Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Pioneer factor ETV2 safeguards endothelial cell specification by recruiting the repressor REST to restrict alternative lineage commitment

Nature Cardiovascular Research volume 4, pages 689–709 (2025)Cite this article

Subjects

Abstract

Mechanisms of cell fate specification are central to developmental biology and regenerative medicine. ETV2 is a master regulator for the endothelial cell (EC) lineage specification. Here we study mechanisms by which ETV2 overexpression in human induced pluripotent stem-cell-derived mesodermal progenitors efficiently specifies ECs. We used CUT&RUN, scRNA-seq and scATAC-seq to characterize the molecular features of EC differentiation mediated by ETV2. We defined the scope of ETV2 pioneering activity and identified its direct downstream target genes. Induced ETV2 expression both directed specification of endothelial progenitors and suppressed acquisition of alternative fates. Functional screening and candidate validation revealed cofactors essential for efficient EC specification, including the transcriptional activator GABPA. Notably, the transcriptional repressor REST was also necessary for efficient EC specification. ETV2 recruited REST to repress non-EC lineage genes. Our study provides an unparalleled molecular analysis of EC specification at single-cell resolution and highlights the important role of pioneer factors to recruit repressors that suppress commitment to alternative lineages.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Integrated single-cell transcriptomic and epigenomic analysis of EC differentiation.
Fig. 2: Reconstruction of EC differentiation trajectories with and without ETV2 overexpression.
Fig. 3: Integrative scATAC-seq and scRNA-seq analyses nominate cell-type-resolved TFs that regulate EC specification.
Fig. 4: ETV2 promotes EC specification by modulating EC gene expression and GTPase signaling.
Fig. 5: CRISPR-Cas9 screening identifies GABPA and REST as key regulators of ETV2-directed EC specification.
Fig. 6: ETV2 recruits GABPA and REST to enhancers.
Fig. 7: GABPA collaborates with ETV2 to open chromatin and promote EC gene expression.
Fig. 8: REST collaborates with ETV2 to open chromatin and repress gene expression.

Similar content being viewed by others

Data availability

The raw sequencing and related processed data have been deposited in the Gene Expression Omnibus under accession number GSE267565. Source Data are provided with this publication. Other information is available from the corresponding author upon reasonable request.

Code availability

No custom code was used in this study. Analysis was carried out with publicly available pipelines using approaches described in Methods.

References

  1. Wang, K. et al. Robust differentiation of human pluripotent stem cells into endothelial cells via temporal modulation of ETV2 with modified mRNA. Sci. Adv. https://doi.org/10.1126/sciadv.aba7606 (2020).

  2. Lai, W. K. M. & Pugh, B. F. Understanding nucleosome dynamics and their links to gene expression and DNA replication. Nat. Rev. Mol. Cell Biol. 18, 548–562 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Barral, A. & Zaret, K. S. Pioneer factors: roles and their regulation in development. Trends Genet. 40, 134–148 (2024).

    Article  CAS  PubMed  Google Scholar 

  4. Donaghey, J. et al. Genetic determinants and epigenetic effects of pioneer-factor occupancy. Nat. Genet. 50, 250–258 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhou, P. et al. GATA4 regulates developing endocardium through interaction with ETS1. Circ. Res. https://doi.org/10.1161/CIRCRESAHA.120.318102 (2022).

  6. Swinstead, E. E. et al. Steroid receptors reprogram FoxA1 occupancy through dynamic chromatin transitions. Cell 165, 593–605 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Liu, Z. & Kraus, W. L. Catalytic-independent functions of PARP-1 determine Sox2 pioneer activity at intractable genomic loci. Mol. Cell 65, 589–603.e9 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sunkel, B. D. & Stanton, B. Z. Pioneer factors in development and cancer. iScience 24, 103132 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Oh, S. Y., Kim, J. Y. & Park, C. The ETS factor, ETV2: a master regulator for vascular endothelial cell development. Mol. Cell 38, 1029–1036 (2015).

