Genome-wide characterization of the routes to pluripotency (original) (raw)

Accession codes

Primary accessions

European Nucleotide Archive

Sequence Read Archive

Data deposits

Sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under accession number SRP046744 for all RNA-seq and ChIP-seq experiments, and in the European Bioinformatics Institute under the European Nucleotide Archive (ENA) accession number ERP004116 for MethylC-sequencing. The global and cell surface mass spectrometry proteomics raw data have been deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository under data set identifiers PXD000413 and PXD001456, respectively.

Change history

A minor addition was made to the Acknowledgements in the HTML and PDF versions.

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Acknowledgements

We thank M. Gertsenstein and M. Pereira for chimaera production, C. Monetti for cell culture, R. Cowling for DNA purification, and K. Harpal for chimaera embryo sectioning and staining. We acknowledge the intellectual contributions of P. P. L. Tam and R. P. Harvey. A.N. is Tier 1 Canada Research Chair in Stem Cells and Regeneration. This work was supported by grants awarded to A.N., I.M.R. and P.W.Z. from the Ontario Research Fund Global Leadership Round in Genomics and Life Sciences grants (GL2-01-028), to A.N. from the Canadian stem cell network (9/5254 (TR3)) and from the Canadian Institutes of Health Research (CIHR MOP102575). This work received support from the Korean Ministry of Knowledge Economy (grant 10037410 to J.-S.S.), from the SNUCM Research Fund (grant 0411-20100074 to J.-S.S.), and from Macrogen Inc. (grant MGR03-11 and MGR03-12). The Stemformatics resource is supported by an Australian Research Council special research grant to Stem Cells Australia (C.A.W. and S.M.G.). The analysis of the miRNA was supported by grants from the National Health and Medical Research Council of Australia (1024852 to J.L.C. and T.P.) and the Australian Research Council (DP1300101928 to T.P.). W.R. is a Cancer Institute of NSW Fellow and with J.E.J.R. receives support from the Cancer Council of NSW and National Health & Medical Research Council (571156 and 1061906). J.E.J.R. receives funding from Cure the Future & Tour de Cure. K.-A.L.C. is supported, in part, by the Wound Management Innovation CRC (established and supported under the Australian Government’s Cooperative Research Centres Program). S.M.G. received support from the Australian Research Council (SR110001002). C.A.W. is a QLD Smart Futures Fellow. M.B., J.M. and A.J.R.H. are supported by the Netherlands Proteomics Centre, and by the European Community’s Seventh Framework Programme (FP7/2007-2013) by the PRIME-XS project grant agreement number 262067. P.W.Z. is the Canada Research Chair in Stem Cell Bioengineering. S.M.I.H. received a fellowship from the McEwen Centre of Regenerative Medicine.

Author information

Author notes

  1. Javier Munoz & Kim-Anh Lê Cao
    Present address: †Present addresses: Proteomics Unit, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain (J.M.); The University of Queensland Diamantina Institute, Translational Research Institute, 37 Kent Street, Princess Alexandra Hospital, Brisbane, Queensland 4102, Australia (K.-A.L.C.).,
  2. Samer M. I. Hussein, Mira C. Puri and Peter D. Tonge: These authors contributed equally to this work.

