Genome-scale screens identify JNK–JUN signaling as a barrier for pluripotency exit and endoderm differentiation

Abstract

Human embryonic stem cells (ESCs) and human induced pluripotent stem cells hold great promise for cell-based therapies and drug discovery. However, homogeneous differentiation remains a major challenge, highlighting the need for understanding developmental mechanisms. We performed genome-scale CRISPR screens to uncover regulators of definitive endoderm (DE) differentiation, which unexpectedly uncovered five Jun N-terminal kinase (JNK)–JUN family genes as key barriers of DE differentiation. The JNK–JUN pathway does not act through directly inhibiting the DE enhancers. Instead, JUN co-occupies ESC enhancers with OCT4, NANOG, SMAD2 and SMAD3, and specifically inhibits the exit from the pluripotent state by impeding the decommissioning of ESC enhancers and inhibiting the reconfiguration of SMAD2 and SMAD3 chromatin binding from ESC to DE enhancers. Therefore, the JNK–JUN pathway safeguards pluripotency from precocious DE differentiation. Direct pharmacological inhibition of JNK significantly improves the efficiencies of generating DE and DE-derived pancreatic and lung progenitor cells, highlighting the potential of harnessing the knowledge from developmental studies for regenerative medicine.

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Fig. 1: Genome-scale screens identify regulators of DE differentiation.
Fig. 2: Genetic inactivation of the JNK pathway improves DE differentiation.
Fig. 3: Pharmacological JNK inhibition improves endoderm differentiation.
Fig. 4: JUN impedes chromatin landscape remodeling during ESC-DE differentiation.
Fig. 5: JUN impedes SMAD2/3 reconfiguration during ESC-DE differentiation.
Fig. 6: Modeling the effect of JNK inhibition on ESC-DE transition.

Data availability

Sequencing data are available under NCBI GEO accession number GSE109524. H3K27ac ChIP-seq data are from GSM733718. p300 ChIP-seq data are from GSM803542. Codes used in this study are available from the corresponding authors on request.

References

  1. 1.

    Anderson, K. V. & Ingham, P. W. The transformation of the model organism: a decade of developmental genetics. Nat. Genet. 33, 285–293 (2003).

  2. 2.

    Zhu, Z. & Huangfu, D. Human pluripotent stem cells: an emerging model in developmental biology. Development 140, 705–717 (2013).

  3. 3.

    D’Amour, K. A. et al. Efficient differentiation of human embryonic stem cells to definitive endoderm. Nat. Biotech. 23, 1534–1541 (2005).

  4. 4.

    Robertson, E. J. Dose-dependent Nodal/Smad signals pattern the early mouse embryo. Semin. Cell Dev. Biol. 32, 73–79 (2014).

  5. 5.

    Teo, A. K. K. et al. Pluripotency factors regulate definitive endoderm specification through eomesodermin. Genes Dev. 25, 238–250 (2011).

  6. 6.

    Shi, Z.-D. et al. Genome editing in hPSCs reveals GATA6 haploinsufficiency and a genetic interaction with GATA4 in human pancreatic development. Cell Stem Cell 20, 675–688.e6 (2017).

  7. 7.

    Fisher, J. B., Pulakanti, K., Rao, S. & Duncan, S. A. GATA6 is essential for endoderm formation from human pluripotent stem cells. Biol. Open 6, 1084–1095 (2017).

  8. 8.

    Tiyaboonchai, A. et al. GATA6 plays an important role in the induction of human definitive endoderm, development of the pancreas, and functionality of pancreatic β cells. Stem Cell Rep. 8, 589–604 (2017).

  9. 9.

    Amit, M., Shariki, C., Margulets, V. & Itskovitz-Eldor, J. Feeder layer- and serum-free culture of human embryonic stem cells. Biol. Reprod. 70, 837–845 (2004).

  10. 10.

    Vallier, L., Alexander, M. & Pedersen, R. A. Activin/Nodal and FGF pathways cooperate to maintain pluripotency of human embryonic stem cells. J. Cell Sci. 118, 4495–5509 (2005).

