Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex

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Abstract

Systematic interrogation of gene function requires the ability to perturb gene expression in a robust and generalizable manner. Here we describe structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci. We used these engineered Cas9 activation complexes to investigate single-guide RNA (sgRNA) targeting rules for effective transcriptional activation, to demonstrate multiplexed activation of ten genes simultaneously, and to upregulate long intergenic non-coding RNA (lincRNA) transcripts. We also synthesized a library consisting of 70,290 guides targeting all human RefSeq coding isoforms to screen for genes that, upon activation, confer resistance to a BRAF inhibitor. The top hits included genes previously shown to be able to confer resistance, and novel candidates were validated using individual sgRNA and complementary DNA overexpression. A gene expression signature based on the top screening hits correlated with markers of BRAF inhibitor resistance in cell lines and patient-derived samples. These results collectively demonstrate the potential of Cas9-based activators as a powerful genetic perturbation technology.

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Figure 1: Structure-guided design and optimization of an RNA-guided transcription activation complex.
Figure 2: Characterization of SAM-mediated gene and lincRNA activation and derivation of selection rules for efficient sgRNAs.
Figure 3: Simultaneous activation of endogenous genes using multiplexed sgRNA expression.
Figure 4: Evaluation of SAM specificity.
Figure 5: Genome-scale gene activation screening identifies mediators of BRAF inhibitor resistance.
Figure 6: Validation of top hits from genome-scale gene activation screen for PLX-4720 resistance mediators.

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BioProject

Data deposits

All reagents described in this manuscript have been deposited with Addgene (plasmid IDs 61422-61427 for SAM component plasmid and 61597 for the human SAM guide RNA library). RNA-seq data are available at BioProject under accession number PRJNA269048.

References

  1. 1

    Berns, K. et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428, 431–437 (2004)

  2. 2

    Boutros, M. et al. Genome-wide RNAi analysis of growth and viability in Drosophila cells. Science 303, 832–835 (2004)

  3. 3

    Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014)

  4. 4

    Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014)

  5. 5

    Beerli, R. R., Segal, D. J., Dreier, B. & Barbas, C. F., III Toward controlling gene expression at will: specific regulation of the erbB-2/HER-2 promoter by using polydactyl zinc finger proteins constructed from modular building blocks. Proc. Natl Acad. Sci. USA 95, 14628–14633 (1998)

  6. 6

    Zhang, F. et al. Efficient construction of sequence-specific TAL effectors for modulating mammalian transcription. Nature Biotechnol. 29, 149–153 (2011)

  7. 7

    Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013)

  8. 8

    Konermann, S. et al. Optical control of mammalian endogenous transcription and epigenetic states. Nature 500, 472–476 (2013)

  9. 9

    Maeder, M. L. et al. CRISPR RNA-guided activation of endogenous human genes. Nature Methods 10, 977–979 (2013)

  10. 10

    Perez-Pinera, P. et al. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nature Methods 10, 973–976 (2013)

  11. 11

    Mali, P, et al. CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. Nature Biotechnol. 31, 833–838 (2013)

  12. 12

    Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012)

  13. 13

    Gasiunas, G., Barrangou, R., Horvath, P. & Siksnys, V. Cas9-crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria. Proc. Natl Acad. Sci. USA 109, E2579–E2586 (2012)

  14. 14

    Nishimasu, H. et al. Crystal structure of Cas9 in complex with guide RNA and target DNA. Cell 156, 935–949 (2014)

  15. 15

    Peabody, D. S. The RNA binding site of bacteriophage MS2 coat protein. EMBO J. 12, 595–600 (1993)

  16. 16

    Lemon, B. & Tjian, R. Orchestrated response: a symphony of transcription factors for gene control. Genes Dev. 14, 2551–2569 (2000)

  17. 17

    van Essen, D., Engist, B., Natoli, G. & Saccani, S. Two modes of transcriptional activation at native promoters by NF-κB p65. PLoS Biol. 7, e73 (2009)

  18. 18

    Kretzschmar, M., Kaiser, K., Lottspeich, F. & Meisterernst, M. A novel mediator of class II gene transcription with homology to viral immediate-early transcriptional regulators. Cell 78, 525–534 (1994)

  19. 19

    Ikeda, K., Stuehler, T. & Meisterernst, M. The H1 and H2 regions of the activation domain of herpes simplex virion protein 16 stimulate transcription through distinct molecular mechanisms. Genes Cells. 7, 49–58 (2002)

  20. 20

    Neely, K. E. et al. Activation domain-mediated targeting of the SWI/SNF complex to promoters stimulates transcription from nucleosome arrays. Mol. Cell 4, 649–655 (1999)

