Canonical CRISPR–knockout (KO) screens rely on Cas9-induced DNA double-strand breaks (DSBs) to generate targeted gene KOs. These methodologies may yield distorted results because DSB-associated effects are often falsely assumed to be consequences of gene perturbation itself, especially when high copy-number sites are targeted. In the present study, we report a DSB-independent, genome-wide CRISPR screening method, termed iBARed cytosine base editing-mediated gene KO (BARBEKO). This method leverages CRISPR cytosine base editors for genome-scale KO screens by perturbing gene start codons or splice sites, or by introducing premature termination codons. Furthermore, it is integrated with iBAR, a strategy we devised for improving screening quality and efficiency. By constructing such a cell library through lentiviral infection at a high multiplicity of infection (up to 10), we achieved efficient and accurate screening results with substantially reduced starting cells. More importantly, in comparison with Cas9-mediated fitness screens, BARBEKO screens are no longer affected by DNA cleavage-induced cytotoxicity in HeLa-, K562- or DSB-sensitive retinal pigmented epithelial 1 cells. We anticipate that BARBEKO offers a valuable tool to complement the current CRISPR–KO screens in various settings.
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The source code for sgRNA library design can be accessed from https://bitbucket.org/WeiLab/barbeko_sgrna_design/src/master. The ZFC algorithm has been implemented by Python 3 and can be downloaded from https://github.com/wolfsonliu/zfc.
Chang, N. et al. Genome editing with RNA-guided Cas9 nuclease in Zebrafish embryos. Cell Research 23, 465–472 (2013).
Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
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).
Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).
Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
Zhang, L. & Zhou, Q. CRISPR/Cas technology: a revolutionary approach for genome engineering. Sci. China Life Sci. 57, 639–640 (2014).
Koike-Yusa, H., Li, Y., Tan, E.-P., Velasco-Herrera, M. D. C. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).
Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
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).
Zhou, Y. et al. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells. Nature 509, 487–491 (2014).
Shen, Z. & Ou, G. CRISPR–Cas9 knockout screening for functional genomics. Sci. China Life Sci. 57, 733–734 (2014).
Aguirre, A. J. et al. Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting. Cancer Discov. 6, 914–929 (2016).
Fortin, J.-P. et al. Multiple-gene targeting and mismatch tolerance can confound analysis of genome-wide pooled CRISPR screens. Genome Biol. 20, 21 (2019).
Gonçalves, E. et al. Structural rearrangements generate cell-specific, gene-independent CRISPR-Cas9 loss of fitness effects. Genome Biol. 20, 27 (2019).
Munoz, D. M. et al. CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Discov. 6, 900–913 (2016).
Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).
Shrivastav, M., De Haro, L. P. & Nickoloff, J. A. Regulation of DNA double-strand break repair pathway choice. Cell Res. 18, 134–147 (2008).
Bowden, A. R. et al. Parallel CRISPR–Cas9 screens clarify impacts of p53 on screen performance. eLife 9, e55325 (2020).
Haapaniemi, E., Botla, S., Persson, J., Schmierer, B. & Taipale, J. CRISPR–Cas9 genome editing induces a p53-mediated DNA damage response. Nat. Med. 24, 927–930 (2018).
Haapaniemi, E., Botla, S., Persson, J., Schmierer, B. & Taipale, J. Reply to ‘CRISPR screens are feasible in TP53 wild‐type cells’. Mol. Syst. Biol. 15, e8679 (2019).
Ihry, R. J. et al. p53 inhibits CRISPR–Cas9 engineering in human pluripotent stem cells. Nat. Med. 24, 939–946 (2018).
Brown, K. R., Mair, B., Soste, M. & Moffat, J. CRISPR screens are feasible in TP 53 wild‐type cells. Mol. Syst. Biol. 15, e71 (2019).
Peng, J., Zhou, Y., Zhu, S. & Wei, W. High-throughput screens in mammalian cells using the CRISPR–Cas9 system. FEBS J. 282, 2089–2096 (2015).
Zhu, S. et al. Guide RNAs with embedded barcodes boost CRISPR-pooled screens. Genome Biol. 20, 20 (2019).
Billon, P. et al. CRISPR-mediated base editing enables efficient disruption of eukaryotic genes through induction of STOP codons. Mol. Cell 67, 1068–1079.e4 (2017).
Kuscu, C. et al. CRISPR-STOP: gene silencing through base-editing-induced nonsense mutations. Nat. Methods 14, 710–712 (2017).
