CRISPR-Cas9 vs. DNA: A knockout

rna polymerase II

Editor's Introduction

Genome-scale CRISPR-Cas9 knockout screening in human cells

annotated by
Justin German Ian Ahrens Regg Strotheide Ann Buchmann Shelby J. Lake

In recent years, techniques for "silencing" genes have been refined for use in genome modification. One of these techniques uses the CRISPR-Cas9 system, a simplified version of a bacterial defense mechanism. The authors of this study sought to compare the efficiency and reliability of the CRISPR-Cas9 system to the older RNA interference (RNAi) technique. To do so, they constructed a genome-wide CRISPR-Cas9 knockout library and used it to screen melanoma cells for genes that give resistance to a cancer treatment drug. By comparing the results of their CRISPR-Cas9 screen with a similar screen using RNAi, they demonstrated the power and potential of CRISPR.

Paper Details

Original title
Genome-scale CRISPR-Cas9 knockout screening in human cells
Neville Sanjana Ophir Shalem
Original publication date
Vol. 343, Issue 6166, pp.84-87
Issue name


The simplicity of programming the CRISPR (clustered regularly interspaced short palindromic repeats)–associated nuclease Cas9 to modify specific genomic loci suggests a new way to interrogate gene function on a genome-wide scale. We show that lentiviral delivery of a genome-scale CRISPR-Cas9 knockout (GeCKO) library targeting 18,080 genes with 64,751 unique guide sequences enables both negative and positive selection screening in human cells. First, we used the GeCKO library to identify genes essential for cell viability in cancer and pluripotent stem cells. Next, in a melanoma model, we screened for genes whose loss is involved in resistance to vemurafenib, a therapeutic RAF inhibitor. Our highest-ranking candidates include previously validated genes NF1 and MED12, as well as novel hits NF2CUL3TADA2B, and TADA1. We observe a high level of consistency between independent guide RNAs targeting the same gene and a high rate of hit confirmation, demonstrating the promise of genome-scale screening with Cas9.


A major goal since the completion of the Human Genome Project is the functional characterization of all annotated genetic elements in normal biological processes and disease (1). Genome-scale loss-of-function screens have provided a wealth of information in diverse model systems (25). In mammalian cells, RNA interference (RNAi) is the predominant method for genome-wide loss-of-function screening (23), but its utility is limited by the inherent incompleteness of protein depletion by RNAi and confounding off-target effects (67).

The RNA-guided CRISPR (clustered regularly interspaced short palindrome repeats)–associated nuclease Cas9 provides an effective means of introducing targeted loss-of-function mutations at specific sites in the genome (89). Cas9 can be programmed to induce DNA double-strand breaks (DSBs) at specific genomic loci (89) through a synthetic single-guide RNA (sgRNA) (10), which when targeted to coding regions of genes can create frame shift insertion/deletion (indel) mutations that result in a loss-of-function allele. Because the targeting specificity of Cas9 is conferred by short guide sequences, which can be easily generated at large scale by array-based oligonucleotide library synthesis (11), we sought to explore the potential of Cas9 for pooled genome-scale functional screening.

Lentiviral vectors are commonly used for delivery of pooled short-hairpin RNAs (shRNAs) in RNAi because they can be easily titrated to control transgene copy number and are stably maintained as genomic integrants during subsequent cell replication (21213). Therefore, we designed a single lentiviral vector to deliver Cas9, a sgRNA, and a puromycin selection marker into target cells (lentiCRISPR) (Fig. 1A). The ability to simultaneously deliver Cas9 and sgRNA through a single vector enables application to any cell type of interest, without the need to first generate cell lines that express Cas9.

figure 1

Fig. 1. Lentiviral delivery of Cas9 and sgRNA provides efficient depletion of target genes. (A) Lentiviral expression vector for Cas9 and sgRNA (lentiCRISPR). puro, puromycin selection marker; psi+, psi packaging signal; RRE, rev response element; cPPT, central polypurine tract; EFS, elongation factor-1α short promoter; P2A, 2A self-cleaving peptide; WPRE, posttranscriptional regulatory element. (B) Distribution of fluorescence from 293T-EGFP cells transduced by EGFP-targeting lentiCRISPR (sgRNAs 1 to 6, outlined peaks) and Cas9-only (green-shaded peak) vectors, and nonfluorescent 293T cells (gray shaded peak). (C) Distribution of fluorescence from 293T-EGFP cells transduced by EGFP-targeting shRNA (shRNAs 1 to 4, outlined peaks) and control shRNA (green-shaded peak) vectors, and nonfluorescent 293T cells (gray shaded peak).


