The RNA-guided endonuclease Cas9 can be converted into a programmable transcriptional repressor, but inefficiencies in target-gene silencing have limited its utility. Here we describe an improved Cas9 repressor based on the C-terminal fusion of a rationally designed bipartite repressor domain, KRAB–MeCP2, to nuclease-dead Cas9. We demonstrate the system’s superiority in silencing coding and noncoding genes, simultaneously repressing a series of target genes, improving the results of single and dual guide RNA library screens, and enabling new architectures of synthetic genetic circuits.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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


  1. 1.

    Cho, S. W., Kim, S., Kim, J. M. & Kim, J.-S. Targeted genome engineering in human cells with the Cas9 RNA-guided endonuclease. Nat. Biotechnol. 31, 230–232 (2013).

  2. 2.

    Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

  3. 3.

    Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

  4. 4.

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

  5. 5.

    DiCarlo, J. E. et al. Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems. Nucleic Acids Res. 41, 4336–4343 (2013).

  6. 6.

    Hwang, W. Y. et al. Efficient genome editing in zebrafish using a CRISPR-Cas system. Nat. Biotechnol. 31, 227–229 (2013).

  7. 7.

    Wang, H. et al. One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell 153, 910–918 (2013).

  8. 8.

    Zhou, Y. et al. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells. Nature 509, 487–491 (2014).

  9. 9.

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

  10. 10.

    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).

  11. 11.

    Koike-Yusa, H., Li, Y., Tan, E.-P., del Castillo Velasco-Herrera, M. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).

  12. 12.

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

  13. 13.

    Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

  14. 14.

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

  15. 15.

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

  16. 16.

    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).

  17. 17.

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

  18. 18.

    La Russa, M. F. & Qi, L. S. The new state of the art: Cas9 for gene activation and repression. Mol. Cell Biol. 35, 3800–3809 (2015).

  19. 19.

    Evers, B. et al. CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat. Biotechnol. 34, 631–633 (2016).

  20. 20.

    Chavez, A. et al. Highly efficient Cas9-mediated transcriptional programming. Nat. Methods 12, 326–328 (2015).

  21. 21.

    Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).

  22. 22.

    Zalatan, J. G. et al. Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell 160, 339–350 (2015).

  23. 23.

    Tanenbaum, M. E., Gilbert, L. A., Qi, L. S., Weissman, J. S. & Vale, R. D. A protein-tagging system for signal amplification in gene expression and fluorescence imaging. Cell 159, 635–646 (2014).

  24. 24.

    Shao, S. et al. Multiplexed sgRNA expression allows versatile single non-repetitive DNA labeling and endogenous gene regulation. bioRxiv Preprint at https://www.biorxiv.org/content/early/2017/03/29/121905 (2017).

  25. 25.

    Stolzenburg, S. et al. Stable oncogenic silencing in vivo by programmable and targeted de novo DNA methylation in breast cancer. Oncogene 34, 5427–5435 (2015).

  26. 26.

    Li, F. et al. Chimeric DNA methyltransferases target DNA methylation to specific DNA sequences and repress expression of target genes. Nucleic Acids Res. 35, 100–112 (2007).

  27. 27.

    Stepper, P. et al. Efficient targeted DNA methylation with chimeric dCas9-Dnmt3a-Dnmt3L methyltransferase. Nucleic Acids Res. 45, 1703–1713 (2017).

  28. 28.

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

  29. 29.

    Shen, J. P. et al. Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nat. Methods 14, 573–576 (2017).

  30. 30.

    Dixon, S. J., Costanzo, M., Baryshnikova, A., Andrews, B. & Boone, C. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).

  31. 31.

    Menche, J. et al. Uncovering disease-disease relationships through the incomplete interactome. Science 347, 1257601 (2015).

  32. 32.

    Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).

  33. 33.

    Sajesh, B. V. & McManus, K. J. Targeting SOD1 induces synthetic lethal killing in BLM- and CHEK2-deficient colorectal cancer cells. Oncotarget 6, 27907–27922 (2015).

  34. 34.

    Imamura, O. & Campbell, J. L. The human Bloom syndrome gene suppresses the DNA replication and repair defects of yeast dna2 mutants. Proc. Natl Acad. Sci. USA 100, 8193–8198 (2003).

  35. 35.

    Budd, M. E. & Campbell, J. L. The pattern of sensitivity of yeast dna2 mutants to DNA damaging agents suggests a role in DSB and postreplication repair pathways. Mutat. Res. 459, 173–186 (2000).

