Abstract
The epigenetic mechanisms that maintain differentiated cell states remain incompletely understood. Here we employed histone mutants to uncover a crucial role for H3K36 methylation in the maintenance of cell identities across diverse developmental contexts. Focusing on the experimental induction of pluripotency, we show that H3K36M-mediated depletion of H3K36 methylation endows fibroblasts with a plastic state poised to acquire pluripotency in nearly all cells. At a cellular level, H3K36M facilitates epithelial plasticity by rendering fibroblasts insensitive to TGFβ signals. At a molecular level, H3K36M enables the decommissioning of mesenchymal enhancers and the parallel activation of epithelial/stem cell enhancers. This enhancer rewiring is Tet dependent and redirects Sox2 from promiscuous somatic to pluripotency targets. Our findings reveal a previously unappreciated dual role for H3K36 methylation in the maintenance of cell identity by integrating a crucial developmental pathway into sustained expression of cell-type-specific programmes, and by opposing the expression of alternative lineage programmes through enhancer methylation.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
RNA-seq, ATAC-seq and CUT&Tag data have been deposited in the Gene Expression Omnibus under accession code GSE203492. Single-cell RNA-seq data are deposited under GSE203536. RRBS and WGBS data are available under GSE203606. The publicly available datasets used in this study are GSE90893, GSE111172 and GSE77420. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.
Code availability
The code used to analyse the single-cell RNA-seq data presented here, and to generate the corresponding plots, is uploaded to GitHub https://github.com/Michorlab/H3K36_methylation_scRNAseq. Code for additional analyses available upon request.
References
Tsankov, A. M. et al. Transcription factor binding dynamics during human ESC differentiation. Nature 518, 344–349 (2015).
Ho, L. & Crabtree, G. R. Chromatin remodelling during development. Nature 463, 474–484 (2010).
Mittnenzweig, M. et al. A single-embryo, single-cell time-resolved model for mouse gastrulation. Cell 184, 2825–2842.e22 (2021).
Grosswendt, S. et al. Epigenetic regulator function through mouse gastrulation. Nature 584, 102–108 (2020).
Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018).
Aguilera-Castrejon, A. et al. Ex utero mouse embryogenesis from pre-gastrulation to late organogenesis. Nature 593, 119–124 (2021).
Takaoka, K. & Hamada, H. Cell fate decisions and axis determination in the early mouse embryo. Development 139, 3–14 (2011).
Jambhekar, A., Dhall, A. & Shi, Y. Roles and regulation of histone methylation in animal development. Nat. Rev. Mol. Cell Biol. 20, 625–641 (2019).
Suvà, M. L., Riggi, N. & Bernstein, B. E. Epigenetic reprogramming in cancer. Science 339, 1567–1570 (2013).
Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).
Brumbaugh, J., Stefano, B. D. & Hochedlinger, K. Reprogramming: identifying the mechanisms that safeguard cell identity. Development 146, dev182170 (2019).
Apostolou, E. & Hochedlinger, K. Chromatin dynamics during cellular reprogramming. Nature 502, 462–471 (2013).
Onder, T. T. et al. Chromatin-modifying enzymes as modulators of reprogramming. Nature 483, 598–602 (2012).
Borkent, M. et al. A serial shRNA screen for roadblocks to reprogramming identifies the protein modifier SUMO2. Stem Cell Rep. 6, 704–716 (2016).
Yang, C.-S., Chang, K.-Y. & Rana, T. M. Genome-wide functional analysis reveals factors needed at the transition steps of induced reprogramming. Cell Rep. 8, 327–337 (2014).
Qin, H. et al. Systematic identification of barriers to human iPSC generation. Cell 158, 449–461 (2014).
Cheloufi, S. et al. The histone chaperone CAF-1 safeguards somatic cell identity. Nature 528, 218–224 (2015).
Brumbaugh, J. et al. Nudt21 controls cell fate by connecting alternative polyadenylation to chromatin signaling. Cell 172, 106–120.e21 (2018).
Hyun, K., Jeon, J., Park, K. & Kim, J. Writing, erasing and reading histone lysine methylations. Exp. Mol. Med. 49, e324 (2017).
Kim, E. et al. Phosphorylation of EZH2 activates STAT3 signaling via STAT3 methylation and promotes tumorigenicity of glioblastoma stem-like cells. Cancer Cell 23, 839–852 (2013).
Xu, K. et al. EZH2 oncogenic activity in castration-resistant prostate cancer cells is polycomb-independent. Science 338, 1465–1469 (2012).
Shpargel, K. B., Sengoku, T., Yokoyama, S. & Magnuson, T. UTX and UTY demonstrate histone demethylase-independent function in mouse embryonic development. PLoS Genet. 8, e1002964 (2012).
Miller, S. A., Mohn, S. E. & Weinmann, A. S. Jmjd3 and UTX play a demethylase-independent role in chromatin remodeling to regulate T-box family member-dependent gene expression. Mol. Cell 40, 594–605 (2010).
Herz, H.-M. et al. Histone H3 lysine-to-methionine mutants as a paradigm to study chromatin signaling. Science 345, 1065–1070 (2014).
Nacev, B. A. et al. The expanding landscape of ‘oncohistone’ mutations in human cancers. Nature 567, 473–478 (2019).