How does knockout target specificity using the sgRNA/lentiCRISPR system compare to RNAi using shRNA?

Panel B

The sgRNA/lentiCRISPR system completely knocked out expression of the target genes, leading to no EGFP fluorescence. 

Panel C

The peaks for each shRNA knockout are shifted slightly to the left, but because the peaks do not overlap with the gray shaded peak representing nonfluorescent cells, we know that there is still some fluorescence. 


These results indicate that lentiCRISPR is more effective than RNAi at targeting genes for knockout. In Figure 2, the authors demonstrate this by testing a lentiCRISPR library against a genome.

To determine the efficacy of gene knockout by lentiCRISPR transduction, we tested six sgRNAs targeting enhanced green fluorescent protein (EGFP) in a human embryonic kidney (HEK) 293T cell line containing a single copy of EGFP (fig. S1). After transduction at a low multiplicity of infection (MOI = 0.3) followed by selection with puromycin, lentiCRISPRs abolished EGFP fluorescence in 93 ± 8% (mean ± SD) of cells after 11 days (Fig. 1B). Deep sequencing of the EGFP locus revealed a 92 ± 9% indel frequency (n ≥ 104 sequencing reads per condition) (fig. S2). In contrast, transduction of cells with lentiviral vectors expressing EGFP-targeting shRNA led to incomplete knockdown of EGFP fluorescence (Fig. 1C).

Given the high efficacy of gene knockout by lentiCRISPR, we tested the feasibility of conducting genome-scale CRISPR-Cas9 knockout (GeCKO) screening with a pooled lentiCRISPR library. We designed a library of sgRNAs targeting 5′ constitutive exons (Fig. 2A) of 18,080 genes in the human genome with an average coverage of 3 to 4 sgRNAs per gene (table S1), and each target site was selected to minimize off-target modification (14) (see supplementary text).

figure 2

Fig. 2. GeCKO library design and application for genome-scale negative selection screening. (A) Design of sgRNA library for genome-scale knockout of coding sequences in human cells (see supplementary text). (B and C) Cumulative frequency of sgRNAs 3 and 14 days after transduction in A375 and human embryonic stem cells, respectively. Shift in the 14-day curve represents the depletion in a subset of sgRNAs. (D and E) The five most significantly depleted gene sets in A375 cells [nominal P < 10−5, false discovery rate (FDR)–corrected q < 10−5] and HUES62 cells (nominal P < 10−5, FDR-corrected q < 10−3) identified by GSEA (15).


This figure details how the authors designed their GeCKO library.

Panel A

A visual overview of the design of the library. The authors first designed specific sgRNAs targeting 18,080 genes with an average coverage of 3 to 4 sgRNAs per gene.

To understand how well the library worked, they looked the decrease over time in the number of sgRNAs that targeted survival genes (because if a sgRNA knocks out a gene that is essential to survival, the cell will die).

Panels B and C

These graphs show a shift in the cumulative frequency of genes at 3 and 14 days. The shift to the left in the red (Day 14) lines between Panels B and C represents a decrease in the number of specific sgRNAs that targeted survival genes.


The nature of the five gene sets that saw the most significant depletion after the sgRNA knockout suggests that most of the depleted sgRNAs were genes that are essential to survival. 

To test the efficacy of the full GeCKO library at achieving knockout of endogenous gene targets, we conducted a negative selection screen by profiling the depletion of sgRNAs targeting essential survival genes (Fig. 2A). We transduced the human melanoma cell line A375 and the human stem cell line HUES62 with the GeCKO library at a MOI of 0.3. As expected, deep sequencing (figs. S3 and S4) 14 days after transduction revealed a significant reduction in the diversity of sgRNAs in the surviving A375 and HUES62 cells (Fig. 2, B and C) (Wilcoxon rank sum test, P < 10−10 for both cell types). Gene set enrichment analysis (GSEA) (15) indicated that most of the depleted sgRNAs targeted essential genes such as ribosomal structural constituents (Fig. 2, D and E, and tables S2 and S3). The overlap in highly depleted genes and functional gene categories between the two cell lines (fig. S5) indicates that GeCKO can identify essential genes and that enrichment analysis of depleted sgRNAs can pinpoint key functional gene categories in negative selection screens.