    Article  CAS  Google Scholar 

  10. Liu, F. et al. Induction of hematopoietic and endothelial cell program orchestrated by ETS transcription factor ER71/ETV2. EMBO Rep. 16, 654–669 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Gong, W. et al. ETV2 functions as a pioneer factor to regulate and reprogram the endothelial lineage. Nat. Cell Biol. 24, 672–684 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Koyano-Nakagawa, N. & Garry, D. J. Etv2 as an essential regulator of mesodermal lineage development. Cardiovasc. Res. 113, 1294–1306 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lee, D. et al. ER71 acts downstream of BMP, Notch, and Wnt signaling in blood and vessel progenitor specification. Cell Stem Cell 2, 497–507 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ferdous, A. et al. Nkx2-5 transactivates the Ets-related protein 71 gene and specifies an endothelial/endocardial fate in the developing embryo. Proc. Natl Acad. Sci. USA 106, 814–819 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lee, S. et al. Direct reprogramming of human dermal fibroblasts into endothelial cells using ER71/ETV2. Circ. Res. 120, 848–861 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Ng, A. H. M. et al. A comprehensive library of human transcription factors for cell fate engineering. Nat. Biotechnol. 39, 510–519 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Abedin, M. J. et al. Fli1 acts downstream of Etv2 to govern cell survival and vascular homeostasis via positive autoregulation. Circ. Res. 114, 1690–1699 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Loew, R., Heinz, N., Hampf, M., Bujard, H. & Gossen, M. Improved Tet-responsive promoters with minimized background expression. BMC Biotechnol. 10, 81 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lorenzi, L. et al. The RNA Atlas expands the catalog of human non-coding RNAs. Nat. Biotechnol. 39, 1453–1465 (2021).

    Article  CAS  PubMed  Google Scholar 

  20. Ginsberg, M. et al. Efficient direct reprogramming of mature amniotic cells into endothelial cells by ETS factors and TGFβ suppression. Cell 151, 559–575 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chen, J. et al. Molecular and spatial signatures of mouse embryonic endothelial cells at single-cell resolution. Circ. Res. 134, 529–546 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. You, L.-R. et al. Suppression of Notch signalling by the COUP-TFII transcription factor regulates vein identity. Nature 435, 98–104 (2005).

    Article  CAS  PubMed  Google Scholar 

  24. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Corada, M. et al. Sox17 is indispensable for acquisition and maintenance of arterial identity. Nat. Commun. 4, 2609 (2013).

    Article  PubMed  Google Scholar 

  26. Van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020).

    Article  PubMed  Google Scholar 

  27. Galceran, J., Hsu, S. C. & Grosschedl, R. Rescue of a Wnt mutation by an activated form of LEF-1: regulation of maintenance but not initiation of Brachyury expression. Proc. Natl Acad. Sci. USA 98, 8668–8673 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Russ, A. P. et al. Eomesodermin is required for mouse trophoblast development and mesoderm formation. Nature 404, 95–99 (2000).

    Article  CAS  PubMed  Google Scholar 

  29. Li-Villarreal, N. et al. FOXO1 represses sprouty 2 and sprouty 4 expression to promote arterial specification and vascular remodeling in the mouse yolk sac. Development https://doi.org/10.1242/dev.200131 (2022).

  30. Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362–D368 (2017).

    Article  CAS  PubMed  Google Scholar 

  31. Meers, M. P., Bryson, T. D., Henikoff, J. G. & Henikoff, S. Improved CUT&RUN chromatin profiling tools. eLife https://doi.org/10.7554/elife.46314 (2019).

  32. Jolma, A. et al. DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature 527, 384–388 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Zhou, P. et al. Mapping cell type-specific transcriptional enhancers using high affinity, lineage-specific Ep300 bioChIP-seq. eLife https://doi.org/10.7554/elife.22039 (2017).

  34. De Val, S. et al. Combinatorial regulation of endothelial gene expression by ets and forkhead transcription factors. Cell 135, 1053–1064 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Usuba, R., Pauty, J., Soncin, F. & Matsunaga, Y. T. EGFL7 regulates sprouting angiogenesis and endothelial integrity in a human blood vessel model. Biomaterials 197, 305–316 (2019).

    Article  CAS  PubMed  Google Scholar 

  36. Lee, H. K., Chauhan, S. K., Kay, E. & Dana, R. Flt-1 regulates vascular endothelial cell migration via a protein tyrosine kinase-7-dependent pathway. Blood 117, 5762–5771 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zhu, S. et al. Guide RNAs with embedded barcodes boost CRISPR-pooled screens. Genome Biol. 20, 20 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Naruse, Y., Aoki, T., Kojima, T. & Mori, N. Neural restrictive silencer factor recruits mSin3 and histone deacetylase complex to repress neuron-specific target genes. Proc. Natl Acad. Sci. USA 96, 13691–13696 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Huang, Y., Myers, S. J. & Dingledine, R. Transcriptional repression by REST: recruitment of Sin3A and histone deacetylase to neuronal genes. Nat. Neurosci. 2, 867–872 (1999).