Koblan, L. W. et al. Improving cytidine and adenine base editors by expression optimization and ancestral reconstruction. Nat. Biotechnol. 36, 843–846 (2018).
Bradley, K. A., Mogridge, J., Mourez, M., Collier, R. J. & Young, J. A. T. Identification of the cellular receptor for anthrax toxin. Nature 414, 225–229 (2001).
Wei, W., Lu, Q., Chaudry, G. J., Leppla, S. H. & Cohen, S. N. The LDL receptor-related protein LRP6 mediates internalization and lethality of anthrax toxin. Cell 124, 1141–1154 (2006).
Kolde, R., Laur, S., Adler, P. & Vilo, J. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics 28, 573–580 (2012).
Hart, T., Brown, K. R., Sircoulomb, F., Rottapel, R. & Moffat, J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol. Syst. Biol. 10, 733–733 (2014).
Sanson, K. R. et al. Optimized libraries for CRISPR–Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).
Dempster, J. M. et al. Agreement between two large pan-cancer CRISPR–Cas9 gene dependency data sets. Nat. Commun. 10, 1–14 (2019).
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).
Liu, Y. et al. Multi-omic measurements of heterogeneity in HeLa cells across laboratories. Nat. Biotechnol. 37, 314–322 (2019).https://doi.org/10.1038/s41587-019-0037-y
Wu, S. Q. et al. Extensive amplification of bcr/abl fusion genes clustered on three marker chromosomes in human leukemic cell line K-562. Leukemia 9, 858–862 (1995).
Iorio, F. et al. Unsupervised correction of gene-independent cell responses to CRISPR–Cas9 targeting. BMC Genomics 19, 604 (2018).
Enache, O. M. et al. Cas9 activates the p53 pathway and selects for p53-inactivating mutations. Nat. Genet. 52, 748–749 (2020).
Geisinger, J. M. & Stearns, T. CRISPR/Cas9 treatment causes extended TP53-dependent cell cycle arrest in human cells. Nucleic Acids Res. 48, 9067–9081 (2020).
Drainas, A. P. et al. Genome-wide screens implicate loss of cullin ring ligase 3 in persistent proliferation and genome instability in TP53-deficient cells. Cell Rep. 31, 107465 (2020).
Noordermeer, S. M. et al. The shieldin complex mediates 53BP1-dependent DNA repair. Nature 560, 117–121 (2018).
Olivieri, M. et al. A genetic map of the response to DNA damage in human cells. Cell 182, 481–496.e21 (2020).
Bodnar, A. G. et al. Extension of life-span by introduction of telomerase into normal human cells. Science 279, 349–352 (1998).
Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).
Dang, L. et al. Comparison of gene disruption induced by cytosine base editing-mediated iSTOP with CRISPR/Cas9-mediated frameshift. Cell Prolif. 53, e12820 (2020).
Ma, S., Meng, Z., Chen, R. & Guan, K.-L. The Hippo pathway: biology and pathophysiology. Annu. Rev. Biochem. 88, 577–604 (2019).
Park, H. W. et al. Alternative Wnt signaling activates YAP/TAZ. Cell 162, 780–794 (2015).
Yu, F.-X., Zhao, B. & Guan, K.-L. Hippo pathway in organ size control, tissue homeostasis, and cancer. Cell 163, 811–828 (2015).
Sheng, Y. et al. Molecular recognition of p53 and MDM2 by USP7/HAUSP. Nat. Struct. Mol. Biol. 13, 285–291 (2006).
Pannunzio, N. R., Watanabe, G. & Lieber, M. R. Nonhomologous DNA end-joining for repair of DNA double-strand breaks. J. Biol. Chem. 293, 10512–10523 (2018).
Chang, H. H. Y., Pannunzio, N. R., Adachi, N. & Lieber, M. R. Non-homologous DNA end joining and alternative pathways to double-strand break repair. Nat. Rev. Mol. Cell Biol. 18, 495–506 (2017).
Brenneman, M. A., Wagener, B. M., Miller, C. A., Allen, C. & Nickoloff, J. A. XRCC3 controls the fidelity of homologous recombination: roles for XRCC3 in late stages of recombination. Mol. Cell 10, 387–395 (2002).
Rössig, L. et al. Akt-dependent phosphorylation of p21Cip1 regulates PCNA binding and proliferation of endothelial cells. Mol. Cell. Biol. 21, 5644–5657 (2001).