To test the efficacy of GeCKO for positive selection, we sought to identify gene knockouts that result in resistance to the BRAF protein kinase inhibitor vemurafenib (PLX) in melanoma (16) (Fig. 3A). Exposure to PLX resulted in growth arrest of transduced A375 cells, which harbor the V600E gain-of-function BRAF mutation (17) (Fig. 3B), therefore enabling the enrichment of a small group of cells that were rendered drug-resistant by Cas9:sgRNA-mediated modification. After 14 days of PLX treatment, the sgRNA distribution was significantly different when compared with vehicle-treated cells (Fig. 3C) (Wilcoxon rank-sum test, P < 10−10) and clustered separately from all other conditions (Fig. 3D and fig. S6).

figure 3

Fig. 3. GeCKO screen in A375 melanoma cells reveals genes whose loss confers PLX resistance. (A) Timeline of PLX resistance screen in A375 melanoma cells. (B) Growth of A375 cells when treated with dimethyl sulfoxide (DMSO) or PLX over 14 days. (C) Box plot showing the distribution of sgRNA frequencies at different time points, with and without PLX treatment (vehicle is DMSO). The box extends from the first to the third quartile with the whiskers denoting 1.5 times the interquartile range. Enrichment of specific sgRNAs: 7 days of PLX treatment, 1 sgRNA greater than 10-fold enrichment; 14 days of PLX treatment, 379 and 49 sgRNAs greater than 10-fold and 100-fold enrichment, respectively. (D) Rank correlation of normalized sgRNA read count between biological replicates and treatment conditions. (E) Scatterplot showing enrichment of specific sgRNAs after PLX treatment. (F) Identification of top candidate genes using the RIGER P value analysis.


Can GeCKO be used to determine which genes confer resistance to PLX, a drug used to treat melanoma?

Panel B

PLX inhibits growth of A375 melanoma cells that have the V600E gain-of-function BRAF mutation. Figure 3B compares cell growth in PLX-treated cells and those without. PLX inhibits most but not all cell growth; the authors isolated cells that were resistant to PLX for use in their GeCKO screen.

Panels C and D

These panels show how the distribution of sgRNAs differed from the control. After 14 days, 379 of the sgRNAs tested has been enriched greater than 10-fold and 49 had been enriched greater than 100-fold. Panel D shows that these sgRNAs “clustered” together, meaning they were similar.

Panels E and F

Panel E shows the enrichment of specific sgRNAs, and reveals that for some genes, there were multiple enriched sgRNAs. This suggests that the loss of these genes would lead to PLX resistance.

Using an algorithm, the authors identified the genes most likely contributing to PLX resistance. Their results included not only genes that have been previously reported to convey PLX resistance but those that have not been implicated before.

For a subset of genes, we found enrichment of multiple sgRNAs that target each gene after 14 days of PLX treatment (Fig. 3E), suggesting that loss of these particular genes contributes to PLX resistance. We used the RNAi Gene Enrichment Ranking (RIGER) algorithm to rank screening hits by the consistent enrichment among multiple sgRNAs targeting the same gene (Fig. 3F and table S4) (12). Our highest-ranking genes included previously reported candidates NF1 and MED12 (1819) and also several genes not previously implicated in PLX resistance, including neurofibromin 2 (NF2), Cullin 3 E3 ligase (CUL3), and members of the STAGA histone acetyltransferase complex (TADA1 and TADA2B). These candidates yield new testable hypotheses regarding PLX resistance mechanisms (see supplementary text). For example, NF1 and NF2, although unrelated in sequence, are each mutated in similar but distinct forms of neurofibromatosis (20). In addition, epigenetic dysregulation resulting from mutations in the mechanistically related STAGA and Mediator complexes (21) may have a role in acquired drug resistance. All of these hits were also identified through a second independent library transduction (figs. S7 and S8 and tables S5 and S6).