    Article  CAS  PubMed  Google Scholar 

  40. May, D. et al. Large-scale discovery of enhancers from human heart tissue. Nat. Genet. 44, 89–93 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ameen, M. et al. Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease. Cell 185, 4937–4953.e23 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kataoka, H. et al. Etv2/ER71 induces vascular mesoderm from Flk1+PDGFRα+ primitive mesoderm. Blood 118, 6975–6986 (2011).

    Article  CAS  PubMed  Google Scholar 

  43. Resendes, K. K. & Rosmarin, A. G. GA-binding protein and p300 are essential components of a retinoic acid-induced enhanceosome in myeloid cells. Mol. Cell. Biol. 26, 3060–3070 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Choi, J. & Lee, E. A. Analysis of REST binding sites with canonical and non-canonical motifs in human cell lines. BMC Med. Genomics 17, 92 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Paik, D. T. et al. Large-scale single-cell RNA-seq reveals molecular signatures of heterogeneous populations of human induced pluripotent stem cell-derived endothelial cells. Circ. Res. 123, 443–450 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Rasmussen, T. L. et al. ER71 directs mesodermal fate decisions during embryogenesis. Development 138, 4801–4812 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Liu, F. et al. ER71 specifies Flk-1+ hemangiogenic mesoderm by inhibiting cardiac mesoderm and Wnt signaling. Blood 119, 3295–3305 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Matsui, S. et al. Pioneer and PRDM transcription factors coordinate bivalent epigenetic states to safeguard cell fate. Mol. Cell 84, 476–489.e10 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Chong, D. C., Koo, Y., Xu, K., Fu, S. & Cleaver, O. Stepwise arteriovenous fate acquisition during mammalian vasculogenesis. Dev. Dyn. 240, 2153–2165 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Fish, J. E. & Wythe, J. D. The molecular regulation of arteriovenous specification and maintenance. Dev. Dyn. 244, 391–409 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Samuel, R., Duda, D. G., Fukumura, D. & Jain, R. K. Vascular diseases await translation of blood vessels engineered from stem cells. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aaa1805 (2015).

  52. Morita, R. et al. ETS transcription factor ETV2 directly converts human fibroblasts into functional endothelial cells. Proc. Natl Acad. Sci. USA 112, 160–165 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Luo, A. C. et al. A streamlined method to generate endothelial cells from human pluripotent stem cells via transient doxycycline-inducible ETV2 activation. Angiogenesis https://doi.org/10.1007/s10456-024-09937-5 (2024).

  54. Chen, D. Y. et al. Endothelium-derived semaphorin 3G attenuates ischemic retinopathy by coordinating β-catenin-dependent vascular remodeling. J. Clin. Invest. https://doi.org/10.1172/jci135296 (2021).

  55. Chen, D. Y. et al. GPR124 facilitates pericyte polarization and migration by regulating the formation of filopodia during ischemic injury. Theranostics 9, 5937–5955 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Joung, J. et al. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828–863 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  60. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, (2014).

  61. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Nordin, A., Zambanini, G., Pagella, P. & Cantù, C. The CUT&RUN suspect list of problematic regions of the genome. Genome Biol. 24, 185 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. https://doi.org/10.1186/gb-2008-9-9-r137 (2008).

  67. Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Robinson, J. T. et al. Integrative Genomics Viewer. Nat. Biotechnol. 29, 24 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e4 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Institutes of Health (R01HL151450 to W.T.P. and J.M.M.-M.) and American Heart Association (23POST1019621 to D.C.).

