. PLoS One 6, e23205 (2011).

  28. 28.

    et al. HIF1α induced switch from bivalent to exclusively glycolytic metabolism during ESC-to-EpiSC/hESC transition. EMBO J. 31, 2103–2113 (2012).

  29. 29.

    , , & Expression of two novel mouse Iroquois homeobox genes during neurogenesis. Mech. Dev. 91, 317–321 (2000).

  30. 30.

    et al. The prepattern transcription factor Irx2, a target of the FGF8/MAP kinase cascade, is involved in cerebellum formation. Nat. Neurosci. 7, 605–612 (2004).

  31. 31.

    et al. Refinement and discovery of new hotspots of copy-number variation associated with autism spectrum disorder. Am. J. Hum. Genet. 92, 221–237 (2013).

  32. 32.

    et al. Structural variation of chromosomes in autism spectrum disorder. Am. J. Hum. Genet. 82, 477–488 (2008).

  33. 33.

    et al. Functional genomic screen of human stem cell differentiation reveals pathways involved in neurodevelopment and neurodegeneration. Proc. Natl. Acad. Sci. USA 110, 12361–12366 (2013).

  34. 34.

    et al. Highly efficient neural conversion of human ES and iPS cells by dual inhibition of SMAD signaling. Nat. Biotechnol. 27, 275–280 (2009).

  35. 35.

    et al. Pax6 is a human neuroectoderm cell fate determinant. Cell Stem Cell 7, 90–100 (2010).

  36. 36.

    et al. A qPCR ScoreCard quantifies the differentiation potential of human pluripotent stem cells. Nat. Biotechnol. (2015).

  37. 37.

    et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32, 926–932 (2014).

  38. 38.

    , , , & Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS One 9, e78644 (2014).

  39. 39.

    et al. Large intergenic non-coding RNA-RoR modulates reprogramming of human induced pluripotent stem cells. Nat. Genet. 42, 1113–1117 (2010).

  40. 40.

    et al. Somatic copy number mosaicism in human skin revealed by induced pluripotent stem cells. Nature 492, 438–442 (2012).

  41. 41.

    et al. Differentiation-defective phenotypes revealed by large-scale analyses of human pluripotent stem cells. Proc. Natl. Acad. Sci. USA 110, 20569–20574 (2013).

  42. 42.

    et al. Generation of isogenic pluripotent stem cells differing exclusively at two early onset Parkinson point mutations. Cell 146, 318–331 (2011).

  43. 43.

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

  44. 44.

    et al. Ensembl 2014. Nucleic acids research. 42, D749–755 (2014).

  45. 45.

    , & EMSAR: estimation of transcript abundance from RNA-seq data by mappability-based segmentation and reclustering. BMC bioinformatics 16, 278 (2010).

  46. 46.

    , & edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  47. 47.

    & Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

  48. 48.

    , & Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).

  49. 49.

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

  50. 50.

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

  51. 51.

    Well-separated clusters and optimal fuzzy partitions. J. Cybern. 4, 95–104 (1974).

  52. 52.

    Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).

  53. 53.

    et al. Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling. Genome Biol. 13, R92 (2012).

  54. 54.

    et al. The histone deacetylase Sirt6 regulates glucose homeostasis via Hif1α. Cell 140, 280–293 (2010).

  55. 55.

    et al. The histone deacetylase SIRT6 is a tumor suppressor that controls cancer metabolism. Cell 151, 1185–1199 (2012).