A similar screen to identify PLX drug resistance in A375 cells was previously conducted using a pooled library of 90,000 shRNAs (19). To compare the efficacy and reliability of genome-scale shRNA screening with GeCKO, we used several methods to evaluate the degree of consistency among the sgRNAs or shRNAs targeting the top candidate genes. First, we plotted the P values for the top 100 hits using either RIGER (Fig. 4A) or redundant siRNA activity (RSA) (fig. S9) scoring. Lower P values for the GeCKO versus shRNA screen indicate better scoring consistency among sgRNAs. Second, for the top 10 RIGER hit genes, 78 ± 27% of sgRNAs targeting each gene ranked among the top 5% of enriched sgRNAs, whereas 20 ± 12% of shRNAs targeting each gene ranked among the top 5% of enriched shRNAs (Fig. 4B).

figure 4

Fig. 4. Comparison of GeCKO and shRNA screens and validation of neurofibromin 2 (NF2). (A) RIGER P values for the top 100 hits from GeCKO and shRNA (19) screens for genes whose loss results in PLX resistance. Analysis using the RSA algorithm shows a similar trend (fig. S9). (B) For the top 10 RIGER hits, the percent of unique sgRNAs (top) or shRNAs (bottom) targeting each gene that are in the top 5% of all enriched sgRNAs or shRNAs. (C) Deep-sequencing analysis of lentiCRISPR-mediated indel at the NF2 locus. (D) A375 cells transduced with NF2-targeting lentiCRISPR and shRNA vectors both show a decrease in NF2 protein levels. (E) Dose-response curves for A375 cells transduced with individual NF2-targeting lentiCRISPR or shRNA vectors. Controls were EGFP-targeting lentiCRISPR or null-hairpin shRNA vectors. Cells transduced with NF2-targeting lentiCRISPRs show a significant increase (F1,8 = 30.3, P < 0.001, n = 4 replicates) in the half-maximal effective concentration (EC50), whereas cells transduced with NF2-targeting shRNA vectors do not (F1,8 = 0.47, P = 0.51, n = 4 replicates).


Is the shRNA screen or the GeCKO screen more efficient and reliable?

The authors used several methods to compare their sgRNA (GeCKO) screen to a previously-conducted shRNA screen that used a library of 90,000 shRNAs.

Panels A and B

Using the RIGER algorithm, the authors plotted the efficiency of the two screens in Panel A. Specifically, they looked at sgRNAs and shRNAs targeting neurofibromin 2 (NF2). Lower P values for the GeCKO screen indicates that the sgRNAs were more consistent than shRNAs.

Panel B compares the top 10 RIGER hits for each screen and shows that, on average, the GeCKO screen was more efficient at identifying and enriching sgRNAs targeting the genes of interest.

Panel C

Deep-sequencing confirmed that for one of the candidate genes 4 out of 5 sgRNAs mutated NF2 alleles at a very high rate (over 99% after 14 days).

Panels D and E

In Panel D, the authors compared to what extent the sgRNAs and shRNAs reduced NF2 expression. In Panel E, they compared PLX resistance using the two methods.

Cells transduced with sgRNAs showed a significant increase in PLX resistance. However, only one of the shRNAs achieved sufficient knockdown of NF2 to increase PLX resistance. This suggests that even a small amount of NF2 expression may be enough to retain PLX sensitivity.

We validated top-ranking genes from the GeCKO screen individually using 3 to 5 sgRNAs (Fig. 4, C to E, and figs. S10 and S11). For NF2, we found that 4 out of 5 sgRNAs resulted in >98% allele modification 7 days after transduction, and all 5 sgRNAs showed >99% allele modification 14 days after transduction (Fig. 4C). We compared sgRNA and shRNA-mediated protein depletion and PLX resistance using Western blot (Fig. 4D) and cell growth assays (Fig. 4E). Interestingly, although all five sgRNAs conferred resistance to PLX, only the best shRNA achieved sufficient knockdown to increase PLX resistance (Fig. 4E), suggesting that even low levels of NF2 are sufficient to retain sensitivity to PLX. Additionally, sgRNAs targeting NF1MED12CUL3TADA1, and TADA2B led to a decrease in protein expression and increased resistance to PLX (figs. S10 and S11). Deep sequencing confirmed a high rate of mutagenesis at targeted loci (figs. S12 and S13), with a small subset of off-target sites exhibiting indels (figs. S14 to S16), which may be alleviated using an offset nicking approach (2223) that was recently shown to reduce off-target modifications (22).