  11. 11.

    James, D., Levine, A. J., Besser, D. & Hemmati-Brivanlou, A. TGFβ/activin/nodal signaling is necessary for the maintenance of pluripotency in human embryonic stem cells. Development 132, 1273–1282 (2005).

  12. 12.

    Brown, S. et al. Activin/Nodal signaling controls divergent transcriptional networks in human embryonic stem cells and in endoderm progenitors. Stem Cells 29, 1176–1185 (2011).

  13. 13.

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

  14. 14.

    Osafune, K. et al. Marked differences in differentiation propensity among human embryonic stem cell lines. Nat. Biotech. 26, 313–315 (2008).

  15. 15.

    Conlon, F. L., Barth, K. S. & Robertson, E. J. A novel retrovirally induced embryonic lethal mutation in the mouse: assessment of the developmental fate of embryonic stem cells homozygous for the 413.d proviral integration. Development 111, 969–981 (1991).

  16. 16.

    González, F. et al. An iCRISPR platform for rapid, multiplexable, and inducible genome editing in human pluripotent stem cells. Cell Stem Cell 15, 215–226 (2014).

  17. 17.

    Zhu, Z., Verma, N., González, F., Shi, Z.-D. & Huangfu, D. A CRISPR/Cas-mediated selection-free knockin strategy in human embryonic stem cells. Stem Cell Rep. 4, 1103–1111 (2015).

  18. 18.

    Kanai-Azuma, M. et al. Depletion of definitive gut endoderm in Sox17 null mutant mice. Development 129, 2367–2379 (2002).

  19. 19.

    Arnold, S. J., Hofmann, U. K., Bikoff, E. K. & Robertson, E. J. Pivotal roles for eomesodermin during axis formation, epithelium-to-mesenchyme transition and endoderm specification in the mouse. Development 135, 501–511 (2008).

  20. 20.

    Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

  21. 21.

    Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).

  22. 22.

    Parnas, O. et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675–686 (2015).

  23. 23.

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

  24. 24.

    Zorn, A. M. & Wells, J. M. Vertebrate endoderm development and organ formation. Annu. Rev. Cell Dev. Biol. 25, 221–251 (2009).

  25. 25.

    Gu, Z. et al. The type I serine/threonine kinase receptor ActRIA (ALK2) is required for gastrulation of the mouse embryo. Development 126, 2551–2561 (1999).

  26. 26.

    Nomura, M. & Li, E. Smad2 role in mesoderm formation, left-right patterning and craniofacial development. Nature 393, 786–790 (1998).

  27. 27.

    Sirard, C. et al. The tumor suppressor gene Smad4/Dpc4 is required for gastrulation and later for anterior development of the mouse embryo. Genes Dev. 12, 107–119 (1998).

  28. 28.

    Yamamoto, M. et al. The transcription factor FoxH1 (FAST) mediates Nodal signaling during anterior-posterior patterning and node formation in the mouse. Genes Dev. 15, 1242–1256 (2001).

  29. 29.

    Haegel, H. et al. Lack of beta-catenin affects mouse development at gastrulation. Development 121, 3529–3537 (1995).

  30. 30.

    Hart, A. H. et al. Mixl is required for axial mesendoderm morphogenesis and patterning in the murine embryo. Development 129, 3597–3608 (2002).

  31. 31.

    Estarás, C., Benner, C. & Jones, K. A. SMADs and YAP compete to control elongation of β-catenin:LEF-1-recruited RNAPII during hESC differentiation. Mol. Cell. 58, 780–793 (2015).

  32. 32.

    Beyer, T. A. et al. Switch enhancers interpret TGF-β and hippo signaling to control cell fate in human embryonic stem cells. Cell Rep. 5, 1611–1624 (2013).

  33. 33.

    Vierbuchen, T. et al. AP-1 Transcription Factors and the BAF Complex Mediate Signal-Dependent Enhancer Selection. Mol. Cell 68, 1067–1082.e12 (2017).

  34. 34.

    Davis, R. J. Signal transduction by the JNK group of MAP kinases. Cell 103, 239–252 (2000).