Author information

Authors and Affiliations

  1. Department of Cardiology, Boston Children’s Hospital, Boston, MA, USA

    Danyang Chen, Xiaonuo Fan & William T. Pu

  2. Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA, USA

    Ninghe Sun

  3. Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, USA

    Ninghe Sun

  4. Department of Cardiac Surgery, Boston Children’s Hospital, Boston, MA, USA

    Kai Wang, Liyan Gong & Juan M. Melero-Martin

  5. Department of Surgery, Harvard Medical School, Boston, MA, USA

    Kai Wang, Liyan Gong & Juan M. Melero-Martin

  6. Harvard Stem Cell Institute, Cambridge, MA, USA

    Juan M. Melero-Martin & William T. Pu

Authors
  1. Danyang Chen
    View author publications

    Search author on:PubMed Google Scholar

  2. Xiaonuo Fan
    View author publications

    Search author on:PubMed Google Scholar

  3. Ninghe Sun
    View author publications

    Search author on:PubMed Google Scholar

  4. Kai Wang
    View author publications

    Search author on:PubMed Google Scholar

  5. Liyan Gong
    View author publications

    Search author on:PubMed Google Scholar

  6. Juan M. Melero-Martin
    View author publications

    Search author on:PubMed Google Scholar

  7. William T. Pu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

D.C. and W.T.P. conceived of and designed the experiments, interpreted results, and wrote the paper with contributions from all authors. D.C. performed the majority of the experiments, library preparation and bioinformatic analyses. X.F. assisted with cell culture and FACS experiments. N.S. assisted with CRISPR screening and library preparation. K.W., L.G. and J.M.M.-M. provided reagents, resources and interpreted results.

Corresponding author

Correspondence to William T. Pu.

Ethics declarations

Competing interests

K.W. and J.M.M.-M. are inventors on a pending patent related to this work, filed by the Children’s Medical Center Corporation (application no. 16/885,999, filed 28 May 2020). The patent covers the transient activation of ETV2 in mesodermal intermediates as a strategy to generate functionally competent ECs from iPS cells. The other authors declare no competing interests.

Peer review

Peer review information

Nature Cardiovascular Research thanks Claudio Cantù and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Highly efficient and rapid EC differentiation from mesodermal progenitors by ETV2 induction.

a, Overview of the TRE3G-ETV2 system (upper panel) and timeline of 4 day EC differentiation protocol. Cells were treated without Dox (Dox–) or treated with Dox (1 μg/ml) for the indicated length of time. b,c, Flow cytometry analysis (b) and quantification (c) for PECAM1 and CDH5 expression at day 4 of differentiation. n = 4 independent biological replicates. One-way ANOVA with Tukey’s multiple comparison test. d, Scheme of 6 day EC differentiation protocol showing timing of Dox addition. Cells were untreated or treated with Dox (1 μg/ml) for indicated length of time. e,f, Flow cytometry analysis (e) and quantification (f) for PECAM1 and CDH5 expression at Day 6 of differentiation. n = 4 independent biological replicates. One-way ANOVA with Tukey’s multiple comparison test. g, TRE3G-ETV2 iPS cells and wild-type parental iPS cells treated with different dose of Dox at day 2 for 24 hours were analyzed for flow cytometry of PECAM1+ CDH5+ ECs at Day 4. h, TRE3G-ETV2 iPS cells treated with different dose of Dox at day 2 for 24 hours were analyzed for flow cytometry of ETV2 expression at Day 3. n = 3 independent biological replicates. i, Representative immunofluorescence for ETV2 in control conditions at day 3.5 of differentiation (D3.5 Dox–) or 24 h after doxycycline induction at day 3 of differentiation (D3 Dox + ). Scale bars, 50 μm. For quantification, n = 8 (D3.5 Dox–) or 7 (D3 Dox + ) independent biological replicates. Level of ETV2 expression in each condition as measured by the immunofluorescence signal intensity. n = 16 representative images per condition. Unpaired two-tailed Student’s t-test. j, Left panel, histogram of ETV2 expression levels of iPS cells, D3.5 Dox– cells, and D3 Dox+ cells. Right panel, quantification of ETV2+ cells in D3.5 Dox– and D3 Dox+ condition. n = 3 independent biological replicates. Unpaired two-tailed Student’s t-test. MFI, mean fluorescence intensity. In panels c,f,h,i,j, data are shown as mean ± s.e.m.

