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:

Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions

Nature Genetics volume 55, pages 1336–1346 (2023)Cite this article

Subjects

Abstract

Comprehensive enhancer discovery is challenging because most enhancers, especially those contributing to complex diseases, have weak effects on gene expression. Our gene regulatory network modeling identified that nonlinear enhancer gene regulation during cell state transitions can be leveraged to improve the sensitivity of enhancer discovery. Using human embryonic stem cell definitive endoderm differentiation as a dynamic transition system, we conducted a mid-transition CRISPRi-based enhancer screen. We discovered a comprehensive set of enhancers for each of the core endoderm-specifying transcription factors. Many enhancers had strong effects mid-transition but weak effects post-transition, consistent with the nonlinear temporal responses to enhancer perturbation predicted by the modeling. Integrating three-dimensional genomic information, we were able to develop a CTCF-loop-constrained Interaction Activity model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer–promoter contact frequency. Our study provides generalizable strategies for sensitive and systematic enhancer discovery in both normal and pathological cell state transitions.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Dynamic gene regulatory network model.
Fig. 2: Dynamic gene regulatory network model predicts temporal sensitivity to enhancer perturbation during cell state transition.
Fig. 3: Systematic identification of core TFs during ESC-DE cell state transition.
Fig. 4: A dynamic network-guided enhancer screen identified core enhancers during hESC-DE cell state transition.
Fig. 5: Validation of identified core enhancers using CRISPRi perturbation.
Fig. 6: Validation of identified core enhancers using CRISPR–Cas9-mediated deletion.
Fig. 7: The CIA model provides improved enhancer prediction.

Data availability

The parental HUES8 hESC line was obtained from Harvard University under a material transfer agreement. Sequencing data are available at GEO under accession GSE213394 (new data from this study) and GSE114102 (published DE-72 h H3K4me1 ChIP–seq data), GSE63525 (published K562 Hi-C data) and GSE72816, GSE177081, GSE177471 (published ChIA–PET data). The Hi-C data are available in the 4D Nucleome Data Portal (https://data.4dnucleome.org/) under accession numbers 4DNESDO2ZYBM, 4DNESQMUTYXH, 4DNESFL8KDMT, 4DNESW8SIXN7, 4DNESW9GVC97, and 4DNESI1DNSGF. Mass spectrometry data are available in the PRIDE database under ProteomeXchange accession PXD043070. Source data are provided with this paper.

Code availability

Publicly available software and packages were used throughout this study according to each developer’s instructions. The MATLAB codes are provided in the Supplementary Code.

References

  1. Luo, Y. et al. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 48, D882–D889 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Korkmaz, G. et al. Functional genetic screens for enhancer elements in the human genome using CRISPR–Cas9. Nat. Biotechnol. 34, 192–198 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Fulco, C. P. et al. Systematic mapping of functional enhancer-promoter connections with CRISPR interference. Science 354, 769–773 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Fulco, C. P. et al. Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).

