A next-generation dual guide CRISPR system for genetic interaction library screening

a-next-generation-dual-guide-crispr-system-for-genetic-interaction-library-screening
A next-generation dual guide CRISPR system for genetic interaction library screening

Data availability

The library design and screening analysis data have been deposited in the Zenodo database with the identifier https://doi.org/10.5281/zenodo.1719195144. The raw screen data is available in the Figshare database with the identifier https://doi.org/10.6084/m9.figshare.25533091.v1. The raw sequencing data is available in the EBI European Nucleotide Archive accession number ERP183979 and are available at the following URL https://www.ebi.ac.uk/ena/browser/view/ERP183979. The editing efficiency and validation data generated in this study are provided in the Supplementary Information and Source data file. Source data are provided with this paper.

Code availability

The code used to generate analysis has been deposited in the Zenodo database (https://doi.org/10.5281/zenodo.17191951)44,45 under a Creative Commons Attribution 4.0 International license (CC BY 4.0). Additional information for the following analyses can be found on the links below. Pilot library design: https://github.com/EmanuelGoncalves/crispy/blob/master/notebooks/dualguide/Library2Composition.py Genetic interaction library design: https://github.com/ibarrioh/DualGuide_COLO1/ Screen analysis: https://github.com/ibarrioh/DualGuide_COLO1/.

References

  1. Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature 568, 511–516 (2019).

    Google Scholar 

  2. Chan, E. M. et al. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature 568, 551–556 (2019).

    Google Scholar 

  3. Adikusuma, F., Pfitzner, C. & Thomas, P. Q. Versatile single-step-assembly CRISPR/Cas9 vectors for dual gRNA expression. PLoS One 12, e0187236 (2017).

    Google Scholar 

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

    Google Scholar 

  5. Erard, N., Knott, S. R. V. & Hannon, G. J. A CRISPR Resource for Individual, Combinatorial, or Multiplexed Gene Knockout. Mol. Cell 67, 348–354.e4 (2017).

    Google Scholar 

  6. Vidigal, J. A. & Ventura, A. Rapid and efficient one-step generation of paired gRNA CRISPR-Cas9 libraries. Nat. Commun. 6, 8083 (2015).

    Google Scholar 

  7. Thompson, N. A. et al. Combinatorial CRISPR screen identifies fitness effects of gene paralogues. Nat. Commun. 12, 1302 (2021).

    Google Scholar 

  8. Diehl, V. et al. Minimized combinatorial CRISPR screens identify genetic interactions in autophagy. Nucleic Acids Res 49, 5684–5704 (2021).

    Google Scholar 

  9. Hill, A. J. et al. On the design of CRISPR-based single-cell molecular screens. Nat. Methods 15, 271–274 (2018).

    Google Scholar 

  10. Hegde, M., Strand, C., Hanna, R. E. & Doench, J. G. Uncoupling of sgRNAs from their associated barcodes during PCR amplification of combinatorial CRISPR screens. PLoS One 13, e0197547 (2018).

    Google Scholar 

  11. Najm, F. J. et al. Orthologous CRISPR-Cas9 enzymes for combinatorial genetic screens. Nat. Biotechnol. 36, 179–189 (2018).

    Google Scholar 

  12. DeWeirdt, P. C. et al. Optimization of AsCas12a for combinatorial genetic screens in human cells. Nat. Biotechnol. 39, 94–104 (2021).

    Google Scholar 

  13. Li, R. et al. Comparative optimization of combinatorial CRISPR screens. Nat. Commun. 13, 2469 (2022).

    Google Scholar 

  14. Gier, R. A. et al. High-performance CRISPR-Cas12a genome editing for combinatorial genetic screening. Nat. Commun. 11, 1–9 (2020).

    Google Scholar 

  15. Kim, H. K. et al. Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nat. Biotechnol. 36, 239–241 (2018).

    Google Scholar 

  16. Kleinstiver, B. P. et al. Engineered CRISPR-Cas12a variants with increased activities and improved targeting ranges for gene, epigenetic and base editing. Nat. Biotechnol. 37, 276–282 (2019).

    Google Scholar 

  17. Zetsche, B. et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell 163, 759–771 (2015).

    Google Scholar 

  18. Port, F. & Bullock, S. L. Augmenting CRISPR applications in Drosophila with tRNA-flanked sgRNAs. Nat. Methods 13, 852–854 (2016).

    Google Scholar 

  19. Xie, K., Minkenberg, B. & Yang, Y. Boosting CRISPR/Cas9 multiplex editing capability with the endogenous tRNA-processing system. Proc. Natl. Acad. Sci. Usa. 112, 3570–3575 (2015).

    Google Scholar 

  20. Dong, F., Xie, K., Chen, Y., Yang, Y. & Mao, Y. Polycistronic tRNA and CRISPR guide-RNA enables highly efficient multiplexed genome engineering in human cells. Biochem. Biophys. Res. Commun. 482, 889–895 (2017).

    Google Scholar 

  21. Zhao, Y. et al. A one-step tRNA-CRISPR system for genome-wide genetic interaction mapping in mammalian cells. Sci. Rep. 9, 14499 (2019).

    Google Scholar 

  22. Knapp, D. J. H. F. et al. Decoupling tRNA promoter and processing activities enables specific Pol-II Cas9 guide RNA expression. Nat. Commun. 10, 1490 (2019).

    Google Scholar 

  23. Yuan, Q. & Gao, X. Multiplex base- and prime-editing with drive-and-process CRISPR arrays. Nat. Commun. 13, 2771 (2022).