Source data

Extended Data Fig. 2 Functional characterization of iPS cell-derived ECs with ETV2 overexpression.

a, iPS cell-derived ECs immunostained for CDH5 (red), PECAM1 (green), and DAPI (blue). Scale bar, 50 μm. b, Alignment of iPS cell-derived ECs under static and flow conditions. 0° represents the direction of flow. Scale bar, 50 μm. c, Migration of iPS cell-derived ECs in the wound scratch assay. Scale bar, 300 μm. d, Nitric oxide (NO) production of iPS cell-derived ECs measured by flow cytometry upon exposure to DAF-FM. MFI, mean fluorescence intensity. n = 3 independent biological replicates. Unpaired two-tailed Student’s t-test. e, AcLDL uptake by iPS cell-derived ECs. Scale bar, 25 μm. Data in a-c,e are representative of n = 3 biologically independent experiments. f, Expression of cell surface adhesion molecules ICAM1, VCAM1, and SELE in iPS cell-derived ECs when stimulated with 10, 100, or 500 ng/μL TNF. n = 3 independent biological replicates. One-way ANOVA with Dunnett’s multiple comparison test. Data in d,f are shown as mean ± s.e.m. g, PCA plot based on the top 1,000 most variable genes across iPS cell-derived ECs and indicated human cell types (from GSE138734). h, Heatmap of the top DEGs in each cluster and their top enriched GO terms (Fisher’s exact test, corrected for multiple testing using the Benjamini–Hochberg method). reg, regulation; dev, development; pos, positive. i,j. Wild-type parental iPS cells and TRE3G-ETV2 iPS cells were differentiated with different concentrations of Dox and analyzed at Day 4 by scRNA-seq. i, UMAP of each genotype and Dox concentration, colored by clusters. j, Integrated UMAPs colored by cell clusters (left) and by expression of selected marker genes.

Source data

Extended Data Fig. 3 Quality control of scRNA-seq and scATAC-seq data.

a, Quality control metrics of scRNA-seq data for cell samples from Dox– and Dox+ groups. Number of genes (nFeature_RNA), number of molecular (nCount_RNA), and percentage of mitochondrial counts (percent.mt) is plotted for each sample. b, Quality control metrics of scATAC-seq data for cell samples from Dox– and Dox+ groups. TSS enrichment score for the cells in each sample. c, Fragment length periodicity for all the cells in each sample. d, The number of peaks for each time point. Bars are colored by peak ___location relative to genomic annotations.

Extended Data Fig. 4 Gene expression of representative cell type markers and integration of scRNA-seq and scATAC-seq data.

a, Dot plots of quantitative bulk changes in gene expression between Dox– and Dox+ cells for markers of mesoderm, ectoderm, endoderm, cardiac precursors, early ECs, mature ECs, arterial EC, venous EC, genes involved in hematopoietic lineage development, and capillary ECs. b–d, Genome tracks of time point-resolved aggregate scATAC-seq data around the MIXL1 (b), ESAM (c) and GJA4 (d) gene loci. e, Heatmap showing the cluster-cluster mapping between scRNA-seq and scATAC-seq clusters after canonical correlation analysis (CCA) matching. f, Heatmap of 77,679 statistically significant peak-to-gene links identified across the dataset with ArchR. Each row represents expression of a gene (scRNA, right) correlated to accessibility of a ATAC-seq peak (scATAC, left).

Extended Data Fig. 5 Differentiation trajectory terminating on cardiac progenitor cells.

a, Gene expression and ATAC gene score dynamics of ETV2 in Dox+ and Dox– EC trajectories across pseudotime. b, UMAP visualization of the cardiac progenitor trajectory of Dox– cells. Each cell is colored by its pseudotime and each cluster is annotated by sample collection time. The smoothed arrow represents a visualization of the interpreted trajectory in the UMAP embedding. c, Gene expression and ATAC gene score dynamics of the CDH5, ETV2, and cardiac progentior genes MEF2C and HAND2. d, Heatmap of TF regulators for which ATAC gene score is positively correlated with chromVAR TF deviation (motif deviation) across the Dox– cardiac progenitor trajectory, ordered by pseudotime.

Extended Data Fig. 6 ETV2 drives endothelial specification in collaboration with GABPA in endothelial progenitors.

a, Visualization of TF-target regulatory networks formed by ETV2 and GABPA. The pink nodes represent the TFs. The green nodes represent the targets. The arrows represent the connections between each of TFs and their predicted target genes (GABPA, blue line; ETV2, gray line).