    Article  CAS  PubMed  Google Scholar 

  5. Sanjana, N. E. et al. High-resolution interrogation of functional elements in the noncoding genome. Science 353, 1545–1549 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Klann, T. S. et al. CRISPR–Cas9 epigenome editing enables high-throughput screening for functional regulatory elements in the human genome. Nat. Biotechnol. 35, 561–568 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Diao, Y. et al. A new class of temporarily phenotypic enhancers identified by CRISPR/Cas9-mediated genetic screening. Genome Res. 26, 397–405 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Diao, Y. et al. A tiling-deletion-based genetic screen for cis-regulatory element identification in mammalian cells. Nat. Methods 14, 629–635 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Reilly, S. K. et al. Direct characterization of cis-regulatory elements and functional dissection of complex genetic associations using HCR-FlowFISH. Nat. Genet. 53, 1166–1176 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gasperini, M. et al. CRISPR/Cas9-mediated scanning for regulatory elements required for HPRT1 expression via thousands of large, programmed genomic deletions. Am. J. Hum. Genet. 101, 192–205 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Huang, J. et al. Dissecting super-enhancer hierarchy based on chromatin interactions. Nat. Commun. 9, 943 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Thakore, P. I. et al. Highly specific epigenome editing by CRISPR–Cas9 repressors for silencing of distal regulatory elements. Nat. Methods 12, 1143–1149 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ulirsch, J. C. et al. Systematic functional dissection of common genetic variation affecting red blood cell traits. Cell 165, 1530–1545 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wakabayashi, A. et al. Insight into GATA1 transcriptional activity through interrogation of cis elements disrupted in human erythroid disorders. Proc. Natl Acad. Sci. USA 113, 4434–4439 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Xie, S., Duan, J., Li, B., Zhou, P. & Hon, G. C. Multiplexed engineering and analysis of combinatorial enhancer activity in single cells. Mol. Cell 66, 285–299 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Hong, J. W., Hendrix, D. A. & Levine, M. S. Shadow enhancers as a source of evolutionary novelty. Science 321, 1314 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Barolo, S. Shadow enhancers: frequently asked questions about distributed cis-regulatory information and enhancer redundancy. Bioessays 34, 135–141 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. Cannavò, E. et al. Shadow enhancers are pervasive features of developmental regulatory networks. Curr. Biol. 26, 38–51 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Perry, M. W., Boettiger, A. N., Bothma, J. P. & Levine, M. Shadow enhancers foster robustness of Drosophila gastrulation. Curr. Biol. 20, 1562–1567 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Frankel, N. et al. Phenotypic robustness conferred by apparently redundant transcriptional enhancers. Nature 466, 490–493 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Osterwalder, M. et al. Enhancer redundancy provides phenotypic robustness in mammalian development. Nature 554, 239–243 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dailey, L. High throughput technologies for the functional discovery of mammalian enhancers: new approaches for understanding transcriptional regulatory network dynamics. Genomics 106, 151–158 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. White, M. A. Understanding how cis-regulatory function is encoded in DNA sequence using massively parallel reporter assays and designed sequences. Genomics 106, 165–170 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Beer, M. A., Shigaki, D. & Huangfu, D. Enhancer predictions and genome-wide regulatory circuits. Annu. Rev. Genomics Hum. Genet. 21, 37–54 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Li, Q. V. et al. Genome-scale screens identify JNK-JUN signaling as a barrier for pluripotency exit and endoderm differentiation. Nat. Genet. 51, 999–1010 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Whalen, S., Truty, R. M. & Pollard, K. S. Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat. Genet. 48, 488–496 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cao, Q. et al. Reconstruction of enhancer-target networks in 935 samples of human primary cells, tissues and cell lines. Nat. Genet. 49, 1428–1436 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Xi, W. & Beer, M. A. Local epigenomic state cannot discriminate interacting and non-interacting enhancer-promoter pairs with high accuracy. PLoS Comput. Biol. 14, e1006625 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Maston, G. A., Landt, S. G., Snyder, M. & Green, M. R. Characterization of enhancer function from genome-wide analyses. Annu. Rev. Genomics Hum. Genet. 13, 29–57 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Davidson, E. H. The Regulatory Genome: Gene Regulatory Networks in Development and Evolution (Academic Press, 2006).

  31. 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).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  33. Cai, X. Exact stochastic simulation of coupled chemical reactions with delays. J. Chem. Phys. 126, 124108 (2007).

    Article  PubMed  Google Scholar 

  34. Walczak, A. M., Mugler, A. & Wiggins, C. H. Analytic methods for modeling stochastic regulatory networks. Methods Mol. Biol. 880, 273–322 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  36. Boyer, L. A. et al. Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122, 947–956 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Gasperini, M., Tome, J. M. & Shendure, J. Towards a comprehensive catalogue of validated and target-linked human enhancers. Nat. Rev. Genet. 21, 292–310 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  39. Xi, W. & Beer, M. A. Loop competition and extrusion model predicts CTCF interaction specificity. Nat. Commun. 12, 1046 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Nasser, J. et al. Genome-wide enhancer maps link risk variants to disease genes. Nature 593, 238–243 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Batut, P. J. et al. Genome organization controls transcriptional dynamics during development. Science 375, 566–570 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Juan, A. H. & Ruddle, F. H. Enhancer timing of Hox gene expression: deletion of the endogenous Hoxc8 early enhancer. Development 130, 4823–4834 (2003).