GeCKO screening provides a mechanistically distinct method from RNAi for systematic perturbation of gene function. Whereas RNAi reduces protein expression by targeting RNA, GeCKO introduces loss-of-function mutations into genomic DNA. Although some indel mutations are expected to maintain the reading frame, homozygous knockout yields high screening sensitivity, which is especially important in cases where incomplete knockdown retains gene function. In addition, RNAi is limited to transcripts, whereas Cas9:sgRNAs can target elements across the entire genome, including promoters, enhancers, introns, and intergenic regions. Furthermore, catalytically inactive mutants of Cas9 can be tethered to different functional domains (2327) to broaden the repertoire of perturbation modalities, including genome-scale gain-of-function screening using Cas9 activators and epigenetic modifiers. In the GeCKO screens presented here, the efficiency of complete knockout, the consistency of distinct sgRNAs, and the high validation rate for top screen hits demonstrate the potential of Cas9:sgRNA-based technology to transform functional genomics.

Supplementary Materials

Materials and Methods

Supplementary Text

Figs. S1 to S16

Tables S1 to S10


References and Notes

  1. E. S. Lander, Nature 470, 187–197 (2011).
  2. K. Berns et al., Nature 428, 431–437 (2004).
  3. M. Boutros et al., Heidelberg Fly Array Consortium, Science 303, 832–835 (2004).
  4. R. Rad et al., Science 330, 1104–1107 (2010).
  5. J. E. Carette et al., Science 326, 1231–1235 (2009).
  6. A. L. Jackson et al., RNA 12, 1179–1187 (2006).
  7. C. J. Echeverri et al., Nat. Methods 3, 777–779 (2006).
  8. L. Cong et al., Science 339, 819–823 (2013).
  9. P. Mali et al., Science 339, 823–826 (2013).
  10. M. Jinek et al., Science 337, 816–821 (2012).
  11. A. P. Blanchard, L. Hood, Nat. Biotechnol. 14, 1649 (1996).
  12. B. Luo et al., Proc. Natl. Acad. Sci. U.S.A. 105, 20380–20385 (2008).
  13. P. J. Paddison et al., Nature 428, 427–431 (2004).
  14. P. D. Hsu et al., Nat. Biotechnol. 31, 827–832 (2013).
  15. A. Subramanian et al., Proc. Natl. Acad. Sci. U.S.A. 102, 15545–15550 (2005).
  16. K. T. Flaherty et al., N. Engl. J. Med. 363, 809–819 (2010).
  17. H. Davies et al., Nature 417, 949–954 (2002).
  18. S. Huang et al., Cell 151, 937–950 (2012).
  19. S. R. Whittaker et al., Cancer Discovery 3, 350–362 (2013).
  20. A. L. Lin, D. H. Gutmann, Nat. Rev. Clin. Oncol. 10, 616–624 (2013).
  21. X. Liu, M. Vorontchikhina, Y. L. Wang, F. Faiola, E. Martinez, Mol. Cell. Biol. 28, 108–121 (2008).
  22. F. A. Ran et al., Cell 154, 1380–1389 (2013).
  23. P. Mali et al., Nat. Biotechnol. 31, 833–838 (2013).
  24. L. A. Gilbert et al., Cell 154, 442–451 (2013).
  25. S. Konermann et al., Nature 500, 472–476 (2013).
  26. P. Perez-Pinera et al., Nat. Methods 10, 973–976 (2013).
  27. M. L. Maeder et al., Nat. Methods 10, 977–979 (2013).

Acknowledgments: We thank G. Cowley, W. Harrington, J. Wright, E. Hodis, S. Whittaker, J. Merkin, C. Burge, D. Peters, C. Cowan, L. P. Club, and the entire Zhang laboratory for technical support and critical discussions. O.S. is a Klarman Family Foundation Fellow, N.S. is a Simons Center for the Social Brain Postdoctoral Fellow, D.A.S. is an NSF Fellow, and J.D. is a Merkin Institute Fellow. D. H. was funded by the German Cancer Foundation. F.Z. is supported by an NIH Director’s Pioneer Award (1DP1-MH100706); a NIH Transformative R01 grant (1R01-DK097768); the Keck, McKnight, Merkin, Vallee, Damon Runyon, Searle Scholars, Klingenstein, and Simons Foundations; and Bob Metcalfe and Jane Pauley. The authors have no conflicting financial interests. A patent application has been filed relating to this work, and the authors plan to make the reagents widely available to the academic community through Addgene and to provide software tools at the Zhang laboratory Web site (