Extended Data Fig. 7 ETV2 directly binds promoters and enhancers of target genes.

a, Timing of the ETV2, H3K4me3, H3K27ac and H3K4me1 CUT&RUN analyses. ETV2 tagged with V5 was induced at day 2 by addition of Dox, and CUT&RUN was performed at day 3. b, Scatter plots comparing the D3 Dox+ ETV2 and V5 CUT&RUN signals during EC differentiation between two biological replicates. Pearson correlation coefficients are shown. c, Venn diagram showing overlap between D3 Dox+ ETV2 and V5 CUT&RUN peaks. Overlapping peaks were defined as high confidence ETV2 regions. d, The top enriched motifs in all ETV2 regions, de novo or primed ETV2 regions in D3 Dox+ cells. Motif enrichment P values were calculated using a one-sided binomial test in HOMER. e, ETV2 (orange) and V5 (blue) CUT&RUN signal at high confidence ETV2 regions. Each row represents a peak region (peak center ± 5 kb). f, Aggregation plots of ETV2 and V5 CUT&RUN signal at high confidence ETV2 regions in D3 Dox– and D3 Dox+ cells. g, Genomic distribution of high confidence ETV2 regions in D3 Dox+ cells. The number in brackets is the percentage of ETV2 peaks. h, Genome tracks of D3 Dox+ ETV2, V5 and IgG CUT&RUN data around the TJP2, ELK3, and LMO2 gene loci. i, Motifs enriched in ETV2-bound de novo, primed, and closed regions (presented in Fig. 4b, one-sided binomial test). j, GSEA of genes linked to ETV2-bound de novo accessible regions. These genes were enriched among differentially expressed genes in D3 Dox+ ETV2OE EPs (CL2 versus CL0). Normalized enrichment score (NES) and FDR are calculated using GSEA. k,l, GO term analysis of upregulated genes (k) or downregulated genes (l) in D3 Dox+ ETV2OE EPs (CL2 versus CL0, Fisher’s exact test, corrected for multiple testing using the Benjamini–Hochberg method). Box plots showing representative gene expression in CL0 (n = 17,707 cells) and CL2 (n = 10,905 cells). Two-sided LR test, ***P < 2.22e-16. Box plots visualize the median, first and third quartiles, and whiskers extend to 1.5× the interquartile range (IQR).

Extended Data Fig. 8 Validation of top candidates in EC differentiation.

a, Flow cytometry for SSEA-4+ in TRE3G-ETV2 iPS cells that were transfected with sgCtrl, or transfected with sgRNAs targeting to the indicated gene. n = 3 independent biological replicates. One-way ANOVA with Dunnett’s multiple comparison test to sgCtrl. b, Validation of screening hits. TRE3G-ETV2 iPS cells were transfected with sgCtrl or sgRNAs targeting to the indicated gene. Differentiation efficiency to NRP2+ CDH5+ ECs under Dox– conditions was measured by flow cytometry. n = 4 independent biological replicates. One-way ANOVA with Dunnett’s multiple comparison test to sgCtrl. c,d, RT–qPCR analysis of pluripotency markers SOX2, NANOG, OCT4 mRNA expression in iPS cells across groups, and mesoderm markers TBXT and MIXL1 mRNA expression in MPCs across groups. n = 5 independent biological replicates. One-way ANOVA with Dunnett’s multiple comparison test to sgCtrl. e, Flow cytometry analysis and quantification for PECAM1+ CDH5+ in sgKLF6, sgLMO2 and sgYBX1 group under Dox+ and Dox– condition on day 4. n = 3 independent biological replicates. One-way ANOVA with Dunnett’s multiple comparison test to sgCtrl. f, RNA expression dynamics of ETV2, GABPA, REST and KLF6 across the Dox+ EC trajectory (presented in Fig. 2e). The red shaded area indicates the ETV2 expression window. Graphs in a,b,e show mean ± s.d. Graphs in c and d show mean ± s.e.m.

Source data

Extended Data Fig. 9 Knockout of GABPA and REST in TRE3G-ETV2 iPS cells.