Zhou, B. P. et al. Cytoplasmic localization of p21 Cip1/WAF1 by Akt-induced phosphorylation in HER-2/neu-overexpressing cells. Nat. Cell Biol. 3, 245–252 (2001).
Kreis, N.-N., Louwen, F. & Yuan, J. The multifaceted p21 (Cip1/Waf1/CDKN1A) in cell differentiation, migration and cancer therapy. Cancers 11, 1220 (2019).
Sikder, R. K. et al. Differential effects of clinically relevant N- versus C-terminal truncating CDKN1A mutations on cisplatin sensitivity in bladder cancer. Mol. Cancer Res. https://doi.org/10.1158/1541-7786.MCR-19-1200 (2020).
Doench, J. G. Am I ready for CRISPR? A user’s guide to genetic screens. Nat. Rev. Genet. 19, 67–80 (2018).
Shalem, O., Sanjana, N. E. & Zhang, F. High-throughput functional genomics using CRISPR–Cas9. Nat. Rev. Genet. 16, 299–311 (2015).
Cheng, T.-L. et al. Expanding C–T base editing toolkit with diversified cytidine deaminases. Nat. Commun. 10, 3612 (2019).
Huang, T. P. et al. Circularly permuted and PAM-modified Cas9 variants broaden the targeting scope of base editors. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0134-y (2019).
Jiang, W. et al. BE-PLUS: a new base editing tool with broadened editing window and enhanced fidelity. Cell Res. 28, 855–861 (2018).
Kim, Y. B. et al. Increasing the genome-targeting scope and precision of base editing with engineered Cas9–cytidine deaminase fusions. Nat. Biotechnol. 35, 371–376 (2017).
Gehrke, J. M. et al. An APOBEC3A–Cas9 base editor with minimized bystander and off-target activities. Nat. Biotechnol. 36, 977–982 (2018).
Grünewald, J. et al. CRISPR DNA base editors with reduced RNA off-target and self-editing activities. Nat. Biotechnol. 37, 1041–1048 (2019).
Li, X. et al. Base editing with a Cpf1–cytidine deaminase fusion. Nat. Biotechnol. 36, 324–327 (2018).
Rees, H. A., Wilson, C., Doman, J. L. & Liu, D. R. Analysis and minimization of cellular RNA editing by DNA adenine base editors. Sci. Adv. 5, eaax5717 (2019).
Wang, X. et al. Efficient base editing in methylated regions with a human APOBEC3A-Cas9 fusion. Nat. Biotechnol. 36, 946–949 (2018).
Wang, X. et al. Cas12a base editors induce efficient and specific editing with low DNA damage response. Cell Rep. 31, 107723 (2020).
Colic, M. et al. Identifying chemogenetic interactions from CRISPR screens with drugZ. Genome Med. 11, 52 (2019).
This work was supported by funds from the National Science Foundation of China (grant nos. NSFC31930016 to W.W. and NSFC31870893 to Z.Z.), Beijing Municipal Science & Technology Commission (grant no. Z181100001318009), and the Beijing Advanced Innovation Center for Genomics at Peking University, the Peking–Tsinghua Center for Life Sciences (to W.W.). We thank the staff of the BIOPIC High-throughput Sequencing Center (Peking University), H. Lyu, L. Du and H. Yang of the National Center for Protein Sciences (Beijing) at Peking University for technical assistance, and the High-Performance Computing Platform at Peking University for NGS data analysis. We thank Y. Sun and D. Xu (Peking University) for providing hTERT RPE1 and RPE1-TP53KO-Cas9 cell lines.
The authors declare no competing interests.
Peer review information Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Effect of ANTXR1 deficiency by AncBE4max on PA/LFnDTA-triggered cytotoxicity in HeLa cells.
a, Schematic indicates sgRNA targeting sites at ANTXR1 genomic locus. b, Images of HeLa cells with or without PA/LFnDTA treatment for 48 hours after AncBE4max editing with indicated sgRNAs. The results shown are from one group of sgRNA transfected HeLa cells and conducted in triplicates with individual PA/LFnDTA toxin treatment. Scale bar: 100 μm. c, Sanger sequencing chromatograms of sgRNA-targeting ANTXR1 genomic fragments of PA/LFnDTA toxin resistant cells, black arrows indicate peaks of targeted cytosines and their editing results. d, C-to-T editing frequency of indicated sgRNAs targeting ANTXR1 in HeLa cells detected by sanger sequencing. Sorting of the sgRNA-expressing cells was conducted 2 days post-transduction (denoted as day 0), and cells were harvested on days 0, 3 and 6. The green lines indicated the editing frequency of targeted cytosine for gene knockouts, and the other blank lines indicated the editing frequency of cytosine locating in the activity windows of AncBE4max.