  35. 35.

    Angel, P., Hattori, K., Smeal, T. & Karin, M. The jun proto-oncogene is positively autoregulated by its product, Jun/AP-1. Cell 55, 875–885 (1988).

  36. 36.

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

  37. 37.

    Zhang, T. et al. Discovery of potent and selective covalent inhibitors of JNK. Chem. Biol. 19, 140–154 (2012).

  38. 38.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  39. 39.

    Rezania, A. et al. Reversal of diabetes with insulin-producing cells derived in vitro from human pluripotent stem cells. Nat. Biotech. 32, 1121–1133 (2014).

  40. 40.

    Pagliuca, F. W. et al. Generation of functional human pancreatic β cells in vitro. Cell 159, 428–439 (2014).

  41. 41.

    Mullen, A. C. et al. Master transcription factors determine cell-type-specific responses to TGF-β signaling. Cell 147, 565–576 (2011).

  42. 42.

    Tsankov, A. M. et al. Transcription factor binding dynamics during human ES cell differentiation. Nature 518, 344–349 (2015).

  43. 43.

    Loh, KyleM. et al. Efficient endoderm induction from human pluripotent stem cells by logically directing signals controlling lineage bifurcations. Cell Stem Cell 14, 237–252 (2014).

  44. 44.

    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).

  45. 45.

    Karnik, R. & Beer, M. A. Identification of predictive CIS-regulatory elements using a discriminative objective function and a dynamic search space. PLoS ONE 10, e0140557 (2015).

  46. 46.

    Ghandi, M., Lee, D., Mohammad-Noori, M. & Beer, M. A. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput. Biol. 10, e1003711 (2014).

  47. 47.

    Hilberg, F., Aguzzi, A., Howells, N. & Wagner, E. F. c-Jun is essential for normal mouse development and hepatogenesis. Nature 365, 179–181 (1993).

  48. 48.

    Johnson, R. S., van Lingen, B., Papaioannou, V. E. & Spiegelman, B. M. A null mutation at the c-jun locus causes embryonic lethality and retarded cell growth in culture. Genes Dev. 7, 1309–1317 (1993).

  49. 49.

    Xu, P. & Davis, R. J. c-Jun NH2-terminal kinase is required for lineage-specific differentiation but not stem cell self-renewal. Mol. Cell. Biol. 30, 1329–1340 (2010).

  50. 50.

    Liu, J. et al. The oncogene c-Jun impedes somatic cell reprogramming. Nat. Cell Biol. 17, 856–867 (2015).

  51. 51.

    Chronis, C. et al. Cooperative binding of transcription factors orchestrates reprogramming. Cell 168, 442–459.e20 (2017).

  52. 52.

    Li, D. et al. Chromatin accessibility dynamics during IPSC reprogramming. Cell Stem Cell 21, 819–833.e6 (2017).

  53. 53.

    Biddie, S. C. et al. Transcription factor AP1 potentiates chromatin accessibility and glucocorticoid receptor binding. Mol. Cell 43, 145–155 (2011).

  54. 54.

    Heinz, S. et al. Effect of natural genetic variation on enhancer selection and function. Nature 503, 487–492 (2013).

  55. 55.

    Phanstiel, D. H. et al. Static and dynamic DNA loops form AP-1-bound activation hubs during macrophage development. Mol. Cell 67, 1037–1048.e6 (2017).

  56. 56.

    Nostro, M. C. et al. Efficient generation of NKX6-1+ pancreatic progenitors from multiple human pluripotent stem cell lines. Stem Cell Rep. 4, 591–604 (2015).

  57. 57.

    McCauley, K. B. et al. Efficient derivation of functional human airway epithelium from pluripotent stem cells via temporal regulation of wnt signaling. Cell Stem Cell 20, 844–857.e6 (2017).

  58. 58.

    Green, M. D. et al. Generation of anterior foregut endoderm from human embryonic and induced pluripotent stem cells. Nat. Biotechnol. 29, 267–272 (2011).

  59. 59.