  36. 36.

    Popuri, V., Croteau, D. L., Brosh, R. M. Jr. & Bohr, V. A. RECQ1 is required for cellular resistance to replication stress and catalyzes strand exchange on stalled replication fork structures. Cell Cycle 11, 4252–4265 (2012).

  37. 37.

    Kiani, S. et al. CRISPR transcriptional repression devices and layered circuits in mammalian cells. Nat. Methods 11, 723–726 (2014).

  38. 38.

    Friedman, J. R. et al. KAP-1, a novel corepressor for the highly conserved KRAB repression domain. Genes Dev. 10, 2067–2078 (1996).

  39. 39.

    Kim, S. S. et al. A novel member of the RING finger family, KRIP-1, associates with the KRAB-A transcriptional repressor domain of zinc finger proteins. Proc. Natl. Acad. Sci. USA 93, 15299–15304 (1996).

  40. 40.

    Moosmann, P., Georgiev, O., Le Douarin, B., Bourquin, J. P. & Schaffner, W. Transcriptional repression by RING finger protein TIF1 beta that interacts with the KRAB repressor domain of KOX1. Nucleic Acids Res. 24, 4859–4867 (1996).

  41. 41.

    Jones, P. L. et al. Methylated DNA and MeCP2 recruit histone deacetylase to repress transcription. Nat. Genet. 19, 187–191 (1998).

  42. 42.

    Nan, X. et al. Transcriptional repression by the methyl-CpG-binding protein MeCP2 involves a histone deacetylase complex. Nature 393, 386–389 (1998).

  43. 43.

    Wade, P. A. et al. Histone deacetylase directs the dominant silencing of transcription in chromatin: association with MeCP2 and the Mi-2 chromodomain SWI/SNF ATPase. Cold Spring Harb. Symp. Quant. Biol. 63, 435–445 (1998).

  44. 44.

    Kimura, H. & Shiota, K. Methyl-CpG-binding protein, MeCP2, is a target molecule for maintenance DNA methyltransferase, Dnmt1. J. Biol. Chem. 278, 4806–4812 (2003).

  45. 45.

    Kleinstiver, B. P. et al. High-fidelity CRISPR-Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016).

  46. 46.

    Fu, Y., Sander, J. D., Reyon, D., Cascio, V. M. & Joung, J. K. Improving CRISPR-Cas nuclease specificity using truncated guide RNAs. Nat. Biotechnol. 32, 279–284 (2014).

  47. 47.

    Polstein, L. R. et al. Genome-wide specificity of DNA binding, gene regulation, and chromatin remodeling by TALE- and CRISPR/Cas9-based transcriptional activators. Genome Res. 25, 1158–1169 (2015).

  48. 48.

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

  49. 49.

    Kuscu, C., Arslan, S., Singh, R., Thorpe, J. & Adli, M. Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease. Nat. Biotechnol. 32, 677–683 (2014).

  50. 50.

    Chavez, A. et al. Comparison of Cas9 activators in multiple species. Nat. Methods 13, 563–567 (2016).

  51. 51.

    Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2–ΔΔC(T) method. Methods 25, 402–408 (2001).

Download references


This work was supported by the NIH (grants RM1 HG008525 and P50 HG005550 to G.M.C.), the National Cancer Institute (grant 5T32CA009216-34 to A.C.), the Burroughs Wellcome Fund (Career Award for Medical Scientists to A.C.), NIGMS (R35 GM119850 to N.E.L.), the Novo Nordisk Foundation (NNF10CC1016517 to N.E.L.), DARPA (Young Faculty Award D16AP00047 to S.K.), the Arizona State University Fulton Schools of Engineering startup fund (S.K.), and the Paul G. Allen Frontiers Group (J.J.C.). HEK293T cells were a gift from P. Mali (University of California, San Diego, San Diego, CA, USA). psPAX2 was a gift from D. Trono (Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland). pCMV-VSV-G was a gift from B. Weinberg (Massachusetts Institute of Technology, Boston, MA, USA). The plasmid containing a single gRNA library targeting essential genes was a gift from R. Bernards (The Netherlands Cancer Institute, Amsterdam, the Netherlands).