Extended Data Fig. 2 Comparing knockout efficiency between AncBE4max and Cas9 by targeting ribosomal genes on cell proliferation.
a, sgRNAStop targeting HBEGF, sgRNAStart targeting ANTXR1 and sgRNAAAVS1 served as negative controls. b, Effects of indicated sgRNAs targeting ribosomal gene RPL23A on cell proliferation in K562 cells by AncBE4max (left) and Cas9 (right). Data are presented as the mean ± s.d. of 3 independent experiments. P values represent comparisons with sgRNAAAVS1 at the endpoint (day 18) using a one-tailed Student’s t-test and adjusted using the Benjamini–Hochberg method. **p < 0.01; ***p < 0.001. c, Editing efficiency of AncBE4max with indicated sgRNAs targeting RPL23A detected by sanger sequencing. sgRNA-expressing cells were sorted on 2 days post transduction (denoted as day 0) and cells were harvested daily until day 6. The colored lines indicated the conversion efficiency of targeted cytosine for gene knockouts and the other blank lines indicated the conversion efficiency of cytosine locating in the activity windows of AncBE4max (the same with f). d, Editing efficiency of Cas9 with indicated sgRNAs targeting RPL23A were detected by sanger sequencing. e, Effects of indicated sgRNAs targeting ribosomal gene RPL11 on cell proliferation in K562 cells by AncBE4max (left) and Cas9 (right). f, Editing efficiency of AncBE4max with indicated sgRNAs targeting RPL11 detected by sanger sequencing. g, Editing efficiency of Cas9 with indicated sgRNAs targeting RPL11 were detected by sanger sequencing.
a, Schematic shows the scaffold sequence of sgRNAiBAR, in which 4 iBARs employed in BARBEKO library are highlighted in red. b, Pie chart shows the composition of BARBEKO library that newly designed sgRNAStart and sgRNAs targeting splice sites (sgRNASD and sgRNASA) account for 2.5% and 39.3% respectively, and sgRNAStop introduced from Kuscu et al. account for 58.2%.
Extended Data Fig. 4 Comparisons of BARBEKO screening with CRISPR-KO results and comparisons of depleted hits of BARBEKO between timepoints in HeLa cells.
a-b, ROC analysis for screens in HeLa cells based on reference gene sets with 513 (a) and 662 (b) essential genes. c, Boxplots showing the distribution of gene FS of 349 essential, 703 non-essential and other genes in conventional CRISPR-KO, CRISPRiBAR and BARBEKO screening. Boxplots are represented as follows: center line indicating the median, box limits indicating the upper and lower quartiles, whiskers indicating the 1.5x interquartile range and all other observed points plotting as outliers. d, Scatter plot of sgRNAiBAR ZLFC of two biological replicates on day 15, Pearson correlation coefficient is indicated on the top. sgRNAsiBAR targeting AAVS1 locus and non-targeting sgRNAsiBAR as negative controls are labelled in purple and green. e, Scatter plot of gene Fitness Score (FS) on day 15 of two biological replicates, Pearson correlation coefficient is indicated on the top. f, Scatter plot of gene FS of day 15 and day 21, Pearson correlation coefficient is indicated on the top. g, Venn diagram shows the numbers of common and different depleted hits of day 15 and day 21. h, Gene Ontology (GO) analysis of common and day 21-only selected hits. GO terms are ranked from top to bottom based on P value of day 21 results using Metascape. Blue bars represent the numbers of commonly depleted hits and red bars represent the numbers of day 21-only selected hits in each GO terms.
a, Efficiency comparison across 3 types of sgRNAs, sgRNAStart targeting start codons, sgRNASD/SA targeting splice sites and sgRNAStop targeting codons of Gln (CAA, CAG), Arg (CGA) and Trp (TGG). b, Efficiency comparison between sgRNASA targeting splice acceptor sites and sgRNASD targeting splice donor sites. c, Editing efficiency comparison across 4 types (A, C, G, T) of 5’ context of sgRNA-targeted cytosine. d, Editing efficiency comparison across locations of sgRNA-targeted cytosine in AncBE4max editing window. e, Efficiency comparison across sgRNAStop targeting CAA, CAG, TGG and CGA. Boxplots are represented as follows: center line indicating the median, box limits indicating the upper and lower quartiles and whiskers indicating the 1.5x interquartile range. The numbers of sgRNAs in each category are indicated above the corresponding boxplots.
Extended Data Fig. 6 Editing kinetics and effect on cell proliferation by AncBE4max or Cas9 targeting high-copy loci in HeLa cells.
a and d, C-to-T editing frequency of sgRNAs targeting high-copy-number SDHA (a) and TRIP13 (d) loci in HeLa cells detected by sanger sequencing. Sorting of the sgRNA-expressing cells was conducted 2 days post-transduction (denoted as day 0), and cells were harvested on days 0, 3 and 6. The green lines indicated the editing frequency of targeted cytosine for gene knockouts, and the other blank lines indicated the editing frequency of cytosine locating in the activity windows of AncBE4max. b, Schematic showing the genomic region of a highly amplified gene TRIP13 and the targeting sites of sgRNAs selected from BARBEKO (sgRNAStop-1 and sgRNAStop-2) or TKO (sgRNA-7 and sgRNA-8) libraries. c, Effects of indicated sgRNAs targeting TRIP13 on cell proliferation in HeLa cells. 4 sgRNAs were individually delivered into AncBE4max- and Cas9-expressing cells for validation. Data are presented as the mean ± s.d. of 3 independent experiments. sgRNAAAVS1 served as negative control. P values represent comparisons with sgRNAAAVS1 at the end point (day 15), and was calculated using a one-tailed Student’s t-test and adjusted using the Benjamini–Hochberg method, ***p < 0.001.
Extended Data Fig. 7 Comparisons of BARBEKO screening in RPE1 cells with CRISPR-KO based on gold-standard reference gene sets and essential GO terms.
a-c, ROC analysis for screens based on reference gene sets with 349 (a), 513 (b) and 662 (c) essential genes. d, Boxplots showing the distribution of gene FS of 349 essential, 703 non-essential and other genes in conventional CRISPR-KO, high-MOI CRISPR-KO or BARBEKO screening in wild-type or TP53-/- RPE1 cells. Boxplots are represented as follows: center line indicating the median, box limits indicating the upper and lower quartiles, whiskers indicating the 1.5x interquartile range and all other observed points plotting as outliers. e, Gene lists were obtained from Gene Ontology, and the numbers of genes are indicated at the top left. Boxplots are represented the same as above. Screening data from publications was re-analyzed by ZFC algorithm for comparisons.
a, Volcano plot showing the overall outcomes of BARBEKO screen in TP53-/- background at a MOI of ~ 3. The top 5 depleted and enriched genes together with top-ranking Hippo genes are labelled. b, Scatter plot showing the distribution of gene rankings of 4 categories. Gene rankings of BARBEKO screens are calculated according to the gene FS from small to large. Essential genes and ribosomal genes are extracted from reference gene sets, while non-targeting and AAVS1 controls are composed of 3 corresponding sgRNAs by randomly sampling. The results are presented as the mean ± s.d., and the mean value of gene rankings of each categories is highlighted in red.
a, Venn diagram showing the numbers of commonly and differently selected hits of BARBEKO and CRISPR-KO screens. b-d, GO enrichment analysis by Metascape of common essential hits in wild-type cells but not in TP53-/- cells (b), unique essential hits of BARBEKO screen in wild-type cells (c) and CRISPR-KO screen in wild-type cells (d). GO terms are ranked by the value of FDR from small to large. The size of circle represents the number of genes belonging to each term.
Extended Data Fig. 10 Perturbations on different sites of the CDKN1A locus caused variant phenotypes.
a, Schematic shows genomic region of CDKN1A and the targeting sites of sgRNAs selected from BARBEKO library (sgRNAStop-1) and newly designed sgRNAs (sgRNAStop2-4 and sgRNASD). b-c, Effects of indicated sgRNAs targeting CDKN1A on cell proliferation in AncBE4max- (b) and Cas9-expressing (c) RPE1 cells. sgRNAAAVS1 served as negative control. Data are presented as the mean ± s.d. of 3 independent experiments. P values represent comparisons with sgRNAAAVS1 at the end point (day 15), calculated using a one-tailed Student’s t-test and adjusted using the Benjamini–Hochberg method, **p < 0.01, ***p < 0.001.
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Xu, P., Liu, Z., Liu, Y. et al. Genome-wide interrogation of gene functions through base editor screens empowered by barcoded sgRNAs. Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-00944-1