    Huang, S. X. L. et al. Efficient generation of lung and airway epithelial cells from human pluripotent stem cells. Nat. Biotechnol. 32, 84–91 (2013).

  60. 60.

    Mandegar, M. A. et al. CRISPR interference efficiently induces specific and reversible gene silencing in human iPSCs. Cell Stem Cell. 18, 541–553 (2016).

  61. 61.

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 489, 57–74 (2012).

  62. 62.

    Zhu, Z. et al. Genome editing of lineage determinants in human pluripotent stem cells reveals mechanisms of pancreatic development and diabetes. Cell Stem Cell 18, 755–768 (2016).

  63. 63.

    Gotoh, S. et al. Generation of alveolar epithelial spheroids via isolated progenitor cells from human pluripotent stem cells. Stem Cell Rep. 3, 394–403 (2014).

  64. 64.

    Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

  65. 65.

    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).

  66. 66.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  67. 67.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).

  68. 68.

    Mo, A. et al. Epigenomic landscapes of retinal rods and cones. eLife 5, e11613 (2016).

  69. 69.

    van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018).

  70. 70.

    Wang, Q. et al. The p53 family coordinates Wnt and nodal inputs in mesendodermal differentiation of embryonic stem cells. Cell Stem Cell 20, 70–86 (2017).

  71. 71.

    Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 1–9 (2015).

  72. 72.

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

  73. 73.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  74. 74.

    Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).

  75. 75.

    Lee, D. et al. A method to predict the impact of regulatory variants from DNA sequence. Nat. Genet. 47, 955–961 (2015).

  76. 76.

    Beer, M. A. Predicting enhancer activity and variant impact using gkm-SVM. Hum. Mutat. 38, 1251–1258 (2017).

  77. 77.

    Kreimer, A. et al. Predicting gene expression in massively parallel reporter assays: a comparative study. Hum. Mutat. 38, 1240–1250 (2017).

  78. 78.

    Moris, N., Pina, C. & Arias, A. M. Transition states and cell fate decisions in epigenetic landscapes. Nat. Rev. Genet. 17, 693–703 (2016).

  79. 79.

    Huang, S., Guo, Y.-P., May, G. & Enver, T. Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev. Biol. 305, 695–713 (2007).

  80. 80.

    François, P. & Hakim, V. Design of genetic networks with specified functions by evolution in silico. Proc. Natl Acad. Sci. USA 101, 580–585 (2004).

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Acknowledgements

The CV iPSC line was a gift from L. Goldstein. We thank Z. Zhu, Z.-D. Shi, I. Caspi and T. Leonardo for technical assistance; S. Mehta for assistance with CRISPR screen HiSeq; Y. Zou for assistance with ChIP-seq analysis; A.-K. Hadjantonakis and S. Tao for assisting with additional experiments not included in the manuscript; and K.V. Anderson, L. Studer, W. Guo, L. Dow and members of D.H.’s laboratory for insightful discussions. This study was supported in part by New York State Stem Cell Science (NYSTEM; grant nos. C029156 and C32593GG); NIH (grant no. R01DK096239); the MSKCC Cancer Center Support Grant (grant no. P30CA008748); NIH grant no. U01HG009380 (to M.A.B.); NIH grant no. R01HG007348 (to M.A.B.); an NIH T32 Training Grant in Developmental and Stem Cell Biology (grant no. T32HD060600 to G.D.); an NIH T32 Training Grant in Molecular and Cellular Biology (grant no. T32GM008539 to B.P.R.); a Howard Hughes Medical Institute Medical Research Fellowship (to N.V.); and a postdoctoral fellowship from a NYSTEM grant (no. C026879 to Q.W.) to the Center for Stem Cell Biology of the Sloan Kettering Institute and the National Natural Science Foundation of China (grant no. 31771512 to Q.W.).