Author information

Author notes

  1. These authors contributed equally: Nan Cher Yeo, Alejandro Chavez.


  1. Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA

    • Nan Cher Yeo
    • , Alissa Lance-Byrne
    • , Yingleong Chan
    • , Denitsa Milanova
    • , Xiaoge Guo
    • , Angela Tung
    • , Ryan J. Cecchi
    • , Marcelle Tuttle
    • , Elaine T. Lim
    • , Noah Davidsohn
    • , James J. Collins
    •  & George M. Church
  2. Department of Genetics, Harvard Medical School, Boston, MA, USA

    • Nan Cher Yeo
    • , Yingleong Chan
    • , Denitsa Milanova
    • , Xiaoge Guo
    • , Elaine T. Lim
    • , Noah Davidsohn
    •  & George M. Church
  3. Department of Pathology and Cell Biology, Columbia University College of Physicians and Surgeons, New York, NY, USA

    • Alejandro Chavez
  4. School of Biological and Health Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA

    • David Menn
    • , Swechchha Pradhan
    • , Mo R. Ebrahimkhani
    •  & Samira Kiani
  5. Department of Bioengineering, University of California, San Diego, San Diego, CA, USA

    • Chih-Chung Kuo
    •  & Nathan E. Lewis
  6. Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, San Diego, CA, USA

    • Chih-Chung Kuo
    •  & Nathan E. Lewis
  7. Cell Surface Signalling Laboratory, Wellcome Trust Sanger Institute, Cambridge, UK

    • Sumana Sharma
  8. Division of Gastroenterology and Hematology, Mayo Clinic College of Medicine and Science, Phoenix, AZ, USA

    • Mo R. Ebrahimkhani
  9. Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    • James J. Collins
  10. Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA

    • James J. Collins
  11. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    • James J. Collins
  12. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • James J. Collins
  13. Department of Pediatrics, University of California, San Diego, San Diego, CA, USA

    • Nathan E. Lewis


  1. Search for Nan Cher Yeo in:

  2. Search for Alejandro Chavez in:

  3. Search for Alissa Lance-Byrne in:

  4. Search for Yingleong Chan in:

  5. Search for David Menn in:

  6. Search for Denitsa Milanova in:

  7. Search for Chih-Chung Kuo in:

  8. Search for Xiaoge Guo in:

  9. Search for Sumana Sharma in:

  10. Search for Angela Tung in:

  11. Search for Ryan J. Cecchi in:

  12. Search for Marcelle Tuttle in:

  13. Search for Swechchha Pradhan in:

  14. Search for Elaine T. Lim in:

  15. Search for Noah Davidsohn in:

  16. Search for Mo R. Ebrahimkhani in:

  17. Search for James J. Collins in:

  18. Search for Nathan E. Lewis in:

  19. Search for Samira Kiani in:

  20. Search for George M. Church in:


Y.C., D. Menn, D. Milanova, and C.-C.K. contributed equally to the manuscript. A.C. and N.C.Y. conceived the study. N.C.Y. and A.C. designed experiments. A.C. and A.T. designed, built, and tested the initial set of dCas9 repressor fusions. N.C.Y. performed the majority of endogenous gene targeting experiments with assistance from A.L.-B. and additional technical contributions from R.J.C., M.T., and A.C. Library screens and next-generation sequencing were performed by N.C.Y. Analysis of the essential gene library data was performed by Y.C. with contributions from N.C.Y. and A.C. S.S. performed MAGeCK analysis. RNA-seq analysis was led by D. Milanova with assistance in library preparation from N.C.Y. and data interpretation by N.C.Y. and A.C. C.-C.K. analyzed the dual-guide epistasis experiment with oversight from N.E.L. and interpreted data with assistance from N.C.Y. and A.C. X.G. aided in next-generation sequencing and performed preliminary analysis of the sequencing data. N.D. provided technical experience and insight. E.T.L. helped analyze a portion of the library screening data. D. Menn and S.P. performed the synthetic circuit experiments under the guidance of M.R.E. and S.K. S.K. designed all synthetic circuits used in the study. Research was performed in the laboratory of G.M.C. with oversight from both J.J.C. and G.M.C. N.C.Y. and A.C. wrote the manuscript, with contributions from all other authors.

Competing interests

G.M.C. is a founder of and advisor for Editas Medicine. G.M.C. has equity in Editas and Caribou Biosciences (for the full disclosure list, please see http://arep.med.harvard.edu/gmc/tech.html).

Corresponding authors

Correspondence to Alejandro Chavez or Samira Kiani or George M. Church.