Lewis, P. W. et al. Inhibition of PRC2 activity by a gain-of-function H3 mutation found in pediatric glioblastoma. Science 340, 857–861 (2013).
Chan, K.-M. et al. The histone H3.3K27M mutation in pediatric glioma reprograms H3K27 methylation and gene expression. Gene Dev. 27, 985–990 (2013).
Fang, D. et al. The histone H3.3K36M mutation reprograms the epigenome of chondroblastomas. Science 352, 1344–1348 (2016).
Lu, C. et al. Histone H3K36 mutations promote sarcomagenesis through altered histone methylation landscape. Science 352, 844–849 (2016).
Schwartzentruber, J. et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482, 226–231 (2012).
Mohammad, F. et al. EZH2 is a potential therapeutic target for H3K27M-mutant pediatric gliomas. Nat. Med. 23, 483–492 (2017).
Brumbaugh, J. et al. Inducible histone K-to-M mutations are dynamic tools to probe the physiological role of site-specific histone methylation in vitro and in vivo. Nat. Cell Biol. 21, 1449–1461 (2019).
Stadtfeld, M., Maherali, N., Borkent, M. & Hochedlinger, K. A reprogrammable mouse strain from gene-targeted embryonic stem cells. Nat. Methods 7, 53–55 (2010).
Chen, J. et al. H3K9 methylation is a barrier during somatic cell reprogramming into iPSCs. Nat. Genet. 45, 34–42 (2013).
Soufi, A., Donahue, G. & Zaret, K. S. Facilitators and impediments of the pluripotency reprogramming factors’ initial engagement with the genome. Cell 151, 994–1004 (2012).
Sridharan, R. et al. Proteomic and genomic approaches reveal critical functions of H3K9 methylation and Heterochromatin Protein-1γ in reprogramming to pluripotency. Nat. Cell Biol. 15, 872–882 (2013).
Fragola, G. et al. Cell reprogramming requires silencing of a core subset of polycomb targets. PLoS Genet. 9, e1003292 (2013).
Ding, X. et al. The polycomb protein Ezh2 impacts on induced pluripotent stem cell generation. Stem Cells Dev. 23, 931–940 (2014).
Wang, C. et al. Enhancer priming by H3K4 methyltransferase MLL4 controls cell fate transition. Proc. Natl Acad. Sci. USA 113, 11871–11876 (2016).
Liang, G., He, J. & Zhang, Y. Kdm2b promotes induced pluripotent stem cell generation by facilitating gene activation early in reprogramming. Nat. Cell Biol. 14, 457–466 (2012).
Wang, T. et al. The histone demethylases Jhdm1a/1b enhance somatic cell reprogramming in a vitamin-C-dependent manner. Cell Stem Cell 9, 575–587 (2011).
Stadtfeld, M., Maherali, N., Breault, D. T. & Hochedlinger, K. Defining molecular cornerstones during fibroblast to iPS cell reprogramming in mouse. Cell Stem Cell 2, 230–240 (2008).
Polo, J. M. et al. A molecular roadmap of reprogramming somatic cells into iPS cells. Cell 151, 1617–1632 (2012).
Brambrink, T. et al. Sequential expression of pluripotency markers during direct reprogramming of mouse somatic cells. Cell Stem Cell 2, 151–159 (2008).
Schwarz, B. A. et al. Prospective isolation of poised iPSC intermediates reveals principles of cellular reprogramming. Cell Stem Cell 23, 289–305.e5 (2018).
Lengner, C. J. et al. Oct4 expression is not required for mouse somatic stem cell self-renewal. Cell Stem Cell 1, 403–415 (2007).
Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943.e22 (2019).
Li, R. et al. A mesenchymal-to-epithelial transition initiates and is required for the nuclear reprogramming of mouse fibroblasts. Cell Stem Cell 7, 51–63 (2010).
Samavarchi-Tehrani, P. et al. Functional genomics reveals a BMP-driven mesenchymal-to-epithelial transition in the initiation of somatic cell reprogramming. Cell Stem Cell 7, 64–77 (2010).
Massagué, J. TGFβ signalling in context. Nat. Rev. Mol. Cell Biol. 13, 616–630 (2012).
Maherali, N. & Hochedlinger, K. Tgfβ signal inhibition cooperates in the induction of iPSCs and replaces Sox2 and cMyc. Curr. Biol. 19, 1718–1723 (2009).
Gregory, P. A. et al. The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nat. Cell Biol. 10, 593–601 (2008).
Park, S.-M., Gaur, A. B., Lengyel, E. & Peter, M. E. The miR-200 family determines the epithelial phenotype of cancer cells by targeting the E-cadherin repressors ZEB1 and ZEB2. Gene Dev. 22, 894–907 (2008).
Subramanyam, D. et al. Multiple targets of miR-302 and miR-372 promote reprogramming of human fibroblasts to induced pluripotent stem cells. Nat. Biotechnol. 29, 443–448 (2011).
Judson, R. L., Babiarz, J. E., Venere, M. & Blelloch, R. Embryonic stem cell–specific microRNAs promote induced pluripotency. Nat. Biotechnol. 27, 459–461 (2009).
Vierbuchen, T. et al. Direct conversion of fibroblasts to functional neurons by defined factors. Nature 463, 1035–1041 (2010).