Author information

Q.V.L. and D.H. designed experiments and analyzed and interpreted results. Q.V.L. performed most experiments. M.A.B. performed the mathematical modeling and computational analysis. G.D., B.P.R., R.L. and A.S. assisted with the screen and the validation experiments. N.V. performed neuroectoderm differentiation experiments. Q.X. and R.G. provided assistance related to the CRISPR library construction and sequencing. Q.W. and J.M. provided protocols, reagents and technical support on experiments related to ChIP-seq and TGF-β signaling. M.W. and R.K. assisted with ATAC-seq and ChIP-seq experiments. C.-L.S., D.Y., M.G., C.X., M.C., S.C. and T.E. tested JNKis and assisted with additional experiments and data analyses. F.D., P.Z. and D.B. performed Drop-Seq data analysis. Q.V.L., M.A.B. and D.H. wrote the manuscript. All other authors provided editorial advice.

Correspondence to Michael A. Beer or Danwei Huangfu.

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Competing interests

D.H. and Q.V.L. are listed as inventors for a patent application (publication no. WO/2018/035454) relating to this work for the application of JNKi in endoderm differentiation. J.M. is an advisor and stockholder of Scholar Rock.

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Integrated supplementary information

Supplementary Fig. 1 Generation of the HUES8 SOX17GFP/+ iCas9 line.

a, Schematic shows the iCRISPR platform used for efficient genome editing. Doxycycline inducible Cas9 cells were transduced with lentiviruses expressing gRNAs for genome editing. b, Knock-in strategy for generating the SOX17GFP/+ reporter line. The PAM sequence is labeled in purple, the gRNA sequence is labeled in green. c, Southern blotting analysis. 5′ external probe was used to detect the integration of the GFP cassette at one of the SOX17 alleles. Uncropped image from one single experiment is shown in Supplementary Fig. 10. d, Immunostaining of DE cells differentiated from the SOX17GFP/+ cell line for GFP, SOX17, and FOXA2 expression. Scale bar, 100 μm. Representative images are from 2 independent experiments. e, Representative flow cytometry dot plots of DE cells differentiated from the SOX17GFP/+ cell line co-stained for SOX17 and GFP. Representative images are from 2 independent experiments. f, Representative flow cytometry dot plots of DE cells differentiated from SOX17GFP/+ cells that were infected with the EOMES-gRNA lentivirus (at MOI 0.36) with puromycin selection. The control sample (Ctrl) represents DE cells differentiated from uninfected WT SOX17GFP/+ cells. Representative images are from 3 independent experiments. g, Karyotyping result of the HUES8 SOX17GFP/+ line. h, Flow cytometry gating strategy. The FSC-A/SSC-A gate identifies DE cells based on the size and granularity. The FSC-H/FSC-W and SSC-H/SSC-W gates identify single cells. Live-dead staining distinguishes live cells from dead cells.

Supplementary Fig. 2 Genome-scale screen results.

a, Method of calculating the Z-score of each gRNA from raw read counts. b, Method of calculating and ranking the Z-score of each hit gene. c, A scatter plot of the gRNA distribution from Brunello screen. y axis: Z-score of log2 fold change of SOX17 / SOX17+. x axis: the mean abundance of gRNA reads in the SOX17 and SOX17+ populations. Each grey dot represents an individual targeting gRNA. Each black dot represents a non-targeting control gRNA (1,000 total). Selected positive and negative regulator hits are labeled in green and red, respectively. d, Theoretical FDR calculated for GeCKO screen based on MAGeCK v.0.5.7. For each theoretical FDR, the replicated fraction (frepl), is the fraction of genes below that FDR which are also below that FDR in the Brunello screen. Experimental FDR is 1-frepl.

Supplementary Fig. 3 Validation of gene hits identified from the genome-scale screens.

a, Histograms showing SOX17 expression from flow cytometry analysis. The black lines represent results from control cells infected with non-targeting gRNA lentiviruses. The red lines represent results from cells infected with targeting gRNA lentiviruses. Each plot shows a representative result from 2 non-targeting gRNAs and 2 different targeting gRNAs. n = 2 independent experiments. b, Flow cytometry quantification of differentiation efficiency based on the percentage SOX17+ DE cells in H1 and HUES8 SOX17GFP/+ reporter line after gRNA targeting (2 gRNAs/gene). Statistical analysis was performed by unpaired two-tailed Student’s t test. P < 0.05 for one of the MLL2 gRNAs (indicated by one asterisk), while results from all other gRNAs were not significantly (P ≥ 0.05) different from the control non-targeting gRNAs. n = 2 independent experiments. Error bars indicate SD. c, RNA-seq gene expression profiles of 24 verified screen hits in log2 (fold change DE/ESC). Positive regulators are labeled in green and negative regulators are labeled in red. Dashed lines indicate the 2-fold change.