  21. 21

    Marinho, H. S., Real, C., Cyrne, L., Soares, H. & Antunes, F. Hydrogen peroxide sensing, signaling and regulation of transcription factors. Redox Biol. 2, 535–562 (2014)

  22. 22

    Wu, X. et al. Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells. Nature Biotechnol. 32, 670–676 (2014)

  23. 23

    Johannessen, C. M. et al. COT drives resistance to RAF inhibition through MAP kinase pathway reactivation. Nature 468, 968–972 (2010)

  24. 24

    Nazarian, R. et al. Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation. Nature 468, 973–977 (2010)

  25. 25

    Musgrove, E. A. & Sutherland, R. L. Biological determinants of endocrine resistance in breast cancer. Nature Rev. Cancer 9, 631–643 (2009)

  26. 26

    Prahallad, A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100–103 (2012)

  27. 27

    Corcoran, R. B. et al. EGFR-mediated re-activation of MAPK signaling contributes to insensitivity of BRAF mutant colorectal cancers to RAF inhibition with vemurafenib. Cancer Discov. 2, 227–235 (2012)

  28. 28

    Villanueva, J. et al. Acquired resistance to BRAF inhibitors mediated by a RAF kinase switch in melanoma can be overcome by cotargeting MEK and IGF-1R/PI3K. Cancer Cell 18, 683–695 (2010)

  29. 29

    Shi, H., Kong, X., Ribas, A. & Lo, R. S. Combinatorial treatments that overcome PDGFRβ-driven resistance of melanoma cells to V600EB-RAF inhibition. Cancer Res. 71, 5067–5074 (2011)

  30. 30

    Johannessen, C. M. et al. A melanocyte lineage program confers resistance to MAP kinase pathway inhibition. Nature 504, 138–142 (2013)

  31. 31

    Dorsam, R. T. & Gutkind, J. S. G-protein-coupled receptors and cancer. Nature Rev. Cancer 7, 79–94 (2007)

  32. 32

    Lappano, R. & Maggiolini, M. G protein-coupled receptors: novel targets for drug discovery in cancer. Nature Rev. Drug Discov. 10, 47–60 (2011)

  33. 33

    Franke, T. F. PI3K/Akt: getting it right matters. Oncogene 27, 6473–6488 (2008)

  34. 34

    Desgrosellier, J. S. & Cheresh, D. A. Integrins in cancer: biological implications and therapeutic opportunities. Nature Rev. Cancer 10, 9–22 (2010)

  35. 35

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012)

  36. 36

    Lin, W. M. et al. Modeling genomic diversity and tumor dependency in malignant melanoma. Cancer Res. 68, 664–673 (2008)

  37. 37

    Wilks, C. et al. The Cancer Genomics Hub (CGHub): overcoming cancer through the power of torrential data. Database 2014, bau093 (2014)

  38. 38

    Rizos, H. et al. BRAF inhibitor resistance mechanisms in metastatic melanoma: spectrum and clinical impact. Clin. Cancer Res. 20, 1965–1977 (2014)

  39. 39

    Konieczkowski, D. J. et al. A melanoma cell state distinction influences sensitivity to MAPK pathway inhibitors. Cancer Discov. 4, 816–827 (2014)

  40. 40

    Anders, C., Niewoehner, O., Duerst, A. & Jinek, M. Structural basis of PAM-dependent target DNA recognition by the Cas9 endonuclease. Nature 513, 569–573 (2014)

  41. 41

    Hsu, P. D., Lander, E. S. & Zhang, F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 1262–1278 (2014)

  42. 42

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

  43. 43

    Hsu, P. D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nature Biotechnol. 31, 827–832 (2013)

  44. 44

    Luo, B. et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl Acad. Sci. USA 105, 20380–20385 (2008)

  45. 45

    Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009)

  46. 46

    Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011)

  47. 47

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

  48. 48

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011)

  49. 49

    Smalley, K. S. Understanding melanoma signaling networks as the basis for molecular targeted therapy. J. Invest. Dermatol. 130, 28–37 (2010)

  50. 50

    Wong, P. P. et al. Histone demethylase KDM5B collaborates with TFAP2C and Myc to repress the cell cycle inhibitor p21cip (CDKN1A). Mol. Cell. Biol. 32, 1633–1644 (2012)

  51. 51

    Hart, M. J. et al. Direct stimulation of the guanine nucleotide exchange activity of p115 RhoGEF by Gα13. Science 280, 2112–2114 (1998)