Davis, R. L., Weintraub, H. & Lassar, A. B. Expression of a single transfected cDNA converts fibroblasts to myoblasts. Cell 51, 987–1000 (1987).
Yagi, M. et al. Dissecting dual roles of MyoD during lineage conversion to mature myocytes and myogenic stem cells. Gene Dev. 35, 1209–1228 (2021).
Bar-Nur, O. et al. Direct reprogramming of mouse fibroblasts into functional skeletal muscle progenitors. Stem Cell Rep. 10, 1505–1521 (2018).
Beccari, L. et al. Multi-axial self-organization properties of mouse embryonic stem cells into gastruloids. Nature 562, 272–276 (2018).
van den Brink, S. C. et al. Symmetry breaking, germ layer specification and axial organisation in aggregates of mouse embryonic stem cells. Development 141, 4231–4242 (2014).
Baillie-Johnson, P., Brink, S. C. van den, Balayo, T., Turner, D. A. & Arias, A. M. Generation of aggregates of mouse embryonic stem cells that show symmetry breaking, polarization and emergent collective behaviour in vitro. J. Vis. Exp. JoVE https://doi.org/10.3791/53252 (2015).
Diaz-Cuadros, M. et al. In vitro characterization of the human segmentation clock. Nature 580, 113–118 (2020).
Kahata, K., Dadras, M. S. & Moustakas, A. TGF-β family signaling in epithelial differentiation and epithelial–mesenchymal transition. Cold Spring Harbor Perspect. Biol. 10, a022194 (2018).
Mou, H. et al. Dual SMAD signaling inhibition enables long-term expansion of diverse epithelial basal cells. Cell Stem Cell 19, 217–231 (2016).
Hillringhaus, L. et al. Structural and evolutionary basis for the dual substrate selectivity of human KDM4 histone demethylase family. J. Biol. Chem. 286, 41616–41625 (2011).
Krogan, N. J. et al. Methylation of histone H3 by Set2 in Saccharomyces cerevisiae is linked to transcriptional elongation by RNA polymerase II. Mol. Cell. Biol. 23, 4207–4218 (2003).
Streubel, G. et al. The H3K36me2 methyltransferase Nsd1 demarcates PRC2-mediated H3K27me2 and H3K27me3domains in embryonic stem cells. Mol. Cell 70, 371–379.e5 (2018).
Wang, G. G., Cai, L., Pasillas, M. P. & Kamps, M. P. NUP98–NSD1 links H3K36 methylation to Hox-A gene activation and leukaemogenesis. Nat. Cell Biol. 9, 804–812 (2007).
Yuan, W. et al. H3K36 methylation antagonizes PRC2-mediated H3K27 methylation. J. Biol. Chem. 286, 7983–7989 (2011).
Rajagopalan, K. N. et al. Depletion of H3K36me2 recapitulates epigenomic and phenotypic changes induced by the H3.3K36M oncohistone mutation. Proc. Natl Acad. Sci. USA 118, e2021795118 (2021).
Fang, Y. et al. The H3K36me2 methyltransferase NSD1 modulates H3K27ac at active enhancers to safeguard gene expression. Nucleic Acids Res. 49, 6281–6295 (2021).
Chaouch, A. et al. Histone H3.3 K27M and K36M mutations de-repress transposable elements through perturbation of antagonistic chromatin marks. Mol. Cell 81, 4876–4890.e7 (2021).
Barral, A. et al. SETDB1/NSD-dependent H3K9me3/H3K36me3 dual heterochromatin maintains gene expression profiles by bookmarking poised enhancers. Mol. Cell 82, 816–832.e12 (2022).
Chronis, C. et al. Cooperative binding of transcription factors orchestrates reprogramming. Cell 168, 442–459.e20 (2017).
Yin, Y. et al. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356, eaaj2239 (2017).
Kaluscha, S. et al. Evidence that direct inhibition of transcription factor binding is the prevailing mode of gene and repeat repression by DNA methylation. Nat. Genet. 54, 1895–1906 (2022).
Weinberg, D. N. et al. The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape. Nature 573, 281–286 (2019).
Baubec, T. et al. Genomic profiling of DNA methyltransferases reveals a role for DNMT3B in genic methylation. Nature 520, 243–247 (2015).
Yagi, M. et al. Identification of distinct loci for de novo DNA methylation by DNMT3A and DNMT3B during mammalian development. Nat. Commun. 11, 3199 (2020).
von Meyenn, F. et al. Impairment of DNA methylation maintenance is the main cause of global demethylation in naive embryonic stem cells. Mol. Cell 62, 848–861 (2016).
Charlton, J. et al. TETs compete with DNMT3 activity in pluripotent cells at thousands of methylated somatic enhancers. Nat. Genet. 52, 819–827 (2020).
Hu, X. et al. Tet and TDG mediate DNA demethylation essential for mesenchymal-to-epithelial transition in somatic cell reprogramming. Cell Stem Cell 14, 512–522 (2014).
Gao, Y. et al. Replacement of Oct4 by Tet1 during iPSC induction reveals an important role of DNA methylation and hydroxymethylation in reprogramming. Cell Stem Cell 12, 453–469 (2013).
Zviran, A. et al. Deterministic somatic cell reprogramming involves continuous transcriptional changes governed by Myc and epigenetic-driven modules. Cell Stem Cell 24, 328–341.e9 (2019).