    Article  CAS  PubMed  Google Scholar 

  43. Rodriguez-Carballo, E. et al. Chromatin topology and the timing of enhancer function at the HoxD locus. Proc. Natl Acad. Sci. USA 117, 31231–31241 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Xiao, J. Y., Hafner, A. & Boettiger, A. N. How subtle changes in 3D structure can create large changes in transcription. eLife 10, e64320 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kim-Hellmuth, S. et al. Cell type-specific genetic regulation of gene expression across human tissues. Science 369, eaaz8528 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Marderstein, A. R. et al. Demographic and genetic factors influence the abundance of infiltrating immune cells in human tissues. Nat. Commun. 11, 2213 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Donovan, M. K. R., D’Antonio-Chronowska, A., D’Antonio, M. & Frazer, K. A. Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants. Nat. Commun. 11, 955 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zuin, J. et al. Nonlinear control of transcription through enhancer–promoter interactions. Nature 604, 571–577 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dowen, J. M. et al. Control of cell identity genes occurs in insulated neighborhoods in mammalian chromosomes. Cell 159, 374–387 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Anderson, E., Devenney, P. S., Hill, R. E. & Lettice, L. A. Mapping the Shh long-range regulatory domain. Development 141, 3934–3943 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Symmons, O. et al. The Shh topological domain facilitates the action of remote enhancers by reducing the effects of genomic distances. Dev. Cell 39, 529–543 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wang, X. et al. Genome-wide detection of enhancer-hijacking events from chromatin interaction data in rearranged genomes. Nat. Methods 18, 661–668 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yan, J. & Huangfu, D. Epigenome rewiring in human pluripotent stem cells. Trends Cell Biol. 32, 259–271 (2022).

    Article  CAS  PubMed  Google Scholar 

  54. Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 162, 687–688 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  56. Yang, D. et al. CRISPR screening uncovers a central requirement for HHEX in pancreatic lineage commitment and plasticity restriction. Nat. Cell Biol. 24, 1064–1076 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Xiong, W. & Ferrell, J. E. A positive-feedback-based bistable ‘memory module’ that governs a cell fate decision. Nature 426, 460–465 (2003).

    Article  CAS  PubMed  Google Scholar 

  59. Wang, L. et al. Bistable switches control memory and plasticity in cellular differentiation. Proc. Natl Acad. Sci. USA 106, 6638–6643 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Dixon, G. et al. QSER1 protects DNA methylation valleys from de novo methylation. Science 372, eabd0875 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Morgens, D. W. et al. Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nat. Commun. 8, 15178 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Concordet, J. P. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, W242–W245 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Tang, Z. et al. CTCF-mediated human 3D genome architecture reveals chromatin topology for transcription. Cell 163, 1611–1627 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Wu, H. J. et al. Topological isolation of developmental regulators in mammalian genomes. Nat. Commun. 12, 4897 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We acknowledge the assistance from the following MSKCC Cores: Antibody & Bioresource, Flow Cytometry, Gene Editing & Screening, Integrated Genomics Operation and Molecular Cytogenetics. We thank R. Garippa, H. Liu and S. Mehta for assistance with CRISPR library generation and HiSeq for quantifying gRNA abundance after CRISPRi screen; Y. Furuta, J. Liu, Y. Lan and C. Schwarz for assisting with additional experiments not included in the manuscript and C.S. Leslie, L. Studer, A. Ventura, A.-K. Hadjantonakis, T. Evans and members of D.H.’s laboratory for insightful advice. We acknowledge the funding resources from the following: National Institutes of Health grant U01HG012051 (to D.H., M.B. and T.V.), National Institutes of Health grant U01DK128852 (to D.H. and E.A.), National Institutes of Health grant R01HG012367 (to M.B.), National Institutes of Health grant U01HG009380 (to M.B.), National Institutes of Health grant R56HG012110 (to M.B.), National Institutes of Health grant R01DK096239 (to D.H.), National Institutes of Health grant 1S10OD030286-01 (to S.S.), National Institutes of Health grant 1S10OD030286-01 (to S.S.), Starr Tri-I Stem Cell Initiative #2019-001 (to D.H. and E.A.), American Federation for Aging Research Sagol Network GerOmics award (to S.S.), Deerfield Xseed award (to S.S.), National Institutes of Health MSKCC Cancer Center Support Grant P30CA008748, Einstein Cancer Center grant P30CA013330 (to S.S.), NYSTEM postdoctoral fellowship (to D.Y.) from the MSKCC Center for Stem Cell Biology DOH01-TRAIN3-2015-2016-00006 and National Institutes of Health T32 training grant T32GM008539 (to B.P.R.).