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Acknowledgements

We would like to thank S. Shehata, K. Zheng, C. Johannessen, L. Garraway, O. Shalem and members of the Zhang laboratory for assistance and helpful discussions. O.O.A. is supported by a NSF Graduate Research Fellowship, J.S.G. is supported by a D.O.E. Computational Science Graduate Fellowship, H.N. is supported by PRESTO from JST and Grant-in-Aid for Scientific Research (B) from JSPS, O.N. is supported by the CREST program and JST, and F.Z. is supported by the NIMH (DP1-MH100706), the NINDS (R01-NS07312401), NSF, the Keck, Searle Scholars, Klingenstein, Vallee, and Simons Foundations, and Bob Metcalfe. CRISPR reagents are available to the academic community through Addgene, and associated protocols, support forum and computational tools are available via the Zhang laboratory website (http://www.genome-engineering.org).

Author information

S.K. and F.Z. conceived the project. S.K., M.D.B., A.E.T. and F.Z. designed the experiments. S.K., M.D.B., A.E.T., C.B., P.D.H. and J.J. performed experiments and analysed data. H.N. and O.N. helped with structural interpretation. N.H. performed the RNA-seq analysis. J.S.G. performed the depletion guide efficacy analysis. O.O.A. performed the analysis of clinical data sets. S.K., A.E.T., P.D.H. and F.Z. wrote the paper with help from all authors.

Correspondence to Feng Zhang.

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Extended data figures and tables

Extended Data Figure 1 Structure-guided engineering of Cas9 sgRNA.

a, Schematic of the sgRNA stem loops showing contacts between each stem loop and Cas9. Contacting amino acid residues are highlighted in yellow. Tetraloop and stem loop 2 do not make any contacts with Cas9, whereas stem loops 1 and 3 share extensive contacts with Cas9. b, sgRNA 2.0 with MS2 stem loops inserted into the tetraloop and stem loop 2. c, Addition of a second NLS or an alternative HNH domain inactivating point mutation in Cas9 improve efficiency of transcription activation for MYOD1 moderately. d, dCas9–VP64 activators exhibit improved performance by recruitment of MS2–p65 to the tetraloop and stem loop 2. Addition of an AU flip or extension in the tetraloop does not increase the effectiveness of dCas9-mediated transcription activation. e, Tetraloop and stem loop 2 are amenable to replacement with MS2 stem loops. Base changes from the sgRNA 2.0 scaffold are shown at the respective positions, with dashes indicating unaltered bases and bases below dashes indicating insertions. Deletions are indicated by absence of dashes at respective positions. All figures are n = 3 and mean ± s.e.m.

Extended Data Figure 2 SAM mediates efficient activation of a panel of 12 coding genes and 6 lincRNAs.

a, Comparison of the activation levels of 12 genes with dCas9–VP64 in combination with MS2–p65, MS2–p65–HSF1, or MS2–p65–MyoD1. MS2–p65–HSF1 mediated significantly higher levels of activation than MS2–p65 alone for 9 out of 12 genes. The best guide out of 8 tested for each gene (Fig. 2a) was used in this experiment. Activation levels for each type of MS2-fusion is presented as a percentage relative to the activation achieved using MS2–p65. b, Investigation of transcriptional changes in the closest coding transcripts for SAM-mediated activation of 6 lincRNAs. Direction of the coding transcript relative to the lincRNA and distance between transcription start sites are shown. Only targeting of HOTTIP resulted in a significant change in the levels of the closest coding transcript (HOXA13). The best guide out of 8 tested for each gene (Fig. 2e) in combination with dCas9–VP64 and MS2–p65–HSF1 was used in this experiment. All figures are n = 3 and mean ± s.e.m.

Extended Data Figure 3 Activation of lincRNAs by SAM.

Six lincRNAs, three characterized and three uncharacterized, were targeted using SAM. For each lincRNA, 8 sgRNAs were designed to target the proximal promoter region (+1 to −800 bp from the TSS) with 4 different MS2 activators (MS2–p65–HSF1, MS2–p65–MyoD1, MS2–p65, and MS2–VP64) in combination with dCas9–VP64. MS2 activators with a combination of 2 different domains (MS2–p65–HSF1 or MS2–p65–MyoD1) consistently provided the highest activation for each lincRNA, P < 0.01 for MS2–p65–HSF1 or MS2–p65–MyoD1 versus MS2–p65. n = 3 and mean ± s.e.m. is shown.