Amouroux, R. et al. De novo DNA methylation drives 5hmC accumulation in mouse zygotes. Nat. Cell Biol. 18, 225–233 (2016).
Sardina, J. L. et al. Transcription factors drive Tet2-mediated enhancer demethylation to reprogram cell fate. Cell Stem Cell 23, 727–741.e9 (2018).
Yuan, S. et al. Global regulation of the histone mark H3K36me2 underlies epithelial plasticity and metastatic progression. Cancer Discov. 10, 854–871 (2020).
Brabletz, S. & Brabletz, T. The ZEB/miR‐200 feedback loop—a motor of cellular plasticity in development and cancer? EMBO Rep. 11, 670–677 (2010).
Papillon-Cavanagh, S. et al. Impaired H3K36 methylation defines a subset of head and neck squamous cell carcinomas. Nat. Genet. 49, 180–185 (2017).
Velychko, S. et al. Excluding Oct4 from Yamanaka cocktail unleashes the developmental potential of iPSCs. Cell Stem Cell 25, 737–753.e4 (2019).
Sommer, C. A. et al. Induced pluripotent stem cell generation using a single lentiviral stem cell cassette. Stem Cells 27, 543–549 (2009).
Bar-Nur, O. et al. Small molecules facilitate rapid and synchronous iPSC generation. Nat. Methods 11, 1170–1176 (2014).
Nagy, A., Rossant, J., Nagy, R., Abramow-Newerly, W. & Roder, J. C. Derivation of completely cell culture-derived mice from early-passage embryonic stem cells. Proc. Natl Acad. Sci USA. 90, 8424–8428 (1993).
Eggan, K. et al. Hybrid vigor, fetal overgrowth, and viability of mice derived by nuclear cloning and tetraploid embryo complementation. Proc. Natl Acad. Sci. USA 98, 6209–6214 (2001).
Zheng, Y., Thomas, P. M. & Kelleher, N. L. Measurement of acetylation turnover at distinct lysines in human histones identifies long-lived acetylation sites. Nat. Commun. 4, 2203 (2013).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).
Kaya-Okur, H. S., Janssens, D. H., Henikoff, J. G., Ahmad, K. & Henikoff, S. Efficient low-cost chromatin profiling with CUT&Tag. Nat. Protoc. 15, 3264–3283 (2020).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Anders, S. et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat. Protoc. 8, 1765–1786 (2013).
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
Hutchins, A. P. et al. Models of global gene expression define major domains of cell type and tissue identity. Nucleic Acids Res. 45, 2354–2367 (2017).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
John, S. et al. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat. Genet. 43, 264–268 (2011).
Ross-Innes, C. S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J. Integr. Biol. 16, 284–287 (2012).
Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).
Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinf. 10, 232 (2009).
Sun, D. et al. MOABS: model based analysis of bisulfite sequencing data. Genome Biol. 15, R38 (2014).
Feng, H., Conneely, K. N. & Wu, H. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res. 42, e69 (2014).
Acknowledgements
We thank members of the Hochedlinger laboratory for their suggestions, and members of the MGH CRM/HSCI Flow Core, the Harvard Genome Modification Facility and the MGH Next Generation Sequencing Core for their support. We also thank A. Collier for suggestions and discussions, and H. Kretzmer for her help with DNA methylation data. Support from the Dana-Farber Cancer Institute’s Center for Cancer Evolution and Physical Sciences-Oncology Center (U54CA193461; to F.M.) is gratefully acknowledged. M.S.H. was supported by the German Cancer Aid (Deutsche Krebshilfe). K.P. is supported by the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research at UCLA, the David Geffen School of Medicine, the National Institutes of Health (GM099134) and a Faculty Scholar grant from the Howard Hughes Medical Institute. K.H. was supported by funds from the MGH, the National Institutes of Health (R01 HD058013 and P01 GM099134), the Milky Way Research Foundation and the Gerald and Darlene Jordan Chair in Regenerative Medicine.
Author information
Authors and Affiliations
Contributions
M.S.H. and K.H. conceived the study and wrote the manuscript. M.S.H., M.Y., B.D.S., A.J.H. and W.D. performed experiments and analysed the data. S.C. and F.M. analysed single-cell RNA-seq data. A.M. and J.C. performed RRBS. A.M. and C.H. performed WGBS. A.M., J.C., C.H., M.S.H., J.L. and K.P. performed DNA methylation analysis. R.I.S., L.P.W., J.L., K.P. and M.S.H. performed additional bioinformatics analyses.
Corresponding author
Ethics declarations
Competing interests
F.M. is a co-founder of and has equity in Harbinger Health, has equity in Zephyr AI, and serves as a consultant for Harbinger Health, Zephyr AI and Red Cell Partners. F.M. declares that none of these relationships are directly or indirectly related to the content of this manuscript. The remaining authors declare no competing interests.
Peer review
Peer review information
Nature Cell Biology thanks Samantha Morris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Key role of H3K36-methylation in cell identity maintenance.