Supplementary Fig. 4 Phenotypes of MKK7 and JUN KO hESC lines.

a, CRISPR/Cas9 gene targeting strategy used to generate MKK7 and JUN KO lines. Two homozygous KO cells were picked for further analysis. The blue bars indicate the exons of the gene. JUN is an intronless gene. CRISPR target and PAM sequences are shown in green and purple, respectively. b, Growth curve of WT, MKK7 KO and JUN KO cells cultured in the self-renewing condition (ESC) or DE differentiation condition. For the ESC condition, cell numbers are normalized to D0 (1 day after splitting). For the DE condition, cell numbers are normalized to D0 (1 day after splitting and the day when DE differentiation was initiated). n = 3 independent experiments. c, Flow cytometry quantification of the proliferation marker phospho-histone H3 and the apoptosis marker cleaved caspase-3 from WT, MKK7 KO and JUN KO DE D3 cells. n = 3 independent experiments. d, Immunostaining of ESC markers OCT4, NANOG and SOX2 in WT, MKK7 KO and JUN KO ESCs. Scale bar, 100 μm. e, Immunostaining of ESC markers OCT4, NANOG and SOX2 in WT, MKK7 KO and JUN KO ESCs. Scale bar, 100 μm. Representative images in (d) and (e) are from 2 independent experiments. f, RT-qPCR analysis of DE marker genes in undifferentiated WT, MKK7 KO and JUN KO ESCs. The relative expression level is normalized to the housekeeping gene GAPDH. n = 3 independent experiments. g, RT-qPCR analysis of DE (SOX17, FOXA2) and ESC (OCT4, NANOG, SOX2) marker genes in WT, MKK7 KO and JUN KO DE cells. The relative expression levels were normalized to the housekeeping gene GAPDH. n = 3 independent experiments. Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. Exact P values are shown. NS, not significant (P ≥ 0.05).

Supplementary Fig. 5 Neuroectoderm (NE) differentiation of MKK7 and JUN KO hESC lines.

a, NE differentiation schematic. The day when NE differentiation was initiated is designated as D0. Cells were examined on D4, 6, 8 and 10 of NE differentiation. b, Immunostaining of PAX6, SOX1 and OCT4 on D10 of NE differentiation from WT, MKK7 KO and JUN KO cell lines. Images are from one experiment. c, Representative flow cytometry dot plots of D10 NE cells stained for PAX6 (WT, MKK7 KO and JUN KO cell lines). d, Flow cytometry quantification of differentiation efficiency based on the percentage of PAX6+ NE cells from D4, D6, D8, D10 (WT, MKK7 KO and JUN KO cell lines). n = 3 independent experiments (D6-10), n = 2 independent experiments (D4). Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. NS, not significant (P ≥ 0.05).

Supplementary Fig. 6 Transcriptional profiling of JNKi-treated DE cells.