Authors and Affiliations

  1. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada,
    Samer M. I. Hussein, Mira C. Puri, Peter D. Tonge, Andrew J. Corso, Mira Li, Ian M. Rogers & Andras Nagy
  2. Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5T 3H7, Canada,
    Mira C. Puri
  3. Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands,
    Marco Benevento, Javier Munoz & Albert J. R. Heck
  4. Netherlands Proteomics Centre, Padualaan 8, 3584CH Utrecht, The Netherlands,
    Marco Benevento, Javier Munoz & Albert J. R. Heck
  5. Institute of Medical Science, University of Toronto, Toronto, Ontario M5T 3H7, Canada,
    Andrew J. Corso & Andras Nagy
  6. Genome Biology Department, The John Curtin School of Medical Research, The Australian National University, Acton (Canberra), ACT 2601, Australia,
    Jennifer L. Clancy, Hardip R. Patel & Thomas Preiss
  7. Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, 4072, Queensland, Australia
    Rowland Mosbergen, Othmar Korn & Christine A. Wells
  8. Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul 110-799, South Korea,
    Dong-Sung Lee, Jong-Yeon Shin, Jong-Il Kim & Jeong-Sun Seo
  9. Department of Biomedical Sciences and Biochemistry, Seoul National University College of Medicine, Seoul 110-799, South Korea,
    Dong-Sung Lee, Jong-Il Kim & Jeong-Sun Seo
  10. Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St Lucia, 4072, Queensland, Australia
    Nicole Cloonan, David L. A. Wood, Maely E. Gauthier, Kim-Anh Lê Cao & Sean M. Grimmond
  11. Gene and Stem Cell Therapy Program and Bioinformatics Lab, Centenary Institute, Camperdown 2050, NSW, Australia & Sydney Medical School, 31 University of Sydney 2006, New South Wales, Australia
    Robert Middleton, William Ritchie & John E. J. Rasko
  12. Genome Discovery Unit, The John Curtin School of Medical Research, The Australian National University, Acton (Canberra) 2601, ACT, Australia,
    Hardip R. Patel
  13. Institute of Biomaterials and Biomedical Engineering (IBBME), University of Toronto, Toronto M5S-3G9, Canada,
    Carl A. White, Nika Shakiba & Peter W. Zandstra
  14. The Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto M5S 3E1, Canada,
    Carl A. White & Peter W. Zandstra
  15. Life Science Institute, Macrogen Inc., Seoul 153-781, South Korea,
    Jong-Yeon Shin & Jeong-Sun Seo
  16. Department of Systems & Computational Biology, Albert Einstein College of Medicine of Yeshiva University, Bronx, 10461, New York, USA
    Jessica C. Mar
  17. Cell and Molecular Therapies, Royal Prince Alfred Hospital, Camperdown 2050, New South Wales, Australia,
    John E. J. Rasko
  18. College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8TA, UK,
    Christine A. Wells
  19. Victor Chang Cardiac Research Institute, Darlinghurst (Sydney), New South Wales 2010, Australia,
    Thomas Preiss
  20. Department of Physiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada,
    Ian M. Rogers
  21. Department of Obstetrics and Gynaecology, University of Toronto, Toronto, Ontario M5S 1E2, Canada,
    Ian M. Rogers & Andras Nagy
  22. QIMR Berghofer Medical Research Institute, Genomic Biology Lab, 300 Herston Road, Herston, 4006, Queensland, Australia
    Nicole Cloonan

Authors

  1. Samer M. I. Hussein
  2. Mira C. Puri
  3. Peter D. Tonge
  4. Marco Benevento
  5. Andrew J. Corso
  6. Jennifer L. Clancy
  7. Rowland Mosbergen
  8. Mira Li
  9. Dong-Sung Lee
  10. Nicole Cloonan
  11. David L. A. Wood
  12. Javier Munoz
  13. Robert Middleton
  14. Othmar Korn
  15. Hardip R. Patel
  16. Carl A. White
  17. Jong-Yeon Shin
  18. Maely E. Gauthier
  19. Kim-Anh Lê Cao
  20. Jong-Il Kim
  21. Jessica C. Mar
  22. Nika Shakiba
  23. William Ritchie
  24. John E. J. Rasko
  25. Sean M. Grimmond
  26. Peter W. Zandstra
  27. Christine A. Wells
  28. Thomas Preiss
  29. Jeong-Sun Seo
  30. Albert J. R. Heck
  31. Ian M. Rogers
  32. Andras Nagy