Author information

Authors and Affiliations

  1. Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA

    Renhe Luo, Jielin Yan, Hyein S. Cho, Bess P. Rosen, Julian Pulecio, Dapeng Yang, Rachel A. Glenn, Tingxu Chen, Qing V. Li, Thomas Vierbuchen & Danwei Huangfu

  2. Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA

    Renhe Luo, Jielin Yan, Tingxu Chen & Qing V. Li

  3. Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

    Jin Woo Oh, Wang Xi, Dustin Shigaki & Michael A. Beer

  4. Computational & Systems Biology Program, Sloan Kettering Institute, New York City, NY, USA

    Wilfred Wong

  5. Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA

    Wilfred Wong, Dylan Murphy, Bess P. Rosen & Rachel A. Glenn

  6. Department of Medicine, Weill Cornell Medicine, New York City, NY, USA

    Dylan Murphy & Effie Apostolou

  7. Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA

    Ronald Cutler & Simone Sidoli

Authors
  1. Renhe Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jielin Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jin Woo Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wang Xi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dustin Shigaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wilfred Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hyein S. Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dylan Murphy
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ronald Cutler
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bess P. Rosen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Julian Pulecio
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dapeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Rachel A. Glenn
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Tingxu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Qing V. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Thomas Vierbuchen
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Simone Sidoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Effie Apostolou
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Danwei Huangfu
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Michael A. Beer
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.L., D.H. and M.A.B. devised experiments and interpreted results. R.L. performed most experiments and analyzed the results. M.A.B. developed the mathematical models and performed computational data analysis, with contributions from J.W.O., W.X. and D.S. J.Y., B.P.R., D.Y. and Q.V.L. assisted with ChIP–seq. S.S. supervised and J.Y. and R.C. assisted with ChIP–MS. T.V. and D.H. supervised and R.A.G. and T.C. assisted with validation. E.A. and D.H. supervised and J.P., D.M. and W.W. assisted with Hi-C and subsequent data analysis. H.S.C. performed gene expression correlation analysis. R.L., D.H. and M.A.B. wrote the manuscript; all other authors provided editorial advice.

Corresponding authors

Correspondence to Danwei Huangfu or Michael A. Beer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Kyle Loh 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 Supporting data for the dynamic GRN model.

a, The detailed schematic of core circuit in the gene regulatory network (GRN). The core transcription factors (TFs) cooperatively auto-regulate each other by binding to core enhancers and co-regulate downstream peripheral genes by binding to peripheral enhancers. b, The ranking plot of principle component 1 (PC1) weight of all TFs in PCA analysis from scRNA-seq data during human embryonic stem cell to definitive endoderm (hESC-DE) transition. c, The principle component analysis (PCA) plots showing selective TFs from the PCA component 1 (Extended Data Fig. 1b) of single-cell RNA-seq (scRNA-seq) sampled every 12 hours during hESC-DE transition. d, The schematic of core circuit establishment during cell state transition, similar to Moris et al.67. The transition of a cell from one steady state to another is accompanied by the deconstruction of the original core circuit (A, B, C) and the establishment of core circuit of the new state (X, Y, Z). e, Stochastic Gillespie simulations of the dynamic GRN network model. The green, cyan, purple, yellow and orange lines represent 100%, 70%, 30%, 10% and 0% of original total enhancer strength respectively.

Extended Data Fig. 2 Core TFs identification and characterization during hESC-DE transition.

a, Flow cytometry analysis showing the gating strategy (left), differentiation efficiency at DE-72h measured by DE markers SOX17 and CXCR4 (middle) or transition efficiency every 12 h measured by SOX17 (right). b, MAGeCK robust ranking aggregation (RRA) scores for negative hits in two genome-scale DE screens from Li et al.25. JUN is the only identified TF among the negative hits. c, Motif z score of ATAC-seq by gkm-SVM at each time point during hESC-DE transition. d, Feature violin plots from scRNA-seq data showing core TFs expression changing during hESC-DE transition at single cell resolution. e, Volcano plots showing protein-protein interactions identified by ChIP-MS using EOMES as the bait at DE-24h, GATA6 and SOX17 as baits at DE-48h. Blue dots represent the significantly enriched proteins with log2FC > 2 and -log10(P-value) > 2. Selective TFs enriched in ESC and endoderm GO terms are labeled by triangles.