Download references


We thank members of the Hochedlinger and Park laboratories for productive discussions and a critical reading of the manuscript. We also thank M. Stadtfeld for his helpful discussions and D. Melton for his generous donation of HUES2 and HUES3 lines. We are grateful to K. Folz-Donahue, M. Weglarz and L. Prickett at the Massachusetts General Hospital (MGH)/Harvard Stem Cell Institute (HSCI) flow cytometry core for their constant assistance and support. We are also thankful to the members of the Tufts Genomics Core for performing RNA-seq. Work in the Lee laboratory was supported by grants from the Robertson Investigator Award of the New York Stem Cell Foundation and from the Maryland Stem Cell Research Fund (TEDCO). A.M. and J.L.R. are supported by US National Institutes of Health (NIH) grant P01GM099117. A.M. is a New York Stem Cell Foundation Robertson Investigator. Parts of this work were supported by the Howard Hughes Medical Institute (HHMI), MGH startup funds, the Gerald and Darlene Jordan Endowed Chair for Regenerative Medicine (to K.H.) and a pilot grant from the NIH (P01GM099117 to K.H.). J.C. was supported by the Vranos Family Graduate Research Fellowship in Developmental & Regenerative Biology.

Author information

Author notes

    • Jiho Choi
    •  & Soohyun Lee

    These authors contributed equally to this work.


  1. Department of Molecular Biology, Cancer Center and Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Jiho Choi
    •  & Konrad Hochedlinger
  2. Harvard Stem Cell Institute, Cambridge, Massachusetts, USA.

    • Jiho Choi
    • , Kendell Clement
    • , Alexander M Tsankov
    • , Ramona Pop
    • , Alexander Meissner
    •  & Konrad Hochedlinger
  3. Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.

    • Jiho Choi
    •  & Konrad Hochedlinger
  4. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.

    • Soohyun Lee
    • , Guidantonio Malagoli Tagliazucchi
    • , Francesco Ferrari
    •  & Peter J Park
  5. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • William Mallard
    • , Kendell Clement
    • , Alexander M Tsankov
    • , Ramona Pop
    • , John L Rinn
    •  & Alexander Meissner
  6. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA.

    • William Mallard
    • , Kendell Clement
    • , Alexander M Tsankov
    • , Ramona Pop
    • , John L Rinn
    •  & Alexander Meissner
  7. Center for Genome Research, Department of Life Sciences, University of Modena and Reggio Emilia, Modena, Italy.

    • Guidantonio Malagoli Tagliazucchi
  8. Institute for Cell Engineering, Department of Neurology, The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

    • Hotae Lim
    • , In Young Choi
    •  & Gabsang Lee
  9. Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.

    • John L Rinn


  1. Search for Jiho Choi in:

  2. Search for Soohyun Lee in:

  3. Search for William Mallard in:

  4. Search for Kendell Clement in:

  5. Search for Guidantonio Malagoli Tagliazucchi in:

  6. Search for Hotae Lim in:

  7. Search for In Young Choi in:

  8. Search for Francesco Ferrari in:

  9. Search for Alexander M Tsankov in:

  10. Search for Ramona Pop in:

  11. Search for Gabsang Lee in:

  12. Search for John L Rinn in:

  13. Search for Alexander Meissner in:

  14. Search for Peter J Park in:

  15. Search for Konrad Hochedlinger in:


J.C., S.L., P.J.P. and K.H. conceived the experiments, interpreted results and wrote the manuscript. J.C. generated all HUES2- and HUES3-derived in vitro-differentiated fibroblasts and hiPSCs. A.M. and J.L.R. provided RNA-seq data from hESCs and hiPSCs generated with retroviral vectors. J.C. performed AP staining, immunostaining, lactate production and glucose uptake assays, western blot analysis, RT-PCR and qPCR analyses. S.L., W.M., G.M.T., F.F. and P.J.P. performed bioinformatics analysis of RNA-seq data. H.L., I.Y.C. and G.L. performed neural differentiation experiments and marker analyses of differentiated cells. R.P. conducted the ScoreCard assay, which was bioinformatically analyzed by A.M.T., and K.C. performed bioinformatics analysis of reduced representation bisulfite sequencing data.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Peter J Park or Konrad Hochedlinger.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Figures

    Supplementary Figures 1–5

  2. 2.

    Supplementary Note

About this article

Publication history






Further reading