Contributions

S.M.I.H., M.C.P., P.D.T. and A.N. conceived, designed and carried out most of the experiments, interpreted results and wrote the manuscript. P.W.Z. contributed to study design. T.P., C. A. Wells, I.M.R., P.W.Z., C. A. White, N.S., A.J.C. and J.C.M. assisted with data interpretation and manuscript writing. M.L., S.M.I.H. and M.C.P. performed ChIP. M.C.P., S.M.I.H., N.C., O.K., D.L.A.W., M.E.G. and S.M.G. produced and analysed RNA-seq data. S.M.I.H., D.-S.L., M.C.P., J.-Y.S., J.-I.K. and J.-S.S. produced and analysed MethylC-seq and ChIP-seq data. J.E.J.R, W.R. and R.Mi. performed the IR analysis, interpretation and contributed to the manuscript writing. C. A. Wells, R.Mo., O.K., K.-A.LC. and J.C.M. provided support for bioinformatics analyses and data visualization. M.B., J.M. and A.J.R.H. performed the LC-MS analysis and proteomic data analysis. H.R.P. mapped the miRNA Next Generation Sequencing (NGS) data and provided support for bioinformatics analyses and data visualization. J.L.C. and T.P. analysed and interpreted the miRNA NGS data. C.A.W. performed the CSC proteomics. C.A.W., N.S. and P.W.Z. analysed CSC proteome data.

Corresponding author

Correspondence toAndras Nagy.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Effects of lowering doxycycline on reprogramming cells.

a, Frequency of doxycycline-independent pluripotent cells obtained when 1B secondary MEFs were reprogrammed in 1,500 ng ml−1 doxycycline until the indicated day. b, Morphology of cells at day 15 after lowering the doxycycline concentration from 1,500 ng ml−1 to levels as indicated on day 8 of reprogramming. c, Clonal efficiency measurement at day 15 of reprogramming after lowering the doxycycline concentration on day 8 to the level indicated. d, e, 1B secondary iPSCs show widespread contribution to all germ layers of chimaeric embryos. Whole-mount view (d) and transverse section of E10.5 diploid chimaera (e). Embryo is representative of n = 6 chimaeric embryos with strong (>75%) iPSC donor cell contribution. h, heart; hg, hindgut; nt, neural tube. Scale bars, 750 μm (d) and 400 µm (e). f, RNA-seq analysis of transgene and endogenous expression levels during reprogramming. CPM, counts per million.

Extended Data Figure 2 Locus-specific sequencing data.

Read coverage histograms representing gene expression and epigenetic status at the genomic loci of selected ESC-associated genes.

Extended Data Figure 3 Hierarchical clustering and principal component analysis (PCA) for multi-omics analyses.

a, Pearson correlation complete linkage hierarchical clustering of long RNA-seq data set. Colour coding indicates the grouping of samples based on clustering. b–d, PCA performed on each platform (10 neighbours for _k_-value nearest neighbour (KNN) imputation). Short RNA-seq platform PCA was performed on miRNAs (b). Long RNA-seq platform PCA was performed on protein-coding transcripts (b). Cell surface proteome PCA represents proteins detected by cell surface focused mass spectrometry analysis (b). c, PCA of global CpG methylation analysis. Red arrow follows the high-doxycycline sample trajectory; black dashed arrow follows D8H through low-doxycycline trajectory. Low-doxycycline samples D21L and D21 are highlighted in blue to indicate that compared to other platforms they do not project with ESC/iPSC (see text for further details). d, H3K4me3, H3K36me3 and H3K27me3 PCAs represent genome-wide enriched regions at annotated genes.

Extended Data Figure 4 Integration of gene expression data from 1B reprogramming and other transcriptome data sets.

a, Distribution of the entropy score of protein-coding gene expression for individual samples (blue) and sample groups (red) indicated as probability density curve. b, Pearson correlation analysis of 1B secondary reprogramming sample protein-coding gene expression with transcriptomes of early embryonic stages and epiblast stem cells (EpiSCs) derived from a range of developmental stages20. c, Pearson correlation analysis of 1B secondary reprogramming sample protein-coding gene expression with transcriptome of sorted secondary reprogramming intermediates8. d, Expression of CD44 and Icam1 markers during 1B reprogramming. Error bars represent standard error of the mean. e, Pearson correlation analysis of 1B reprogramming sample protein-coding gene expression with sorted reprogramming and pluripotent cells from the Col1a1 primary reprogramming system6.