a, Gene expression analysis by RT-qPCR of DE marker genes in DE cells differentiated from control and JNKi treated condition, n = 3 independent experiments. b, Flow cytometry quantification of differentiation efficiency based on the percentage of CXCR4+SOX17+ DE D3 cells from untreated control and JNKi treated condition, including HUES8 SOX17GFP/+ line, HUES6 hESC line, CV hiPSC, and BJ hiPSC lines. n = 3 independent experiments. Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. Exact P values are shown. c, Representative flow cytometry dot plots of DE D3 cells co-stained for SOX17 and CXCR4 from untreated control and JNKi treated condition, including HUES8 SOX17GFP/+ line, HUES6 hESC line, CV hiPSC, and BJ hiPSC lines. d, Unsupervised hierarchical clustering of the expression values from individual JNKi treated and control DE cells for the 1,000 most variably expressed genes. Each vertical line represents the gene expression profile for each single cell (orange bars label control DE cells; blue bars label JNKi treated DE cells). The ESC and DE signature genes that were shown in Fig. 3d are indicated by the dark grey (DE) and pink (ESC) bars. The color gradient of the heatmap reflects the row-normalized z-score of expression where dark blue indicates the lowest expression value for a particular gene. The gaps in the heatmap serve as a visual separator of the top two branches of the dendrograms for the clustering of both cells and genes, respectively. e, Expression patterns of selected DE genes in individual cells following DE differentiation. Every dot represents a single cell. Their positions are determined by the expression levels for the genes indicated on the x and y axes. The color gradient reflects OCT4 expression.

Supplementary Fig. 7 JNKi treatment improves DE and subsequent endoderm lineage differentiation.

a, Schematic of DE differentiation for endoderm derivative differentiation. JNKi was only added during the first day of differentiation. b, Representative flow cytometry dot plots of DE D3 cells stained for CXCR4 and SOX17-GFP (FITC-gating) from untreated control and JNKi (1 day) treated condition, HUES8 SOX17GFP/+ line differentiated from high (100 ng/ml) and low (20 ng/ml) Activin A. c, Flow cytometry quantification of differentiation efficiency based on the percentage of CXCR4+SOX17-GFP+ DE D3 cells from (b). n = 3 independent experiments. d, Representative Flow cytometry dot plots of DE D3 cells stained for CXCR4 and SOX17-GFP from untreated control and JNKi (1 day) treated condition, including HUES8 (non-reporter), H1, HUES6 hESC lines, CV hiPSC, and BJ hiPSC lines. Quantification is shown in Fig. 3g. e-f. RT-qPCR analysis of pancreatic progenitor (e) and lung progenitor (f) markers gene expression. The relative expression levels were normalized to the housekeeping gene GAPDH, and further normalized to the expression level in hESC. n = 3 independent experiments. Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. Exact P values are shown.

Supplementary Fig. 8 The effects of JNK inhibition on chromatin accessibility and transcription factors binding in ESC-DE lineage transition.

a, Motif analysis at regions of differential SMAD2/3 ChIP-seq binding signals in ESCs and DE cells. Beeswarm plot shows change in SMAD2/3 binding signal ESC vs DE, motifs and gkm-SVM are trained on genomic intervals in the tails of this distribution. b, Heatmaps of TF binding intensities at differentially bound SMAD2/3 sites in ESC and DE D3 stage. c, The Venn diagram shows overlapping OCT4 and JUN binding sites at the ESC stage using the standard peak calling criteria (q = 0.05) (P < 3.89 × 10−224) for OCT4. d, The boxplots show the average ATAC-seq, SMAD2/3, OCT4, H3K27ac (GSM733718), and p300 (GSM803542) ChIP-seq signals over OCT4+JUN+ and OCT4+JUN- regions at the ESC stage. On these and following boxplots, boxes show interquartile range, whiskers show fixed multiples of interquartile range, and solid line shows median. e, Average ChIP-seq signals of OCT4, NANOG, SMAD2/3 and JUN at regions of OCT4+JUNhigh ESC enhancers and OCT4+JUNlow ESC enhancers. f, Boxplots show the normalized read counts of ATAC-seq and SMAD2/3 ChIP-seq of DE D1 Ctrl and JNKi condition in OCT4+SMAD2/3+ ESC sites, GATA6+SMAD2/3+ DE sites (left 2 panels) and in the top SMAD2/3+ESC, top SMAD2/3+ DE sites (right 2 panels). g, Western blotting analysis of protein expression at 15 minutes, 1 hour, 4 hours and 1 day after initiating DE differentiation showed that Activin A treatment induces C-terminal SMAD2 phosphorylation (at Ser465/467) as expected, an effect blocked by SB431542, a selective inhibitor of ACVR1B/ALK4, TGFBR1/ALK5 and ACVR1C/ALK7. JNKi treatment inhibited JUN phosphorylation, but did not change the level of C-terminal SMAD2 phosphorylation (S465/467) and linker SMAD2 phosphorylation (S245/250/255). Conversely SB431542 treatment also did not affect the level of JUN phosphorylation. Uncropped images are shown in Supplementary Fig. 11. Representative images are from two independent experiments. h, Average ATAC-seq signals at regions of decreased (red) or increased (blue) SMAD2/3 binding upon JNKi treatment at DE D1. i, SMAD2/3 binding intensity (number of reads per 300 bp genomic interval) at ESC and DE associated gene enhancers based on SMAD2/3 ChIP-seq analysis of DE D1 control and DE D1 JNKi. j, Venn diagram of GATA6 and SMAD2/3 unique and common binding sites in DE D1 Ctrl and DE D1 JNKi. k, ChIP-seq peaks of increased JUN binding in ESC relative to DE D1 are preferentially located near the top 200 ESC-expressed genes (blue) and farther from the top 200 DE-expressed genes (red) compared to random gene sets (grey). 142 are located within 100 kb of the transcription start site (TSS) of one of the 200 genes with the greatest fold decrease in expression during DE differentiation, which is a significant enrichment compared to the expected overlap of 107.6 ± 11.3 binding sites by chance from sampling ten random sets of 200 genes (P < 0.0012). Error bars indicate SD.