(a) Colony counts for AP staining of reprogrammable MEFs transduced as indicated (Fig. 1b,c). K36M wells were confluent and could not be counted. P values were determined by two-sided unpaired Student’s t test, error bars indicate mean ± SD (n = 3 biologically independent experiments). (b) Mice with dox-inducible alleles of WT H3.3 or K36M in the Col1a1 locus were crossed with mice harboring a dox-inducible OKSM cassette in the same locus, and an EGFP reporter in the 3’UTR of Pou5f1. (c) Immunofluorescence of MEFs derived as in a). Scale bar 50 µm. (d) Mass spectrometry of histone modifications in day 4 reprogramming intermediates (n = 2 independent biological experiments). (e) MEFs without endogenous OKSM but with inducible H3.3 WT or K36M were doxycycline-treated for 2 days, then replated and transduced with constitutive OKSM virus. Doxycycline was added to the medium for the indicated intervals, intracellular flow cytometry for Nanog was performed on day 8. (f) Quantification of Nanog positivity by flow cytometry on day 8 in K36M cells treated with doxycycline for the indicated time. Cells were either not pre-treated or pre-treated with doxycycline 2 days prior to initiation of reprogramming (see k). Error bars indicate mean ± SD (n = 3 independent biological experiments). (g) Fraction of Oct4-GFP+ cells during reprogramming in FBS/LIF medium supplemented with ascorbic acid (left) and without supplementation (right), error bars indicate mean ± SD (n = 3 independent biological experiments). (h) Membrane dye dilution assay for reprogramming cultures. (i) Percentage of viable cells as assessed by Annexin V/PI negativity on day 2 and 4 of reprogramming. Error bars indicate mean ± SD (n = 3 independent biological experiments). (j) Fraction of Oct4-GFP+ cells after sorting of positive cells and expansion on gelatin (top), and in picked iPSCs passaged on feeders (bottom). (k) Day 10 K36M reprogramming cultures were sorted by Oct4-GFP reporter positivity. Positive cells were maintained in FBS/LIF, negative cells underwent continued reprogramming in AGi medium. (l) Quantification of Oct4-GFP+ cells by flow cytometry in K36M cells sorted by Oct4-GFP reporter status (see i), error bars indicate mean ± SD (n = 3 independent biological experiments).
Extended Data Fig. 2 K36M enhances the reprogramming of different cell types and generates iPSCs highly similar to control iPSCs.
(a) Reprogramming of GMPs to iPSCs. Alkaline phosphatase staining of iPSC colonies at the indicated timepoints. Quantification of colony counts. P values were determined by two-sided unpaired Student’s t test, n = 3 biologically independent experiments. (b) Reprogramming of keratinocytes to iPSCs. Alkaline phosphatase staining on day 13 of cells cultured for the indicated timeframes. Area percentage of well that is AP positive. P values were determined by two-sided unpaired Student’s t test, error bars indicate mean ± SD (n = 3 independent biological experiments). (c) Immunofluorescence for Nanog, H3K36me3, H3K36me2, and H3K27me3 of passaged iPSC cultures from WT and K36M backgrounds cultured without doxycycline on irradiated feeders. (d) Relative expression (RNA-seq) of key pluripotency genes in passaged iPSCs of both backgrounds, n = 2 biologically independent experiments. (e) DNA methylation at MEF (n = 63,696) and ESC enhancers (n = 72,638) for MEFs and passaged iPSCs of both backgrounds (RRBS, n = 2 biologically independent uninduced MEF replicates were integrated, one replicate each for uninduced iPSCs of each genotype). (f) Representative gene tracks showing RNA-seq, ATAC-seq, and RRBS data for Cdh1 and Pou5f1 in passaged iPSCs of both backgrounds. (g) Correlation matrices for key histone modifications and chromatin accessibility in passaged WT and K36M iPSCs (CUT&Tag and ATAC-seq, n = 2 biologically independent replicates were integrated for this analysis). (h) Correlation of K36M vs. WT derived iPSCs for H3K36me3 over gene bodies (CUT&Tag, n = 2 biologically independent replicates were integrated for this analysis). (i) Correlation of K36M vs. WT derived iPSCs for H3K36me2 in genome-wide 50 kb bins (CUT&Tag, n = 2 biologically independent replicates were integrated for this analysis). (j) Representative gene tracks showing H3K36me3, H3K36me2, H3K27me3, and H3K4me3 at pluripotency gene Nanog (n = 2 biologically independent replicates). (k) Quantification of the diameter of embryoid bodies from passaged iPSCs of both backgrounds (without doxycycline). P value determined by two-sided unpaired Student’s t test, n = 49 for WT, n = 44 for K36M. (l) qPCR for Nanog, Nestin, Sox7, and Gata6 in embryoid bodies derived from passaged iPSCs of both backgrounds (without doxycycline), error bars indicate mean ± SD (n = 3 independent biological experiments). (m) H&E sections of teratomas generated with iPSCs of both backgrounds (without doxycycline). 4/4 WT and 6/6 K36M iPSC lines produced well-differentiated teratomas. Images depict tissue-like structures of all germ layers. (n) Coat-color chimeras generated by blastocyst injection of K36M iPSCs.
Extended Data Fig. 3 Single-cell RNA-seq reveals main trajectories of WT and K36M reprogramming intermediates.