Extended Data Figure 5 Effect of Oct4, Sox2, Klf4 and Myc expression level on reprogramming outcomes.

a, Pearson correlation analysis of RNA-seq data from 1B reprogramming samples and reprogramming clones from ref. 7 that are competent or incompetent to become factor-independent secondary iPSC (SC and SI clones, respectively). b, Transgene and endogenous gene expression determined by RNA-seq for Myc, Pou5f1 (Oct4), Sox2 and Klf4 in SC and SI clones7. Bar graphs represent average expression of doxycycline-treated samples or SC iPSCs. Error bars represent standard error of the mean. Student’s _t_-test was used for statistics. c, PCA of protein-coding stage-specific genes from Fig. 2c, comparing 1B reprogramming samples and secondary reprogramming clones from ref. 7. F-class cells cluster separately from SI and SC clones. Moreover, 1B reprogramming follows a different trajectory than SI and SC clones towards iPSCs. Colour coding indicates the grouping of samples. d, Pearson correlation complete linkage hierarchical clustering of 1B reprogramming samples and SI and SC secondary reprogramming clones. Clustering was performed on protein-coding stage-specific genes and based on FPKM values normalized to the averaged ESC/iPSCs values from the respective study. Heat maps show stage-specific protein-coding gene expression belonging to iPSC/ESC (top heat map) and F-class (bottom heat map) genes. Clusters and genes on the right of each heat map highlight genes that show a different expression pattern between F-class and doxycycline-treated SI clones. For gene lists associated with d, refer to Supplementary Table 1.

Extended Data Figure 6 Global analysis of histone mark and intron retention changes during reprogramming.

a, Intensity plots of genes associated with H3K4me3 (green) and H3K27me3 (red) ±10 kb of annotated TSSs. b, Heat map representation of PRC2 components and histone demethylase expression at the RNA (RNA-seq) and protein level. c, Correlation of gene transcription with protein and intron retention for genes that exhibit intron retention from Fig. 2c. d, Correlation of intron retention, RNA expression and protein level for Kdm6a. e, Violin plots comparing observed and random Pearson correlations of intron retention versus gene FPKM at reprogramming stages. Bars represent average Pearson correlation coefficients. Error bars represent standard error of the mean. Student’s _t_-test was used for statistics. f, Number of expressed transposable elements during reprogramming.

Extended Data Figure 7 Tracking secondary MEF histone mark changes during reprogramming from one sample to another.

a, Pie-chart diagram tracking the histone mark changes using secondary MEF and secondary iPSCs as reference points. Each histone mark is colour coded: H3K4me3, green; H3K4me3H3K27me3, orange; H3K27me3, red; no mark, grey. Loci were tracked from their start (2°MEF) and end (2°iPSCs) histone signatures. b–g, Tracking bar graphs of histone mark changes. The histone mark change is shown at the top of each set of 12 histograms. Bars represent number of genes whose mark changed for the time point indicated at the top of the individual histogram, and which of these genes carry the same mark at the other time points (x axis). For example, in b ‘2°MEF (H3K4me3/H3K27me3→H3K4me3)’ the histogram shows the number of genes that were bivalent in secondary MEFs but changed to H3K4me3 monovalent at another time point. In the case of the small histogram labelled D2H, the black-framed green bar represents the number of loci that showed this change from secondary MEFs at D2H and the bars for all the other samples indicate how many of these D2H loci were also H3K4me3+ in the other samples.