Integrated supplementary information

  1. Supplementary Figure 1 Targeted screen to identify repression domains that function with dCas9.

    (a) Schematic of the EYFP fluorescent reporter construct used in the targeted screen. A protospacer sequence followed by TGG PAM is placed within the minimal CMV (minCMV) promoter upstream of the EYFP reporter gene. Transcription of the EYFP reporter is driven upon binding of a GAL4-VP16 protein to the UAS sequences present upstream of the minCMV promoter. (b) The fluorescent reporter and sgRNA were co-transfected with the indicated repressors into HEK293T cells. At 2 days post-transfection, EYFP fluorescence levels were measured to quantify the amount of repression from the various dCas9 fusion proteins. The name of the protein from which the transcriptional regulatory domain was isolated is listed on the x-axis. All domains were fused to the C-terminus of the dCas9 protein. KRAB and the six top-performing domains were used for subsequent engineering. n = 2 biologically independent samples (cell cultures).

  2. Supplementary Figure 2 Reporter screen of 49 dCas9 bipartite repressors.

    A series of all pairwise repeating and non-repeating bipartite repressors were generated using KRAB and the six top-performing domains identified from the initial screen (MeCP2, SIN3A, HDT1, MBD2B, NIPP1, and HP1a). Each repressor was tested using the same fluorescent reporter assay as in Supplementary Figure 1. n = 2 biologically independent samples (cell cultures).

  3. Supplementary Figure 3 Repression of endogenous genes using top bipartite fusions.

    HEK293T cells were co-transfected with gRNAs against the indicated four genes simultaneously with the labeled repressors. Samples were collected for RNA extraction at 4 days post-transfection. The expression levels of targeted genes were measured by RT-qPCR. n = 2 biologically independent samples (cell cultures).

  4. Supplementary Figure 4 Reporter screen of eight dCas9 tripartite repressors.

    A series of rationally designed tripartite repressors were generated based on the top-performing bipartite repression domains. Each repressor was tested using the same fluorescent reporter assay as in Supplementary Figure 1. n = 2 biologically independent samples (cell cultures).

  5. Supplementary Figure 5 Repression of endogenous genes with the top tripartite fusions compared with that observed with the bipartite KRAB–MeCP2 repressor.

    The three best performing tripartite repressors based on the reporter screen were selected and tested against a series of endogenous loci, along with HP1A-KRAB and KRAB-MeCP2. RNA levels of the four indicated genes from a multiplexed repression experiment were measured using RT-qPCR. n = 2 biologically independent samples (cell cultures).

  6. Supplementary Figure 6 Reporter screen of different dCas9 fusion proteins containing KRAB or MeCP2.

    (a) Schematic of CRP repression device. dCas9 repressors coupled with a U6-driven gRNA regulate EYFP output through binding two targets sites within the CRP promoter. (b) dCas9-KRAB-MeCP2 outperformed either KRAB or MeCP2 either as single or double fusions to dCas9 when paired with sgRNA in repressing EYFP output. Data are presented as mean ± s.e.m. n = 3 biologically independent samples (cell cultures). One-sided Student’s T-test was used for statistical comparison. ¥ indicates p < 0.05 vs. dCas9, * indicates p < 0.05 vs. dCas9-KRAB.

  7. Supplementary Figure 7 dCas9–KRAB–MeCP2-mediated repression is highly specific in human cells.

    (a) HEK293T cells were transfected with a gRNA targeting the CXCR4 gene along with the indicated dCas9 repressor. Expression of the target gene CXCR4 and several nearest neighboring genes, including MCM6, DARS, THSD7B, HNMT, and SPOPL, were examined. These genes are located at approximately -274, -208, 651, 1849, and 2387 kb, respectively, away from CXCR4. (b) Expression of the target gene SYVN1 and several nearest neighboring genes, including ZNHIT2, FAU, MRPL49, SPDYC, and CAPN1, were examined. These genes are located at approximately -10, -6, -5, 42, and 53 kb, respectively, away from SYVN1. For a-b, data are shown as mean ± s.e.m. n = 4 biologically independent samples (cell cultures). One-sided Student T-test was used to perform statistical comparison. * p < 0.05 v.s. dCas9-KRAB. (c) RNA-seq analysis of HEK293T cells transfected with gRNA targeting CXCR4 along with dCas9, dCas9-KRAB or dCas9-KRAB-MeCP2. Shown are density plots indicating the fold changes in gene expression from different groups. CXCR4 expression is indicated with blue, orange, or red dot and control is indicated with a black dot. n = 2 biologically independent samples (cell cultures).