Supplementary Fig. 9 JNK inhibition lowers the threshold of Activin A doses for efficient endoderm differentiation.

a, Representative flow cytometry dot plots of DE D3 cells stained for CXCR4 and SOX17, GATA4 and GATA6 from untreated control and JNKi treated condition (H1 hESC line, 20 ng/ml Activin A). b, Flow cytometry quantification of differentiation efficiency based on the percentage of CXCR4+SOX17+, GATA6+GATA4+ DE D3 cells from (a). n = 3 independent experiments. c, Representative flow cytometry dot plots of DE D3 cells stained for CXCR4 and SOX17, GATA6 and GATA4 (H1 hESC line, WT vs MKK7 and JUN KO, treated with 20 ng/ml Activin A). d, Flow cytometry quantification of differentiation efficiency based on the percentage of CXCR4+SOX17+, GATA6+GATA4+ DE D3 cells from (c). n = 3 independent experiments. Error bars indicate SD. Statistical analysis was performed by unpaired two-tailed Student’s t test. Exact P values are shown. e, Immunostaining of DE markers SOX17 and FOXA2 in WT, MKK7 KO and JUN KO DE cells differentiated from 20 ng/ml Activin A condition. Scale bar, 100 μm. Representative images are from two independent experiments.

Supplementary Fig. 10 Uncropped images for Fig. 2c and Supplementary Fig. 1c.

Protein ladder: Precision Plus Protein Dual Color Standards from Bio-Rad #1610374. DNA ladder: 1 kb Plus DNA Ladder from New England Biolabs # N3232. Boxed areas with red dashed borders were shown in the corresponding figures.

Supplementary Fig. 11 Uncropped images for Fig. 3b and Supplementary Fig. 8f.

Protein ladder: Precision Plus Protein Dual Color Standards from Bio-Rad #1610374. Boxed areas with red dashed borders were shown in the corresponding figures.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Supplementary Notes 1 and 2, Supplementary Tables 1–5

Reporting Summary

Supplementary Data Set 1

Comprehensive analyses of GeCKO and Brunello screens by MAGeCK and Z-score methods, related to Fig. 1d–f and Supplementary Fig. 2

Supplementary Data Set 2

RNA-seq analysis of differentially expressed genes in DE and ESC, related to Supplementary Fig. 3c

Supplementary Data Set 3

RNA-seq analysis of MKK7 KO and WT cells (DE stage and ESC stage), related to Fig. 2f

Supplementary Data Set 4

Summary of motif analysis, related to Figs. 4a, 5f and Supplementary Fig. 8a

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