Extended Data Figure 8 Determining expression threshold for defining bivalent loci and bivalency in other reprogramming systems.

a, RNA-seq expression value (log2 of FPKM) distribution (as represented by density curves) of four categories of genes: (1) genes marked by H3K4me3 and H3K36me3 (blue line); (2) genes marked by H3K4me3 alone (green line); (3) genes marked by H3K27me3 alone (red line); and (4) genes marked by H3K4me3 and H3K27me3, but not H3K36me3 (orange line). Genes were combined from all the samples to identify each category. Expression threshold was defined as the 10th percentile expression boundary of genes marked by H3K4me3 and H3K36me3. Genes that were expressed at lower levels than this threshold were considered not expressed in subsequent analyses. b, Assessment of cellular heterogeneity in 1B reprogramming by chromatin mark and expression association of two cell surface markers: CD24 and CD73. Upper scatter plots show H3K27me3 versus H3K36me3 enrichment in individual samples. Lower plot shows percentage of cells expressing each marker for same samples as determined by FACs analysis. Active locus: H3K4me3+H3K36me3+H3K27me3−. Heterogeneous locus: H3K4me3+H3K36me3+H3K27me3+. c, Absolute number (primary y axis) and proportion (secondary y axis) of false (heterogeneous) bivalent loci during secondary reprogramming. the presence of H3K36me3 distinguishes false bivalent loci (H3K4me3+H3K27me3+H3K36me3+) that represent heterogeneity from true bivalent loci that are transcriptionally repressed (H3K36me3−). d, Tracking of histone mark status of secondary MEF heterogeneous loci. Heterogeneous loci resolve into silent and active loci during reprogramming. e, Total number of detected bivalent loci as defined by lack of H3K36me3 mark and expression levels below the threshold as shown in panel a. Dark and light green bar graphs highlight proportion shared among all samples and with secondary MEFs, respectively. f, Sequential addition of novel bivalent marks with respect to stages of reprogramming, as indicated by colours. g, h, Corresponding bivalent loci identified in 1B samples and two independent data sets6,31. i, Tracking of bivalent loci for Polo et al. reprogramming system6. For gene lists related to e, refer to Supplementary Table 2.

Extended Data Figure 9 Long non-coding RNA expression analysis.

a, Determination of expression threshold for lncRNA genes using H3K4me3 and H3K36me3 chromatin mark. b, Distribution of the entropy of non-coding gene expression for individual samples (blue) and sample groups (red) indicated as probability density curve. c, Percentage of unannotated transcripts with listed genomic features. d, Analysis of unannotated lncRNA transcripts for coding potential using coding potential calculator (CPC). (See Supplementary Information for details.) e, RNA and protein expression profiles of three novel coding transcripts.

Extended Data Figure 10 Comparison of lncRNA expression in 1B secondary reprogramming and other reprogramming systems.

a, Pearson correlation analysis of differentially expressed un-annotated RNA transcripts for 1B reprogramming samples and secondary reprogramming clones that are competent or incompetent to become factor-independent secondary iPSCs (SC and SI clones, respectively)7. b, Pearson correlation analysis of differentially expressed unannotated RNA transcripts for 1B reprogramming samples and sorted reprogramming intermediates from ref. 8. c, Heat map of differentially expressed novel RNAs from 1B reprogramming samples with secondary reprogramming clones that are competent or incompetent to become factor-independent secondary iPSCs (SC and SI clones, respectively)7. For gene lists related to c, refer to Supplementary Table 4. d, Read coverage histograms representing gene expression and epigenetic status of unannotated lncRNAs observed in F-class (D16H) and ESC-like state (secondary iPSCs). e, GO analysis results for genes downregulated in F-class state (FDR <1%), but unchanged in ESC-like state, from D8H (combined groups 3, 6 and 9). f, GO analysis results for genes upregulated in ESC-like state (FDR <1%), but unchanged in F-class state, from D8H (combined groups 1b, 4b and 7b). For gene lists, full GO term analyses and P values associated with e, f refer to Supplementary Table 5.

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Hussein, S., Puri, M., Tonge, P. et al. Genome-wide characterization of the routes to pluripotency.Nature 516, 198–206 (2014). https://doi.org/10.1038/nature14046

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