(a) UMAP embedding of scRNA-seq data (Seurat framework) using MEFs, reprogramming intermediates on days 2, 4, 6, 8 for WT and K36M, as well as passaged iPSCs. For each indicated group, one sample was encapsulated leading to n = 38,743 cells total. (b) Expression of pluripotency gene Nanog projected on the same UMAP embedding as in (a). (c) Expression of mesenchymal gene Prrx1 projected on the same UMAP embedding as in (a). (d,e) Diffusion pseudotime mapping of day 2 to day 8 intermediates undergoing reprogramming. WT cells are colored in blue, K36M cells in red. (f) Expression of pluripotency gene Nanog projected on the same pseudotime embedding as in (d). (g) Expression of epithelial gene Cdh1 projected on the same pseudotime embedding as in (d). (h) Expression of mesenchymal gene Zeb1 projected on the same pseudotime embedding as in (d). (i) Relative expression (RNA-seq) of mesenchymal and epithelial genes in MEFs expressing H3.3 WT or K36M, but not OKSM, n = 2 biologically independent experiments. (j) Gene ontology terms of genes downregulated in K36M MEFs without OKSM. Analysis and p values from geneontology.org. (k) Gene ontology terms of genes upregulated in K36M MEFs without OKSM. Analysis and p values from geneontology.org.
Extended Data Fig. 4 K36M confers epithelial plasticity on cells undergoing reprogramming.
(a) Gene expression of Twist1 and Cdh1 on the same UMAP embedding as used in Fig. 2d. Dashed circles encompass day-2 and day-4 samples for WT (blue circle) and K36M (red circle). Solid arrow indicates switch-like MET in K36M samples, dashed arrow indicates heterogeneous maintenance/activation of mesenchymal/epithelial programs in WT cells. For each group, one sample was encapsulated leading to n = 38,743 cells total. (b) Immunofluorescence for Vimentin and Epcam in WT and K36M cells on day 4 of reprogramming. Scale bar = 25 µm. Three independent biological experiments with similar results. (c) Correlation plots of single-cell RNA-seq data comparing transcriptional programs within each cell to MEFs (y-axis) and ESCs (x-axis)107. For each sample, the corresponding cells are colored according to their Epcam expression levels, whereas other cells are greyed out. (d) Correlation plots as in (c), expression data of Twist1 is superimposed. (e) Correlation plots as in (c), expression data of Pou5f1 is superimposed.
Extended Data Fig. 5 K36M disrupts TGFβ signaling and modulates epithelial plasticity in diverse contexts.
(a) Flow cytometry histograms displaying Epcam expression in day-4 reprogramming intermediates for WT and K36M samples. Untreated control cells are compared to cells treated with 250 nM Repsox (TGFβi) or 2.5 ng/ml recombinant TGFβ-1 or -2 (rTGFβ-1, rTGFβ-2). (b) Fraction of Oct4-GFP+ cells treated with TGFβi or rTGFβ in day 4 reprogramming intermediates. Error bars indicate mean ± SD (n = 3). (c) Representative tracks for expression of mesenchymal gene Col1a2 on day 8 of reprogramming, WT or K36M cells were treated as indicated. (d) Representative tracks for expression of epithelial gene Cdh1 on day 8 of reprogramming, WT or K36M cells were treated as indicated. (e) Representative tracks for miR-200a and miR-290. (f) Schematic of K36M’s effect on TGFβ signaling and miRNA expression during reprogramming. (g) De-differentiation of MEFs to induced myogenic progenitor cells (iMPCs). qRT-PCR for myotube marker Myh1 and iMPC marker Pax7, P values were determined by two-sided unpaired Student’s t test, error bars indicate mean ± SD (n = 3 biologically independent experiments). Flow cytometry for Pax7-GFP reporter positive cells. (h,i) Immunofluorescence of Nanog and K36M in ES cells of both backgrounds, cultured in S/L (g) or 2iL (h) conditions. Result is representative of three independent biological experiments. (j) Differentiation of ESC aggregates to elongated gastruloids. Representative brightfield images (scale bar = 500 µm) and quantification of long axis diameter (line = mean). P value was determined by two-sided unpaired Student’s t test, n = 19 for WT, n = 20 for K36M. (k) Differentiation of ESCs to pre-somitic mesoderm. Representative immunofluorescence for Cdh2 (scale bar = 50 µm). qRT-PCR for mesodermal transcription factors Tbx6 and Msgn1. P values were determined by two-sided unpaired Student’s t test, error bars indicate mean ± SD (n = 3 independent biological experiments).
Extended Data Fig. 6 H3K36me2 and H3K36me3 cooperatively safeguard cell identity.