Source data

Extended Data Fig. 3 Supporting data for the screen design and gRNA enrichment analysis.

a to d, Statistics of putative enhancers selection and gRNAs design. The number of putative enhancers selected for each core TFs (a), the size of putative enhancers (b), the total number of gRNAs targeted on putative enhancers of each core TF (c), the number of gRNAs targeted on each putative enhancer (d). e, The gRNA z score distribution at representative enhancers showing gRNAs targeting the same enhancer have similar perturbation effect. f, Box plots showing the gRNA z score distribution in all putative enhancers of EOMES, MIXL1, GATA6 and SOX17 loci. All box plots follow the following format: center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers.

Extended Data Fig. 4 idCas9-KRAB SOX17GFP/+ hESC line generation using cassette switching.

a, The schematics of idCas9-KRAB SOX17GFP/+ hESC line generation using cassette switching. gRNAs targeting the puromycin selection cassette and the 5’ sequence outside TRE are designed for inducing double-strand break for homology repair. b, Karyotyping results of the idCas9-KRAB SOX17GFP/+ hESC line. c, RT-qPCR results showing the inducible expression of dCas9-KRAB with doxycycline treatment. n = 3 biologically independent experiments. Error bars indicate mean ± SD. Statistical analysis was performed by two-tailed unpaired student t-test. d, Flow cytometry results showing the inducible expression of dCas9-KRAB with doxycycline treatment.

Source data

Extended Data Fig. 5 Supporting data for validation of core enhancers.

a, b, RT-qPCR showing the expression of the cognate genes decreases by enhancer perturbations at DE-36h (a) but mostly restored at DE-72h (b). n = 3 biologically independent experiments. Error bars indicate mean ± SD. Statistical analysis was performed by two-tailed unpaired student t-test. n.s.: not significant. c, Illustration of the enhancer deletion experiments that resulted in the GATA6e + 9 deletion (del), GATA6e + 12 del and GATA6e + 9/e + 12 double del hESC lines. d, Statistics of SOX17-GFP/CXCR4 double positive cells at DE-72h in WT, GATA6e + 9 del, GATA6e + 12 del, GATA6e + 9/e + 12 double del cells, as well as cells with non-targeting control, GATA6e + 9 perturbation, GATA6e + 12 perturbation and GATA6e + 9/GATA6e + 12 dual-perturbation. n = 3 biologically independent replicates. Error bars indicate mean ± SD. Statistical analysis was performed by two-tailed unpaired multiple comparison test with Dunnett correction. n.s.: not significant. e, Flow plots showing SOX17/GATA6 expression at DE-36h, DE-72h and SOX17-GFP/CXCR4 expression at DE-72h of WT, GATA6e + 9 del, GATA6e + 12 del, GATA6e + 9/e + 12 double del.

Source data

Extended Data Fig. 6 Epigenetic features of DE core enhancers.

Relevant ATAC-seq and ChIP-seq tracks of 4 DE core TF loci. Yellow boxes highlight the DE core TFs (EOMES, GATA6 and SOX17) bind to DE core enhancers. Genomic coordinates from GRCh38 (human hg38) for each gene are labeled. kbp, kilobase pair.

Extended Data Fig. 7 Epigenetic features of ESC and signaling core TF loci.

Relevant ATAC-seq and ChIP-seq tracks of ESC and signaling core TF loci. Genomic coordinates from GRCh38 (human hg38) for each gene are labeled. kbp, kilobase pair.