  8. Supplementary Figure 8 Differentially expressed (DE) genes in an RNA-seq experiment.

    (a-c) Shown are log2 fold change (FC) versus average log2 count per millions (CPM) for dCas9, dCas9-KRAB and dCas9-KRAB-MeCP2 relative to the negative control. Genes with no fold change are marked in black, DE genes are marked in grey, and DE genes above log2 FC of 1.5 threshold are marked in red. Positive log2 FC represents transcriptional activation while negative values indicate repression. (d) Shown is a summary of the number of DE genes showing up- or down-regulation with log2 FC > 1.5 in different groups. (Methods associated with this figure are described in Supplementary Note 2).

  9. Supplementary Figure 9 Cluster analysis of the top 35 DE genes in negative control, dCas9, dCas9–KRAB, and dCas9–KRAB–MeCP2 groups.

    a) Clustering of genes with correlated expression provides insights into the biological effects of repressor’s activity. Heatmap representing distances between each gene pair is calculated based on Euclidean distance, (1-R)2/2 where R represents the Pearson’s correlation of two genes. A scale key bar of normalized log2 CPM shows large negative (colored in blue) and positive (colored in red) correlations. Genes with large positive correlations correspond to small Euclidean distances and cluster together. (b-c) Shown are Venn diagrams comparing transcriptome-wide downregulated or upregulated genes among the different repressors. (Methods associated with this figure are described in Supplementary Note 2).

  10. Supplementary Figure 10 HAP1 cells stably expressing dCas9 repressors.

    (a) Shown is the expression level of dCas9 repressors in the different stable cell lines. n = 2 biologically independent samples (cell cultures). (b) Stable cells were transduced with lentiviruses containing a gRNA targeting the indicated genes. dCas9-KRAB-MeCP2-containing cell line induced stronger suppression of most target genes. For a-b, n = 2 biologically independent samples (cell cultures). (c) Shown are distributions of sgRNA constructs transduced into HAP1 wild-type cells over time. The sgRNA counts remained similar to that of the initial DNA library used to generate lentivirus. Blue squares represent non-essential gene-targeting constructs and orange squares represent essential gene-targeting constructs.

  11. Supplementary Figure 11 dCas9–KRAB–MeCP2 outperformed dCas9–KRAB regardless of targeting position.

    (a) Using the data from our pooled library screens, we plot sgRNA odd ratio ( < 1 indicates depletion) as a function of position from the transcription start site (TSS) for 370 essential gene-targeting sgRNAs. Shown are results from HAP1 cells using dCas9-KRAB or dCas9-KRAB-MeCP2 in the screen at day 14. (b) Of the total sgRNAs depleted by each repressor, we quantified the fraction of significantly depleted sgRNAs within or outside of the optimal window. sgRNAs located within the optimal targeting window showed a higher likelihood of being depleted as compared to sgRNAs positioned outside of the window across all repressors. (c) Of the total sgRNAs tested within or outside of the previously defined targeting window (-50bp to + 200 bp from TSS), we quantified the fraction of significantly depleted sgRNAs when combined with the different repressors. dCas9-KRAB-MeCP2 showed the highest performance regardless of sgRNA targeting window. Similar results were observed in SH-SY5Y and HEK293T screens (figures not shown). See Supplementary Table 7 for a summary of the data in all three cell lines.

  12. Supplementary Figure 12 Overlap of non-essential (NE) gene-targeting sgRNAs showing depletion in HAP1, SH-SY5Y, and HEK293T cells.

    Venn diagrams show the numbers of NE-targeting sgRNAs exhibiting depletion that are unique to a repressor or common among repressors. (a) In HAP1 cells, PLA2G2E-19, sgRNA with the strongest depletion score, was depleted by both dCas9-KRAB-MeCP2 and dCas9-KRAB. Other top depleted sgRNAs seen in KRAB-MeCP2 screen, including HTR3D-6, LHX5-8, MRGPRD-2, and POU4F2-15, are also common to dCas9-KRAB or dCas9. (b) In SH-SY5Y screen, the top two NE-targeting sgRNAs, NPHS2-7 and PLA2G2E-19, seen with dCas9-KRAB-MeCP2 were also depleted with dCas9-KRAB. (c) Overlap of NE-targeting sgRNAs observed in HEK293T was shown. See Supplementary Data 5 for a full list of NE guides exhibiting depletion when combined with different repressors.