a, GABPA and ETV2 proteins level in D3 Dox+ sgCtrl and sgGABPA cells. Representative capillary westerns and quantitative analyses of GABPA normalized to GAPDH. n = 3 independent biological replicates. Unpaired two-tailed Student’s t-test. b, REST and ETV2 proteins level in D3 Dox+ sgCtrl and sgREST cells. Representative capillary westerns and quantitative analyses of REST normalized to GAPDH. n = 3 independent biological replicates. Unpaired two-tailed Student’s t-test. c, GABPA binding signals in D3 Dox+ sgCtrl and sgGABPA cells. Aggregation plots indicate the mean CUT&RUN signal at GABPA sites in each group. d, REST binding signals in D3 Dox+ sgCtrl and sgREST cells. Aggregation plots indicate the mean CUT&RUN signal at REST sites in each group. e, Percentage of ETV2-only peaks, ETV2 & REST co-occupied peaks, REST-only peaks with the indicated motif. f, Heatmaps and pie charts showing D2 GABPA binding signals at D3 Dox+ ETV2 & GABPA co-occupied de novo peaks. g, Heatmaps and pie charts showing D2 REST binding signals at D3 Dox+ ETV2 & REST co-occupied de novo peaks. h, ETV2 & REST binding at a subset of VISTA-validated cardiac enhancers (column 1), accessible chromatin regions in fetal heart (column 2,3) or iPS cell-derived CM (column 4,5). VISTA enhancer coordinates were downloaded from the VISTA Enhancer Browser and filtered for human sequences and positive hits with activity in heart regions. One-sided hypergeometric test. CP, cardiac progenitor. CM, cardiomyocyte. i, Left panel, genome browser views showing GABPA binding signals in D2 cells, ETV2 and GABPA binding signals in D3 Dox+ cells, and D3.5 Dox– FLK1+ cells at representative genes. Right panel, ETV2 and GABPA co-occupied regions enriched GO biological processes terms (GREAT; − log10(binomial FDR), one-sided binomial test) in D3.5 Dox– FLK1+ cells. j, Left panel, genome browser views showing REST binding signals in D2 cells, ETV2 and REST binding signals in D3 Dox+ cells, D3.5 Dox– FLK1+ cells, and D3.5 Dox– FLK1– cells at representative genes. Right panel, ETV2 and REST co-occupied regions enriched GO biological processes terms (GREAT; −log10(binomial FDR), one-sided binomial test) in D3.5 Dox– FLK1+ cells. k, Co-immunoprecipitation (Co-IP) using ETV2 or IgG control antibodies, followed by capillary immunoblotting (IB) for ETV2, GABPA or REST in D3.5 Dox– FLK1+ cells. Data is representative of n = 3 biologically independent experiments. Graphs in a,b show mean ± s.e.m.

Source data

Extended Data Fig. 10 GABPA and REST are required for chromatin opening and transcription activity.

a, Volcano plot showing DEGs identified in D3 Dox+ sgGABPA cells versus D3 Dox+ sgCtrl cells. P values were calculated using two-sided Wald test with Benjamini–Hochberg correction. Significantly upregulated (red) or downregulated genes (teal) are colored, respectively. b, Overlap between genes upregulated in D3 Dox+ cells (versus D3 Dox–) and downregulated in D3 Dox+ sgGABPA cells (versus sgCtrl). c, ETV2 binding signals in D3 Dox+ sgCtrl and D3 Dox+ sgGABPA cells. Regions are group by chromatin occupancy class (de novo, primed, closed and ectopic binding sites). d, Effect of GABPA knockout on chromatin accessibility. A subset of ETV2-bound de novo regions lost accessibility in D3 Dox+ sgGABPA cells. e, Volcano plot showing DEGs identified in D3 Dox+ sgREST cells versus D3 Dox+ sgCtrl cells. P values were calculated using two-sided Wald test with Benjamini–Hochberg correction. Significantly upregulated (red) or downregulated genes (teal) are colored, respectively. f, Overlap between genes downregulated in D3 Dox+ cells (versus D3 Dox–) and upregulated in D3 Dox+ sgREST cells (versus sgCtrl). g, ETV2 binding signals in D3 Dox+ sgCtrl and D3 Dox+ sgREST cells. Regions are group by chromatin occupancy class (de novo, primed, closed and ectopic binding sites). h, Effect of REST knockout on chromatin accessibility. A subset of ETV2-bound de novo regions lost accessibility in D3 Dox+ sgREST cells.

Supplementary information

Supplementary Information

Gating strategy for flow cytometry. All samples were initially gated using forward scatter (FSC) area (FSC-A) and side scatter (SSC) area (SSC-A) gating to identify the live cell population. Single cells were identified and doublets excluded by FSC height (FSC-H) versus FSC-A.

Reporting Summary

Supplementary Tables 1–16.

Source data

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data and unprocessed western blots.

Source Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data and unprocessed western blots.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, D., Fan, X., Sun, N. et al. Pioneer factor ETV2 safeguards endothelial cell specification by recruiting the repressor REST to restrict alternative lineage commitment. Nat Cardiovasc Res 4, 689–709 (2025). https://doi.org/10.1038/s44161-025-00660-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44161-025-00660-y

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research