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Genome-wide characterization of the routes to pluripotency

A Corrigendum to this article was published on 17 June 2015

This article has been updated

Abstract

Somatic cell reprogramming to a pluripotent state continues to challenge many of our assumptions about cellular specification, and despite major efforts, we lack a complete molecular characterization of the reprograming process. To address this gap in knowledge, we generated extensive transcriptomic, epigenomic and proteomic data sets describing the reprogramming routes leading from mouse embryonic fibroblasts to induced pluripotency. Through integrative analysis, we reveal that cells transition through distinct gene expression and epigenetic signatures and bifurcate towards reprogramming transgene-dependent and -independent stable pluripotent states. Early transcriptional events, driven by high levels of reprogramming transcription factor expression, are associated with widespread loss of histone H3 lysine 27 (H3K27me3) trimethylation, representing a general opening of the chromatin state. Maintenance of high transgene levels leads to re-acquisition of H3K27me3 and a stable pluripotent state that is alternative to the embryonic stem cell (ESC)-like fate. Lowering transgene levels at an intermediate phase, however, guides the process to the acquisition of ESC-like chromatin and DNA methylation signature. Our data provide a comprehensive molecular description of the reprogramming routes and is accessible through the Project Grandiose portal at http://www.stemformatics.org.

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Figure 1: Multi-omics analysis of secondary reprogramming.
Figure 2: Molecular characterization of cell states during secondary reprogramming.
Figure 3: Dynamic features of chromatin remodelling in reprogramming cell states.
Figure 4: H3K4me3 dynamics define cell states.
Figure 5: Expression and regulation of lncRNAs during reprogramming.
Figure 6: Paths to F-class and ESC-like pluripotency.

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

  • 10 December 2014

    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

Authors and Affiliations

Authors

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 to Andras Nagy.

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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. bd, 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. bg, 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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