  13. Supplementary Figure 13 Performance of different dCas9 repressors in pooled essentiality screens.

    Shown are rank-ordered genes identified from sgRNA enrichment analysis performed using MAGeCK analysis pipeline in (a) HAP1 (day 14), (b) SH-SY5Y (day 14), and (c) HEK293T (day 14) with the indicated repressors. Genes were plotted according to the p-values obtained from MAGeCK and marked the FDR threshold at 0.25 (25%). The genes are colored by essentiality (red indicates essential genes and gray indicates non-essential genes). The horizontal lines indicate the FDR threshold and where there is no line indicates no genes were identified at the given threshold.

  14. Supplementary Figure 14 Genetic interactions captured through the use of dCas9–KRAB–MeCP2.

    (a) A density plot showing negative and positive selection pressure against gRNA pairs over time. (b) Proteins that are in the same complex are expected to have positive genetic interactions, while pairs of proteins in parallel pathways are expected to show a negative genetic interaction and be more distant from each other in a protein complex network. The negative control (Neg.) and dCas9-KRAB samples failed to capture this expected behavior, and their pi-scores (quantifying genetic interactions) were smaller in general. However, the dCas9-KRAB-MeCP2 samples demonstrated a wider range of pi-scores, and these were moderately consistent with the expected behavior. Specifically, gene pairs that were more closely connected in the protein complex network tended to show more positive genetic interactions. (c) A permutation test was used to assess the significance of this correlation. Specifically, the genetic interaction scores and protein complex network distances were multiplied for each gene pair, and then all such scores were summed across all genetic interactions. This was repeated 10000 times after randomizing the gene identifiers. Only the dCas9-KRAB-MeCP2 showed a significant trend. (permutation p-value: 0.8654, 0.53, and 0.041 for Neg., dCas9-KRAB, and dCas9-KRAB-MeCP2, respectively) (d) The hierarchical clustered heatmap of genetic interactions for negative control (wild-type HAP1 without repressor). (e) Resulting network diagram derived from dCas9-KRAB-MeCP2 screening data.

  15. Supplementary Figure 15 Superiority of dCas9–KRAB–MeCP2 in regulating synthetic circuits.

    (a) When paired with dCas9-KRAB-MeCP2, gRNA expressed from a U6 promoter shows improved repression of EYFP over those paired with dCas9 or dCas9-KRAB. (b) In a two-tier repressor cascade comprised of a gRNA repressing a TALER which in turn inhibits EYFP expression, dCas9-KRAB-MeCP2 improves the transfer of information. While dCas9 and dCas9-KRAB repressors propagate some of the signal through the circuit, dCas9-KRAB-MeCP2 increases signal fidelity, making the full circuit indistinguishable from an unrepressed EYFP. (c) When expressed from a doxycycline inducible Pol II promoter and edited by Csy4, gRNA showed improved repression of EYFP when co-expressed with dCas9-KRAB-MeCP2, relative to dCas9 or dCas9-KRAB. For a-c, n = 4 biologically independent samples (cell cultures). Data are presented as mean ± s.e.m. One-sided Student T-test was performed for all statistical comparison. # p < 0.05 vs. unrepressed or TALER only control, ¥ p < 0.05 v.s. dCas9, and *p < 0.05 v.s. dCas9-KRAB.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figs. 1–15, Supplementary Tables 1–13 and Supplementary Notes 1–5

  2. Reporting Summary

  3. Supplementary Data 1

    A list of differentially expressed genes considered significant at FDR < 0.05 in the RNA-seq experiment

  4. Supplementary Data 2

    A list of all sgRNA sequences in the single guide RNA library and their log2 odds ratios in the HAP1 lethality screen

  5. Supplementary Data 3

    A list of all sgRNA sequences in the single guide RNA library and their log2 odds ratios in the SH-SY5Y lethality screen

  6. Supplementary Data 4

    A list of all sgRNA sequences in the single guide RNA library and their log2 odds ratios in the HEK293T lethality screen

  7. Supplementary Data 5

    A list of non-essential gene-targeting sgRNAs that showed depletion in lethality screens

  8. Supplementary Data 6

    Summary of rank-ordered genes identified from sgRNA enrichment analysis carried out with MAGeCK software

  9. Supplementary Data 7

    Genetic interactions captured through repressor screens

  10. Supplementary Data 8

    DNA sequences and species origins of all protein domains used to construct the different repressors in this study

  11. Source Data, Figure 1

  12. Source Data, Figure 2

  13. Source Data, Figure 6

About this article

Publication history