Extended Data Figure 4 Multiplexed activation using SAM and activation of a panel of 10 genes as a function of SAM component dosage.

a, Activation of a panel of 10 genes by combinations of 2, 4, 6 or 8 sgRNAs simultaneously. The mean fold upregulation is shown on a log10 scale. MS2–p65–HSF1 and dCas9–VP64 were used in this experiment. b, The relative activation efficiency of individual sgRNAs varies depending on the target gene and the degree of multiplexing. n = 3 and mean ± s.e.m. is shown.

Extended Data Figure 5 The effect of guide and SAM-component dilution on target activation.

a, The results for dilution of sgRNA 2.0 on target activation. b, The result for dilution of sgRNA 1.0 on target activation. # denotes an activation of <twofold at 1× guide dilution. c, Effect of MS2–p65–HSF1 and dCas9–VP64 dilution, at 1:1, 1:4, 1:10 and 1:50 of the original dosage for each component, on the effectiveness of transcription upregulation. The amount of sgRNA expression plasmid was kept constant. d, Effect of diluting all three SAM components (dCas9–VP64, MS2–p65–HSF1, and sgRNA) at 1:4, 1:10, and 1:50 of the original dosage for each component. Fold upregulation is calculated using GFP-transfected cells as the baseline. Error bars indicate s.e.m. and n = 3 for all figures.

Extended Data Figure 6 RNA-seq analysis of transcriptome changes mediated by SAM.

a, A heat map of log(TPM) expression values of all statistically significant differentially expressed genes (t-test q value < 0.05 adjusted with FDR multiple hypothesis correction) found in any of the six experimental conditions compared to the GFP-transfected control. b, Expression levels in log(TPM) values of all detected genes in RNA-seq libraries of GFP-transfected controls (x-axis of all graphs) compared to (from left to right): non-targeting control sgRNA no. 2 in 1× dilution and 50× dilution (y axis). Marked are HBG1 (red) and HGB2 (blue).

Extended Data Figure 7 Genome-scale lentiviral screen using puromycin-resistant SAM sgRNA library.

a, Design of three lentiviral vectors for expressing sgRNA, dCas9–VP64, and MS2–p65–HSF1. Each vector contains a distinct selection marker to enable co-selection of cells expressing all three vectors. b, Lentiviral delivery of SAM components was tested by first generating 293FT cell lines stably integrated with dCas9–VP64 and MS2–p65–HSF1, and subsequently transducing these cells with single-gene targeting lentiviral sgRNAs at MOI <0.2. Transcription activation efficiency is measured 4 days post sgRNA lentivirus transduction and selection with zeocin or puromycin. Activation is at least as effective as previously observed with transient transfection in all three cases. c, Box-plot showing the distribution of sgRNA frequencies at different time points post lentiviral transduction with the Puromycin library, after treatment with DMSO vehicle or PLX-4720. Two infection replicates are shown. d, Identification of top candidate genes using the RIGER P value analysis (KS method) based on the average of both infection replicates. Genes are organized by positions within chromosomes. e, Overlap between the top 20 hits from the zeocin and puromycin screens. Genes belonging to the same family are indicated by the same colour. There is a 50% overlap between the top hits of each screen as shown in the intersection of the Venn diagram. f, Relevant signalling pathways in BRAF inhibitor resistance. Reactivation of the Ras-ERK pathway as well as the parallel PI3K-Akt pathway have previously been implicated as two alternative resistance mechanisms to BRAF inhibitors23,24,26,27,28,29. Both pathways have been described as stimulating proliferation and survival49. BAD, FOXO and p27 are common inhibited downstream targets49. Recently, stimulation of the cAMP-CREB pathway by GPCRs has been described as a potential additional resistance mechanism30. Top candidates from our screen are indicated in blue and putative connections to all three pathways are shown25,50,51. Candidates previously validated to mediate PLX-4720 resistance are underlined in green26,30. COT and CREB are independently validated mediators of resistance23,30.

Extended Data Figure 8 Individual validation of PLX-4720 resistance mediation by top screen hits.

a, Validation of the top 10 Zeo screen hits and the top 10 shared hits (13 genes total). Every gene was independently activated by all three guides from the screen and tested for the ability to increase survival of A375 cells treated with three different concentrations of PLX-4720 (2μM, 0.5μM and 0.15μM). The z-score based on the % increase in survival relative to control (A375 cells transduced with dCas9–VP64 and MS2–p65–HSF1 alone) is shown for each guide and PLX-4720 concentration. Five cDNAs available from a previous large-scale gain-of-function PLX-4720 resistance screen were also included30. Every guide for each top hit mediates significant PLX-4720 resistance. b, The same panel of top hits exhibits a large range of basal expression levels and is effectively activated by all guides. The expression level relative to the housekeeping gene GAPDH is shown both at baseline as well as after activation by each individual guide. c, Ranks of the validated set of genes in the previous ORF screen. Six genes were not part of the cDNA library, five hits are shared (present in the top 3%) and only LPAR5 and ARHGEF1 were present but not highly ranked. Both of these genes had highly ranked members of the same family. d, Levels of overexpression from the five tested cDNA constructs. Transcript levels were higher for these five cDNAs than those mediated by SAM for the same genes. e, Correlation of survival at 2 μM PLX-4720 treatment and transcript upregulation achieved by individual guides. For most genes (9 out of 12 shown), the percent survival is very similar across transcript levels achieved by all three guides. Dotted lines indicate control survival.

Extended Data Figure 9 Expression of top hits and screen signatures are elevated in PLX-4720 resistant melanoma cell lines and patient samples.

a, Heat map showing sensitivity to different drugs (top), expression of SAM top screen hits (middle), and SAM screen signature scores (bottom; see Methods for signature generation) in Cancer Cell Line Encyclopedia cell lines35. Drug sensitivities are measured as Activity Areas (AA). The melanoma cell lines are sorted by PLX-4720 drug sensitivity. RAF inhibitors: PLX-4720 and RAF265; MEK inhibitors: AZD6244 and PD-0325901. b, Heat map showing expression of gene/signature markers for BRAF-inhibitor sensitivity (top), expression of SAM top screen hits (middle) and screen signature scores (bottom) in different BRAF(V600) patient melanoma samples (primary or metastatic) from The Cancer Genome Atlas. c, Heat map showing MITF expression (top), screen signature scores (middle), and expression of SAM top screen hits (bottom) in different BRAF(V600E) patient melanoma biopsies post-treatment with BRAF inhibitors38. d, Bar chart showing the number of patients (out of 13 total) from c with at least a twofold change (post/pre-treatment) in gene expression of the top PLX-4720 screen hits in the post-treatment samples. All associations are measured using the information coefficient (IC) between the index and each of the features and P values are determined using a permutation test. All heat maps show Z scores.

Extended Data Figure 10 Guide depletion analysis to identify gene set enrichment and guide efficiency parameters.

a, b, Heat maps of sgRNA nucleotide content versus depletion after 21 days. sgRNA targeting significantly depleted genes (from RIGER analysis) in sgRNA-zeo (a) or sgRNA-puro (b) screens were analysed for trends based on G or T content in the sgRNA sequence. sgRNA depletion is positively correlated with G content and negatively correlated with T content. Other bases analysed (A and C) had significant (P < 0.0007) but weak (r < 0.2) negative correlation. c, 90% of guides analysed fall within a 100-bp window < 200 bp from the TSS. Boxplots of distance from 5′ end of the guide to the TSS for sgRNA-zeo and sgRNA-puro in same and reverse direction (relative to target transcription). Whiskers span 5th to 95th quartile. d, Coefficients and P values for ordinary least squares predicting sgRNA depletion of significantly depleted genes from G content, T content, distance from 5′ end of the guide to the TSS and direction of guide. Only nucleotide content has a significant effect on depletion in this model, consistent with a high efficiency of guides within 200 bp of the TSS regardless of strand orientation (Fig. 2d). e, The cumulative frequency of sgRNAs 3 and 21 days after transduction in A375 cells is shown. Shift in the 21-day curve represents the depletion in a subset of sgRNAs. Less than 0.1% of all guides are not detected at day 3 (detected by less than 10 reads). f, Depleted guides (Supplementary Table 3) can be analysed for significant clustering of gene categories. Gene categories exhibiting significant depletion based on Ingenuity Pathway Analysis (P < 0.01 after Benjamini–Hochberg FDR correction) are shown. Categories based on the 1,000 most depleted guides individually (left) and the average of all 3 guides/gene (right). These categories include either positive or negative regulators of each pathway that reduce proliferation and survival.

Supplementary information

Supplementary Information

This file contains Supplementary Sequences and Supplementary Tables 1-5 (see separate files for Supplementary Tables 6 and 7). (PDF 527 kb)

Supplementary Table

This file contains Supplementary Table 6, which is a list of sgRNA target sequences for the human genome. (CSV 2202 kb)

Supplementary Table

This file contains Supplementary Table 7, which shows the normalized raw counts of sgRNAs from all of the screens conducted in this study. (CSV 13592 kb)

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Konermann, S., Brigham, M., Trevino, A. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015) doi:10.1038/nature14136

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