(a) Histone methyltransferases and demethylases implicated in the regulation of H3K36me2 (top) and H3K36me3 (bottom). (b) Fraction of Epcam+ cells on day 4 of reprogramming (top) in WT cells with knockdown of indicated histone methyltransferases. Colony counts after 6 days of doxycycline followed by 4 days of independent growth (bottom), error bars indicate mean ± SD (n = 3 biologically independent experiments). (c,d) Fraction of Epcam+ (c) or Oct4-GFP+ (d) cells on day 8 of reprogramming in WT and K36M cells transduced with either empty vector or dox-inducible Nsd2, error bars indicate mean ± SD (n = 3 biologically independent experiments). (e) Flow cytometry for Epcam on day 8 of reprogramming in WT cells with knockdown of the indicated histone demethylases. (f,g) Fraction of Epcam+ cells on day 4 (f) and day 8 (g) of reprogramming in WT cells with knockdown of the indicated histone demethylases, error bars indicate mean ± SD (n = 3 biologically independent experiments). (h) Fraction of Oct4-GFP+ cells on day 8 of reprogramming in WT cells with knockdown of the indicated histone demethylases. P values were determined by unpaired Student’s t test, error bars indicate mean ± SD (n = 3 biologically independent experiments). (i,j) Flow cytometry for Epcam during reprogramming in WT cells with overexpression of the indicated histone demethylases, error bars indicate mean ± SD (n = 3 biologically independent experiments). (k) Fraction of Oct4-GFP+ cells on day 4 and day 8 of reprogramming in WT cells with overexpression of the indicated histone demethylases, error bars indicate mean ± SD (n = 3 biologically independent experiments). (l) qRT-PCR for mesenchymal genes Vim and Prrx1, epithelial genes Epcam and Cdh1, and pluripotency gene Pou5f1 on day 4 of reprogramming in WT cells overexpressing Kdm2a vs. empty vector control. P values were determined by unpaired Student’s t test, error bars indicate mean ± SD (n = 3 independent biological experiments).
Extended Data Fig. 7 K36M alters H3K36me2/3 deposition and gene expression.
(a) Median H3K36me3 deposition at expressed genes (RPKM > 0.1, n = 10,251 for WT, n = 10,496 for K36M) of indicated expression quintiles for WT (left) and K36M (right) samples on day 4 of reprogramming (n = 2 biologically independent replicates were integrated for this analysis). (b) Median fold change of H3K36me3 density over gene bodies of differentially expressed genes (upregulated = red, downregulated = blue, n = 1,872) on day 4 (n = 2 biologically independent replicates were integrated for this analysis). (c) Fold change of gene expression (y axis) vs. fold change of H3K36me3 (x axis) between K36M and WT samples on day 4 of reprogramming (n = 2 biologically independent replicates were integrated for this analysis). (d) Representative gene tracks for H3K36me3 and RNA levels at mesenchymal gene Vim, epithelial gene Cdh1, and pluripotency gene Pou5f1 (n = 2 biologically independent replicates). (e) Profile plots of H3K36me2 at promoters, enhancers, and gene bodies (n = 2 biologically independent replicates were integrated for this analysis). (f) Profile plots of H3K36me2 and H3K27ac at H3K36me2 domains containing down- or upregulated enhancers (n = 2 biologically independent replicates were integrated for this analysis). (g) Representative tracks for Prrx1, a mesenchymal gene downregulated in K36M samples on day 4, Krt8, and Pou5f1, epithelial/pluripotency genes upregulated in K36M samples on day 4 (n = 2 biologically independent replicates). Putative regulatory elements highlighted in grey. (h) Gene ontology terms of genes closest to H3K36me2 domain-embedded enhancers that are differentially downregulated in K36M cells. Analysis and p values from geneontology.org. (i) Gene ontology terms of genes closest to H3K36me2 domain-embedded enhancers that are differentially upregulated in K36M cells. Analysis and p values from geneontology.org. (j) Dot plot representing enrichment of ENCODE data for differentially active enhancers within H3K36me2 domains. P values were determined by Fisher’s exact test.
Extended Data Fig. 8 PRC2 contributes to the K36M-dependent silencing of the somatic program.
(a) H3K27me3 deposition within H3K36me2 domains in K36M vs. WT samples in day-4 reprogramming intermediates. Domains gaining H3K27me3 are colored in red, domains losing H3K27me3 are colored in blue. (b) Ontology terms for genes with promoters overlapping H3K36me2 domains and gaining H3K27me3. Analysis and p values from geneontology.org. (c) Heatmaps for H3K27me3 and H3K4me3 at promoters mesenchymal and epithelial genes in WT and K36M samples. (d,e) Fraction of Epcam+ and Oct4-GFP+ cells in WT (blue) and K36M (red) samples with knockdown of indicated PRC2 components (top). Log2(fold change) of fraction normalized to control siRNA (bottom). Error bars indicate mean ± SD (n = 3 independent biological experiments). (f) Representative histograms of flow cytometry for Epcam in K36M cells with control siRNA and knockdown of Ezh2 or Suz12. (g) qRT-PCR for mesenchymal (Vim, Prrx1, Zeb1), epithelial (Cdh1, Epcam), and pluripotency (Pou5f1) marker genes, error bars indicate mean ± SD (n = 3 biologically independent experiments). (h) Immunofluorescence for H3K27me3 in WT and K36M cells transfected with control siRNA or knockdown of Ezh2 or Suz12. Representative result from three independent biological experiments.
Extended Data Fig. 9 K36M rewires DNA methylation patterns.