Extended Data Fig. 8 Supporting data for enhancer prediction using CIA model.

a, Hi-C-based and CTCF loop-based chromatin conformation analysis at the GATA6, MIXL1 and EOMES loci. b, Precision-recall plot comparing the performance for prediction of enhancer hits from the screen using different Hi-C datasets. c, Precision-recall plot comparing the performance for prediction of enhancer hits from the screen using CIA model with additional H3K4me1 chromatin feature. d, Bar plot comparing the area under precision recall curve (AUPRC) and correlation scores between logistic regression of chromatin feature combination and \({\rm{{Activity}=\sqrt{{ATAC}* H3K27{ac}}}}\) in CTCF loop-constrained Interaction Activity (CIA) model. e, A scatter plot showing the P(in loop) can classify hits (green) and non-hits (gray) more clearly than Hi-C-based enhancer-promoter contact frequency. The size of each dot represents the log2FC of each enhancer from the screen.

Extended Data Fig. 9 The CIA model predicts active enhancers in different scenarios (K562 Reilly).

a, gRNA enrichment analysis identified 36 hits from the HCR-FF (Hybridization Chain Reaction Fluorescent In-Situ Hybridization coupled with Flow Cytometry) screen in K562 cells from Reilly et al.9 b, The scatter plot showing the P(in loop) can classify hits (green) and non-hits (gray) in K562 HCR-FF screen more clearly than Hi-C-based enhancer-promoter contact frequency. The size of each dot represents the log2FC of each enhancer from the screen. c, Bar plot comparing the AUPRC and correlation scores between Hi-C-based enhancer prediction with CIA model and ABC model using K562 HCR-FF screen results. d, Precision-recall plot comparing the performance for prediction of enhancer hits from the K562 HCR-FF screen using CTCF loop-based model and Hi-C-based model. e, The scatter plot showing the combinatory criteria of P(in loop), H3K27ac and ATAC can clearly separate the hits (green) and non-hits (blue and gray) from the K562 HCR-FF screen. P(in loop) > 0.5 is used to highlight enhancers and targeting promoters in the same CTCF loop (green and blue). The solid line represents the same threshold criterion of \({\rm{{Activity}=\sqrt{{ATAC}* H3K27{ac}}}}\) in Fig. 7e. The size of each dot represents the log2FC of each enhancer from the K562 HCR-FF screen.

Extended Data Fig. 10 The CIA model predicts active enhancers in different scenarios (K562 Nasser).

a, 69 identified hits in the K562 cells from Nasser et al are plotted40. b, The scatter plot showing the P(in loop) can classify hits (green) and non-hits (gray) in K562 Nasser more clearly than Hi-C-based enhancer-promoter contact frequency. The size of each dot represents the effect size of each enhancer from Nasser et al. c, Bar plot comparing the AUPRC and correlation scores between Hi-C-based enhancer prediction with CIA model and ABC model using K562 Nasser results. d, Precision-recall plot comparing the performance for prediction of enhancer hits from the K562 Nasser using CTCF loop-based model and Hi-C-based model. e, The scatter plot showing the combinatory criteria of P(in loop), H3K27ac and ATAC can clearly separate the hits (green) and non-hits (blue and gray) from the K562 Nasser. P(in loop) > 0.5 is used to highlight enhancers and targeting promoters in the same CTCF loop (green and blue). The solid line represents the same threshold criterion of \({\rm{{Activity}=\sqrt{{ATAC}* H3K27{ac}}}}\) in Fig. 7e. The size of each dot represents the effect size of each enhancer from the K562 Nasser.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Table 1: EOMES, GATA6 and SOX17 ChIP–MS data. Supplementary Table 2: Core enhancer perturbation screen gRNA library sequences and gRNA enrichment analysis. Supplementary Table 3: Core enhancer perturbation screen gRNA enrichment analysis for regions. Supplementary Table 4: Data of core enhancer screening for regions surrounding core DE TFs. Supplementary Table 5: Data of K562 Reilly enhancer screen. Supplementary Table 6: Data of K562 Nasser enhancer screen. Supplementary Table 7: gRNA for cell line generation. Supplementary Table 8: Primer sequences for cell line genotyping, RT–qPCR and gRNA enrichment analysis. Supplementary Table 9: Antibody for flow cytometry, ChIP–seq and ChIP–MS. Supplementary Table 10: gRNA sequences for hit validation.

Supplementary Code

Supplementary Code of MATLAB_program.

Source data

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, R., Yan, J., Oh, J.W. et al. Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions. Nat Genet 55, 1336–1346 (2023). https://doi.org/10.1038/s41588-023-01450-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-023-01450-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing