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:

Augmenting and directing long-range CRISPR-mediated activation in human cells

An Author Correction to this article was published on 27 June 2022

This article has been updated

Abstract

Epigenetic editing is an emerging technology that uses artificial transcription factors (aTFs) to regulate expression of a target gene. Although human genes can be robustly upregulated by targeting aTFs to promoters, the activation induced by directing aTFs to distal transcriptional enhancers is substantially less robust and consistent. Here we show that long-range activation using CRISPR-based aTFs in human cells can be made more efficient and reliable by concurrently targeting an aTF to the target gene promoter. We used this strategy to direct target gene choice for enhancers capable of regulating more than one promoter and to achieve allele-selective activation of human genes by targeting aTFs to single-nucleotide polymorphisms embedded in distally located sequences. Our results broaden the potential applications of the epigenetic editing toolbox for research and therapeutics.

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

Access options

Buy this article

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

Fig. 1: Heterotopic activation of enhancer sequences by dCas9-based aTFs in multiple human cell lines.
Fig. 2: Various levels of heterotopic enhancer activation using aTFs with different architectures and/or harboring other activation domains.
Fig. 3: Directing heterotopic enhancer activities to a specific promoter in the human β-globin locus using dCas9-based aTFs.
Fig. 4: Inducing allele-selective gene upregulation in HEK293 cells using heterotopic enhancer activation.

Similar content being viewed by others

Data availability

Datasets from amplicon sequencing have been deposited with the NCBI Sequence Read Archive (PRJNA578485). Datasets from ChIP–seq, RNA-seq and ATAC–seq experiments have been deposited with the Gene Expression Omnibus repository under accession GSE139190. The GO database used in this study can be downloaded from https://bioportal.bioontology.org/ontologies/GO/. Source data are provided with this paper.

Change history

References

  1. Pickar-Oliver, A. & Gersbach, C. A. The next generation of CRISPR–Cas technologies and applications. Nat. Rev. Mol. Cell Biol. 20, 490–507 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Thakore, P. I., Black, J. B., Hilton, I. B. & Gersbach, C. A. Editing the epigenome: technologies for programmable transcription and epigenetic modulation. Nat. Methods 13, 127–137 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wang, H., La Russa, M. & Qi, L. S. CRISPR–Cas9 in genome editing and beyond. Annu. Rev. Biochem. 85, 227–264 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ernst, J. et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 473, 43–49 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 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 

  7. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gao, X. et al. Comparison of TALE designer transcription factors and the CRISPR–dCas9 in regulation of gene expression by targeting enhancers. Nucleic Acids Res. 42, e155 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hilton, I. B. et al. Epigenome editing by a CRISPR–Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33, 510–517 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kuscu, C. et al. Temporal and spatial epigenome editing allows precise gene regulation in mammalian cells. J. Mol. Biol. 431, 111–121 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Li, K. et al. Interrogation of enhancer function by enhancer-targeting CRISPR epigenetic editing. Nat. Commun. 11, 485 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  12. 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 

  13. Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Simeonov, D. R. et al. Discovery of stimulation-responsive immune enhancers with CRISPR activation. Nature 549, 111–115 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tak, Y. E. et al. Inducible and multiplex gene regulation using CRISPR–Cpf1-based transcription factors. Nat. Methods 14, 1163–1166 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Laguna, T. et al. New insights on the transcriptional regulation of CD69 gene through a potent enhancer located in the conserved noncoding sequence 2. Mol. Immunol. 66, 171–179 (2015).

    Article  CAS  PubMed  Google Scholar 

  17. Chen, J. C. J. & Goldhamer, D. J. The core enhancer is essential for proper timing of MyoD activation in limb buds and branchial arches. Dev. Biol. 265, 502–512 (2004).

    Article  CAS  PubMed  Google Scholar 

  18. Wienert, B., Martyn, G. E., Funnell, A. P. W., Quinlan, K. G. R. & Crossley, M. Wake-up sleepy gene: reactivating fetal globin for β-hemoglobinopathies. Trends Genet. 34, 927–940 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Diepstraten, S. T. & Hart, A. H. Modelling human haemoglobin switching. Blood Rev. 33, 11–23 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Sankaran, V. G. & Orkin, S. H. The switch from fetal to adult hemoglobin. Cold Spring Harb. Perspect. Med. 3, a011643 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sankaran, V. G. et al. Human fetal hemoglobin expression is regulated by the developmental stage-specific repressor BCL11A. Science 322, 1839–1842 (2008).

    Article  CAS  PubMed  Google Scholar 

  22. Li, Q., Harju, S. & Peterson, K. R. Locus control regions: coming of age at a decade plus. Trends Genet. 15, 403–408 (1999).

    Article  PubMed  Google Scholar 

  23. Zannis, V. I., Kan, H. Y., Kritis, A., Zanni, E. E. & Kardassis, D. Transcriptional regulatory mechanisms of the human apolipoprotein genes in vitro and in vivo. Curr. Opin. Lipidol. 12, 181–207 (2001).

    Article  CAS  PubMed  Google Scholar 

  24. Cavalli, M. et al. Allele-specific transcription factor binding to common and rare variants associated with disease and gene expression. Hum. Genet. 135, 485–497 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Spisák, S. et al. CAUSEL: an epigenome- and genome-editing pipeline for establishing function of noncoding GWAS variants. Nat. Med. 21, 1357–1363 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bailey, S. D. et al. ZNF143 provides sequence specificity to secure chromatin interactions at gene promoters. Nat. Commun. 2, 6186 (2015).

    Article  PubMed  Google Scholar 

  27. Tapscott, S. J., Lassar, A. B. & Weintraub, H. A novel myoblast enhancer element mediates MyoD transcription. Mol. Cell. Biol. 12, 4994–5003 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Jost, M. et al. Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs. Nat. Biotechnol. 38, 355–364 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. ENCODE Project Consortium. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    Article  Google Scholar 

  31. Zhou, D., Pawlik, K. M., Ren, J., Sun, C.-W. & Townes, T. M. Differential binding of erythroid Krupple-like factor to embryonic/fetal globin gene promoters during development. J. Biol. Chem. 281, 16052–16057 (2006).

    Article  CAS  PubMed  Google Scholar 

  32. Liu, N. et al. Direct promoter repression by BCL11A controls the fetal to adult hemoglobin switch. Cell 173, 430–442 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Liu, N. et al. Transcription factor competition at the γ-globin promoters controls hemoglobin switching. Nat. Genet. 53, 511–520 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cooper, D. N., Krawczak, M., Polychronakos, C., Tyler-Smith, C. & Kehrer-Sawatzki, H. Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease. Hum. Genet. 132, 1077–1130 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Veitia, R. A., Caburet, S. & Birchler, J. A. Mechanisms of Mendelian dominance. Clin. Genet. 93, 419–428 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Matharu, N. et al. CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency. Science 363, eaau0629 (2019).

  38. Dang, V. T., Kassahn, K. S., Marcos, A. E. & Ragan, M. A. Identification of human haploinsufficient genes and their genomic proximity to segmental duplications. Eur. J. Hum. Genet. 16, 1350–1357 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Inoue, K. & Fry, E. A. Haploinsufficient tumor suppressor genes. Adv. Med. Biol. 118, 83–122 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. Pochampally, R. R., Horwitz, E. M., DiGirolamo, C. M., Stokes, D. S. & Prockop, D. J. Correction of a mineralization defect by overexpression of a wild-type cDNA for COL1A1 in marrow stromal cells (MSCs) from a patient with osteogenesis imperfecta: a strategy for rescuing mutations that produce dominant-negative protein defects. Gene Ther. 12, 1119–1125 (2005).

    Article  CAS  PubMed  Google Scholar 

  41. Liang, J. R., Lingeman, E., Ahmed, S. & Corn, J. E. Atlastins remodel the endoplasmic reticulum for selective autophagy. J. Cell Biol. 217, 3354–3367 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bernstein, B. E. et al. Genomic maps and comparative analysis of histone modifications in human and mouse. Cell 120, 169–181 (2005).

    Article  CAS  PubMed  Google Scholar 

  43. Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Thorvaldsdóttir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer: high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).

    Article  PubMed  Google Scholar 

  46. 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 

  47. Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Mi, H. et al. Protocol update for large-scale genome and gene function analysis with the PANTHER classification system (v14.0). Nat. Protoc. 14, 703–721 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. 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 

  51. 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 

  52. Ktistaki, E., Lacorte, J.-M., Katrakili, N., Zannis, V. I. & Talianidis, I. Transcriptional regulation of the apolipoprotein A-IV gene involves synergism between a proximal orphan receptor response element and a distant enhancer located in the upstream promoter region of the apolipoprotein C-III gene. Nucleic Acids Res. 22, 4689–4696 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Clement, K. et al. CRISPResso2 provides accurate and rapid genome editing sequence analysis. Nat. Biotechnol. 37, 224–226 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Li, B., Kadura, I., Fu, D.-J. & Watson, D. E. Genotyping with TaqMAMA. Genomics 83, 311–320 (2004).

    Article  CAS  PubMed  Google Scholar 

  55. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Wang, Y. et al. SPIN reveals genome-wide landscape of nuclear compartmentalization. Genome Biol. 22, 36 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

J.K.J. was supported by grants from the National Institutes of Health (R35 GM118158, R01 CA211707, RM1 HG009490 and R01 CA204954), a St. Jude Children’s Research Hospital Collaborative Research Consortium award, a Massachusetts General Hospital (MGH) Collaborative Center for X-Linked Dystonia-Parkinsonism grant, and the Desmond and Ann Heathwood MGH Research Scholar Award. L.P. was supported by grants from the National Institute of Health (R00 HG008399 and R35 HG010717). We thank B. Kleinstiver (MGH) for providing the BPK1179, BPK880, BPK617 and BPK1160 plasmids. We thank M. Freedman, J. Seo and C. Lareau for assistance with important pilot experiments. We thank P. Farnham, M. Rivera and L. P. Pottenplackel for comments on the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

Y.E.T. and J.K.J. conceived of and designed experiments. Y.E.T., J.E.H., N.T.P. and H.T.S. performed experiments. S.I. designed and made the computational pipeline for RNA-seq, ChIP–seq and ATAC–seq. L.P. and Q.Y. analyzed SNP density at regulatory elements. M.J.A. and L.S.Z. analyzed Hi-C data and performed SPIN analysis. Y.E.T. and J.K.J. wrote the manuscript with input from all the authors.

Corresponding author

Correspondence to J. Keith Joung.

Ethics declarations

Competing interests

J.K.J. has financial interests in Beam Therapeutics, Chroma Medicine (formerly YKY, Inc.), Editas Medicine, Excelsior Genomics, Pairwise Plants, Poseida Therapeutics, SeQure Dx, Transposagen Biopharmaceuticals and Verve Therapeutics (formerly Endcadia). L.P. has financial interests in Excelsior Genomics, Edilytics and SeQure Dx. M.J.A. has financial interests in Excelsior Genomics and SeQure Dx. The interests of J.K.J., L.P. and M.J.A. were reviewed and are managed by MGH and Partners HealthCare in accordance with their conflict-of-interest policies. S.I. is an employee of Verve Therapeutics. Y.E.T. and J.K.J. are inventors on patent applications that cover epigenetic editing technologies. All other authors have no competing interests.

Additional information

Peer review information Nature Methods thanks Fyodor Urnov and the other, anonymous, reviewers for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Chromatin status at IL2RA, CD69, and MYOD1 determined by ATAC–seq and H3K27Ac ChIP-seq.

a, IL2RA promoter was closed and inactive in all cell types, IL2RA enhancer regions were closed and inactive in HEK293 and K562 cells, but open and active in U2OS and HepG2 cells. E1, E2, E3, E4: IL2RA enhancer gRNA target sites, P: IL2RA promoter gRNA target site. The RBM17 locus which was open (transposase accessible chromatin) and active (enriched in H3K27Ac marks) in all cell types is shown for comparison. b, CD69 promoter was closed and inactive in all cell types, CD69 enhancer regions were closed in all cell types. E1, E2: CD69 enhancer gRNA target sites, P: CD69 promoter gRNA target site. c, Chromatin at MYOD1 promoter was open in U2OS and HEK293 cells but not in HepG2 and K562 cells. E1, E2, E3, E4: MYOD1 enhancer gRNA target sites, P: MYOD1 promoter gRNA target site.

Extended Data Fig. 2 Chromatin status at the β-globin locus determined by ATAC-seq and H3K27Ac ChIP-seq.

All promoters at the β-globin locus showed closed and inactive chromatin states in all cell types. HS2 enhancer region showed closed and inactive chromatin features in HEK293 cells, but open and active chromatin features U2OS and HepG2 cells. E: HS2 enhancer gRNA target site, PE: HBE1 promoter gRNA target site, PG: HBG1/2 promoter gRNA target site, PB: HBB promoter gRNA target site.