(a) Number of colonies following alkaline phosphatase staining of WT and K36M cell cultures transduced with non-selectable, dox-inducible lentiviruses for the expression of SKM, OSM, or OKM. Cultures were induced for 12 days and stained on day 15, n = 3 independent biological experiments. (b) Correlation plot of log2(fold-change) differences (K36M vs. WT) at Sox2 peaks called in WT and K36M samples. Differences of Sox2 enrichment are correlated with differences in H3K27ac abundance (n = 2 biologically independent replicates were integrated for this analysis). Pearson’s R = 0.56. (c) Profile plots showing H3K36me2 abundance at ectopic and ESC-specific Sox2 sides in WT and K36M cells on day 4 of reprogramming. (d) Sox2 enrichment at Sox2 binding sites as defined in iPSCs, log2(RPKM). (e) Correlation plot of log2(fold-change) differences (K36M vs. WT) at Sox2 peaks called in WT and K36M samples. Differences of Sox2 enrichment are correlated with differences in chromatin accessibility (as measured by ATAC-seq, n = 2 biologically independent replicates were integrated for this analysis). Pearson’s R = 0.68. (f) Median chromatin accessibility (as measured by ATAC-seq, n = 2 biologically independent replicates were integrated for this analysis) at ectopic and ESC-specific Sox2 binding sites in MEFs and passaged iPSCs. (g) Density ridge plots of DNA methylation at differentially methylated regions losing (left, n = 30,294) or gaining (right, n = 28,060) methylation in iPSCs vs. MEFs (n = 2 biologically independent replicates for uninduced MEFs, for d4 and d8 samples were integrated, one sample for each genotype in uninduced iPSCs). (h-k) Representative gene tracks of Cdh1, Krt8, the miR-290 cluster, and Pou5f1 (n = 2 biologically independent replicates). Putative regulatory elements affected by DNA demethylation are highlighted in grey. (l) CpG density at differentially active enhancers in H3K36me2 domains (n = 4,939). P value determined by two-sided Wilcoxon rank sum test. Center line indicates median; lower/upper hinges indicate 25th/75th percentiles; whiskers extend to 1.5x IQR. P value determined by two-sided Wilcoxon rank sum test.
Extended Data Fig. 10 DNA demethylation is limiting for K36M-dependent enhancer rewiring.
(a) Distribution of DNA methylation (WGBS, n = 2 biologically independent replicates were integrated for this analysis) at differentially active enhancers in H3K36me2 domains (n = 4,939) in day-4 reprogramming intermediates of WT, K36M and K36M + DMOG samples. (b) Distribution of DNA methylation (WGBS, n = 2 biologically independent replicates were integrated for this analysis) at ectopic (n = 45,095) and ESC-exclusive (n = 27,708) Sox2 binding sites in day-4 reprogramming intermediates of WT, K36M and K36M + DMOG samples. (c) Representative histogram plots from flow cytometric analysis for Epcam of K36M cells with knockdown of the indicated Tet demethylases. (d) Fraction of Epcam+ cells in K36M cells with Tet knockdown on day 4 of reprogramming, error bars indicate mean ± SD (n = 3 biologically independent experiments). (e) qPCR of miRNAs miR-200b-3p, miR-205-5p, and miR-290a-5p in untreated (K36M Ctrl) and DMOG-treated K36M cells (K36M DMOG). P values were determined by unpaired Student’s t test, error bars indicate mean ± SD (n = 3 independent biological experiments). (f) Fraction of Epcam+ cells in K36M cultures transduced with either an empty vector or dox-inducible overexpression vectors for Dnmt3a and Dnmt3b, error bars indicate mean ± SD (n = 3 independent biological experiments). (g) Bisulfite-seq of a Cdh1 enhancer in K36M cells transduced with either empty vector (left), or overexpression of Dnmt3a (middle) or Dnmt3b (right). (h) H3K36me2 levels within H3K36me2 domains (n = 7,610) on day 4 of reprogramming in WT, untreated K36M cells (K36M Ctrl), and DMOG-treated K36M cells (K36M DMOG). Crossbars indicate median (n = 2 biologically independent replicates were integrated for this analysis). (i-k) Representative gene tracks of Krt8, Pou5f1, and the miR-290 cluster (n = 2 biologically independent replicates). Putative regulatory elements highlighted in grey.
Supplementary information
Supplementary Information
Supplementary Fig. 1.
Supplementary Table 1
Knockdown efficiencies (1), primer sequences (2) and Modspec raw data (Extended Data Fig. 1d) (3).
Source data
Source Data Fig. 1
Statistical source data.
Source Data Fig. 3
Statistical source data.
Source Data Fig. 3
Uncropped immunoblot images.
Source Data Fig. 6
Statistical source data.
Source Data Fig. 6
Uncropped dot blot images.
Source Data Extended Data Fig. 1
Statistical source data.
Source Data Extended Data Fig. 2
Statistical source data.
Source Data Extended Data Fig. 3
Statistical source data.
Source Data Extended Data Fig. 5
Statistical source data.
Source Data Extended Data Fig. 6
Statistical source data.
Source Data Extended Data Fig. 7
Statistical source data.
Source Data Extended Data Fig. 8
Statistical source data.
Source Data Extended Data Fig. 9
Statistical source data.
Source Data Extended Data Fig. 10
Statistical source data.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hoetker, M.S., Yagi, M., Di Stefano, B. et al. H3K36 methylation maintains cell identity by regulating opposing lineage programmes. Nat Cell Biol 25, 1121–1134 (2023). https://doi.org/10.1038/s41556-023-01191-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41556-023-01191-z
This article is cited by
-
Gut microbial metabolite facilitates colorectal cancer development via ferroptosis inhibition
Nature Cell Biology (2024)
-
H3K36 methylation is a reprogramming barrier
Nature Cell Biology (2023)