Extended Data Fig. 3 Total activation of APOA4 and APOC3 and orthogonal confirmation of allele-selective expression via allele-specific RT-qPCR.

a-b, Total expression of APOA4 and APOC3 in HEK293 cells by bi-partite p65 aTF targeting the promoter (P) and various sites on the enhancers including SNP regions (E1 to E6) and non-SNP region (E0) determined by RT-qPCR, normalized to HPRT1 levels, calculated relative to sample with non-targeting gRNA (None). Open circles indicate biological replicates (n = 3), bars the mean of replicates and error bars the s.e.m. c, Schematic of the location of RT-qPCR primers used for APOC3 allele-selective expression. Allele-specific primers detecting a SNP in APOC3 exon 3 have a common forward primer (PF) which spans the exon 2 and exon 3 junction, and two different reverse primers which are specific for allele 1 (T at rs4520, PR_1) or for allele 2 (C at rs4520, PR_2) in exon 3, with a ‘T’ mismatch in the penultimate base at the 3’ for both primers. d, Allele-selective expression of APOC3 in HEK293 cells by bi-partite p65 aTF targeting the promoter (P) and various sites on the enhancers including SNP regions (E1 to E6) and non-SNP region (E0) determined by RT-qPCR using the primers described in c, normalized to HPRT1 levels, calculated relative to sample with non-targeting gRNA. Open circles indicate biological replicates (n = 3), bars the mean of replicates and error bars the s.e.m. The apparent difference in allele-specific expression levels when compared to total expression in b is potentially due to the amplification of a smaller fragment of cDNA in the allele-specific reaction. e, Validation of the specificity of allele-specific RT-qPCR primers used in d, with U2OS cells in which the variant T nucleotide is absent at rs4520 (only the C nucleotide is present at the same position). APOC3 expression was measured by RT-qPCR using the allele-specific primers used in d, in U2OS cells co-expressing the bi-partite p65 aTF and gRNAs targeting the promoter or non-SNP region of the enhancer (E0). Open circles indicate biological replicates (n = 3), bars the mean of replicates and error bars the s.e.m.

Source data

Extended Data Fig. 4 Haplotype of potential APOA4 and APOC3 enhancer regions and allele ratios of target SNPs.

a, The potential enhancer region was identified by its open and active chromatin features which are similar to the known enhancer, based on the DNase-seq and H3K27Ac data (UCSC genome browser) from HepG2 cells in which APOC3 is highly expressed. Genomic locations of SNPs in SpCas9 PAMs identified in the potential enhancer are shown. SNPs in exon 2 of APOA4 and exon 3 of APOC3 are shown. b, Sanger sequencing traces from TOPO cloned amplicons showing the SNPs in the potential enhancer and exonic regions of APOA4 and APOC3 in HEK293 cells. E1 to E6 are gRNA binding sites in the potential enhancer region which has SNPs in the PAM sequence. SNPs are exclusively associated with one another in two unique haplotypes. c, Allele ratios of target SNPs in the genomic DNA of HEK293 cells were determined by targeted genomic DNA amplicon sequencing and indicate a 1:1 ratio.

Source data

Extended Data Fig. 5 Binding of bi-partite p65 aTF to APOA4 and APOC3 promoter and enhancer target sites in HEK293 cells.

a, Genomic locations of the enhancer gRNAs, APOA4 promoter and APOC3 promoter gRNA. The regions amplified in ChIP-qPCR assays are shown as boxes. b, Binding activity of bi-partite p65 aTF at each gRNA target region in APOA4 and APOC3 loci determined by Cas9 ChIP-qPCR, expressed as a percentage of input DNA. Two sets of primers designed to amplify the human genome at locations other than the APOC3 and APOA4 loci were used as negative controls. Open circles indicate biological replicates (n = 3), bars the mean of replicates and error bars the s.e.m. c, Binding of the bi-partite p65 aTF to the potential upstream enhancer sequence in the presence of the E1-E6 gRNAs. E1, E2, and E4 are expected to bind selectively to Allele 1 (yellow); E3, E5, and E6 to Allele 2 (orange). Relative quantification (percent next-generation sequencing reads) of the two alleles in the DNA from ChIP experiments performed with an anti-Cas9 antibody are shown. Open circles indicate biological replicates (n = 3), bars the mean of replicates and error bars the s.e.m. In, Input DNA; Ch, Cas9 ChIP DNA.

Source data

Extended Data Fig. 6 Allele-selective upregulation of HBB genes in U2OS and K562 cells using heterotopic activation of enhancer.

a, Schematic of HBB gene and the three alleles present in U2OS cells. P indicates the binding site for the gRNA targeting the HBB promoter. E1-E4 indicate binding sites for gRNAs in the HS4 putative enhancer region, which are expected to target either all alleles (E1), selectively target one allele (E2, E3) or two alleles (E4) based on the PAM of the target site (black bold indicates a base that maintains an intact PAM site and gray bold indicates a base that is expected to disrupt the PAM). A SNP in exon 1 of HBB distinguishes between allele 1 (light purple) and allele 2/3 (pink). b, Total expression of HBB in U2OS cells when the bi-partite p65 aTF was co-expressed with a gRNA targeting the promoter (P) and/or with one or more gRNAs targeting the HS4 enhancer region (E1-E4) was determined by RT-qPCR, normalized to HPRT1 levels, and calculated relative to sample with non-targeting gRNA (None). Open circles indicate biological replicates (n = 3), bars the mean of replicates and error bars the s.e.m. c, Relative quantification (percent next-generation sequencing reads of cDNA) of the three alleles of HBB mRNA when the bi-partite p65 aTF was co-expressed with a gRNA targeting the promoter (P) alone or with one or more gRNAs targeting the HS4 enhancer region (E1-E4). Open circles indicate biological replicates (n = 3), bars the mean of replicates and error bars the s.e.m.

Source data

Extended Data Fig. 7 Allele-selective upregulation of MYOD1 genes in U2OS and K562 cells, respectively, using heterotopic activation of enhancer.

a, Schematic of MYOD1 gene and the three alleles present in K562 cells. P indicates the binding site for the gRNA targeting the MYOD1 promoter. E1-E4 indicate binding sites for gRNAs in the known enhancer region termed the distal regulatory region (DRR), which are expected to selectively target allele 1 (E1, E3, E4) or all alleles (E2) based on the PAM of the target site (black bold indicates a base that maintains an intact PAM site and gray bold indicates a base that is expected to disrupt the PAM). A SNP in exon 3 of MYOD1 distinguishes between allele 1 and allele 2/3. b, Total expression of MYOD1 in K562 cells when the bi-partite p65 aTF was co-expressed with a gRNA targeting the promoter (P) and/or with one or more gRNAs targeting the DRR enhancer region (E1-E4) was determined by RT-qPCR, normalized to HPRT1 levels, and calculated relative to sample with non-targeting gRNA (None). Open circles indicate biological replicates (n = 3), bars the mean of replicates and error bars the s.e.m. c, Relative quantification (percent next-generation sequencing reads of cDNA) of the three alleles of MYOD1 mRNA when the bi-partite p65 aTF was co-expressed with a gRNA targeting the promoter (P) alone or with one or more gRNAs expected to target the DRR enhancer region (E1-E4). Open circles indicate biological replicates (n = 3), bars the mean of replicates and error bars the s.e.m.

Source data

Extended Data Fig. 8 Distribution of SNP densities that create or disrupt NGG PAM sequences at putative enhancers and promoters.

The density of SNPs is the number of SNPs divided by the base pair size of each regulatory element (promoter or enhancer) identified from the 1000 Genomes Project using DHS data from 83 different cell lines from ENCODE/Roadmap project. center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range. *** indicates p < 0.001 (Mann-Whitney U test (two-sided) with Bonferroni test for multiple comparison).

Source data

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1–6.

Reporting Summary

Supplementary Tables 1–8

Supplementary Data

Source data for supplementary figures.

Source data

Source Data Fig. 1

Guide IDs and qPCR data.

Source Data Fig. 2

Guide IDs and qPCR data.

Source Data Fig. 3

Guide IDs and qPCR data.

Source Data Fig. 4

Guide IDs, qPCR data and amplicon sequencing read counts.

Source Data Extended Data Fig. 3

Guide IDs and qPCR data.

Source Data Extended Data Fig. 4

Amplicon sequencing read counts.

Source Data Extended Data Fig. 5

Guide IDs, qPCR data and amplicon sequencing read counts.

Source Data Extended Data Fig. 6

Guide IDs, qPCR data and amplicon sequencing read counts.

Source Data Extended Data Fig. 7

Guide IDs, qPCR data and amplicon sequencing read counts.

Source Data Extended Data Fig. 8

SNP density at each category (promoter, enhancer, PAM creation, PAM disruption and mixed).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tak, Y.E., Horng, J.E., Perry, N.T. et al. Augmenting and directing long-range CRISPR-mediated activation in human cells. Nat Methods 18, 1075–1081 (2021). https://doi.org/10.1038/s41592-021-01224-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-021-01224-1

This article is cited by

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