    Google Scholar 

  24. Tzelepis, K. et al. A CRISPR Dropout Screen Identifies Genetic Vulnerabilities and Therapeutic Targets in Acute Myeloid Leukemia. Cell Rep. 17, 1193–1205 (2016).

    Google Scholar 

  25. Han, K. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35, 463 (2017).

    Google Scholar 

  26. Heo, S.-J. et al. Compact CRISPR genetic screens enabled by improved guide RNA library cloning. Genome Biol. 25, 25 (2024).

    Google Scholar 

  27. Walton, R. T., Qin, Y. & Blainey, P. C. CROPseq-multi: a versatile solution for multiplexed perturbation and decoding in pooled CRISPR screens. bioRxiv https://doi.org/10.1101/2024.03.17.585235 (2024).

  28. Gonçalves, E. et al. Minimal genome-wide human CRISPR-Cas9 library. Genome Biol. 22, 40 (2021).

    Google Scholar 

  29. Xie, S., Cooley, A., Armendariz, D., Zhou, P. & Hon, G. C. Frequent sgRNA-barcode recombination in single-cell perturbation assays. PLoS One 13, e0198635 (2018).

    Google Scholar 

  30. Feldman, D., Singh, A., Garrity, A. J. & Blainey, P. C. Lentiviral co-packaging mitigates the effects of intermolecular recombination and multiple integrations in pooled genetic screens. bioRxiv 262121 https://doi.org/10.1101/262121 (2018).

  31. Adamson, B., Norman, T. M., Jost, M. & Weissman, J. S. Approaches to maximize sgRNA-barcode coupling in Perturb-seq screens. bioRxiv 298349 https://doi.org/10.1101/298349 (2018)

  32. Zamanighomi, M. et al. GEMINI: a variational Bayesian approach to identify genetic interactions from combinatorial CRISPR screens. Genome Biol. 20, 137 (2019).

    Google Scholar 

  33. Dede, M., McLaughlin, M., Kim, E. & Hart, T. Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens. Genome Biol. 21, (2020).

  34. Morgens, D. W. et al. Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nat. Commun. 8, 15178 (2017).

    Google Scholar 

  35. Anzalone, A. V. et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature https://doi.org/10.1038/s41586-019-1711-4 (2019).

  36. Tian, R. et al. CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons. Neuron 104, 239–255.e12 (2019).

    Google Scholar 

  37. Replogle, J. M. et al. Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat. Biotechnol. 38, 954–961 (2020).

    Google Scholar 

  38. Nuñez, J. K. et al. Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing. Cell 184, 2503–2519.e17 (2021).

    Google Scholar 

  39. Replogle, J. M. et al. Maximizing CRISPRi efficacy and accessibility with dual-sgRNA libraries and optimal effectors. https://doi.org/10.7554/eLife.81856 (2022).

  40. Bruntraeger, M., Byrne, M., Long, K. & Bassett, A. R. Editing the Genome of Human Induced Pluripotent Stem Cells Using CRISPR/Cas9 Ribonucleoprotein Complexes. Methods Mol. Biol. 1961, 153–183 (2019).

    Google Scholar 

  41. Pinello, L. et al. Analyzing CRISPR genome-editing experiments with CRISPResso. Nat. Biotechnol. 34, 695–697 (2016).

    Google Scholar 

  42. Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

    Google Scholar 

  43. Hodgkins, A. et al. WGE: a CRISPR database for genome engineering. Bioinformatics 31, 3078–3080 (2015).

    Google Scholar 

  44. Barrio-Hernandez, I. et al. ibarrioh/DualGuide_COLO1: Release2-final (Version Release2), Zenodo, https://doi.org/10.5281/zenodo.17191951 (2025).

  45. Barrio-Hernandez, I. et al. MAPPING.zip dataset, Figshare, https://doi.org/10.6084/m9.figshare.25533091.v1 (2024).

  46. Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Google Scholar 

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

    Google Scholar 

Download references

Acknowledgements

We would like to thank the flow cytometry facility in Sanger Cellular Operations for analysis support and DNA sequencing facility in Sanger Scientific Operations for library construction and sequencing. pSpCas9(BB)−2A-GFP (PX458) was a gift from Feng Zhang (Addgene plasmid # 48138; http://n2t.net/addgene:48138; RRID:Addgene_48138)46. pKLV2-U6gRNA5(BbsI)-PGKpuro2ABFP-W, pKLV2-U6gRNA5(BbsI)PGKpuro-2A-mCherry-W and pKLV2-U6gRNA5(BbsI)PGKpuro-2A-mAG-W were gifts from Kosuke Yusa (Addgene plasmid # 67974, #67976, #67977; http://n2t.net/addgene:67974http://n2t.net/addgene:67976http://n2t.net/addgene:67977; RRID:Addgene_67974 RRID:Addgene_67976 RRID:Addgene_67977)24. We are grateful to Fiona Behan for advice and contributions to experimental design, Russell Walton for helpful comments on the manuscript and members of the Garnett and Bassett labs for their advice, discussions and support. This research was funded in part by the Wellcome Trust 220540/Z/20/A as institute core funding to M.G. and A.B. and OpenTargets OTAR2062. Funding from FCT (Fundação para a Ciência e Tecnologia), under projects UIDB/50021/2020 (DOI:10.54499/UIDB/50021/2020), https://doi.org/10.54499/2024.07252.IACDC (through RE-C05-i08.M04), and https://doi.org/10.54499/LISBOA2030-FEDER-00868200 supported E.G. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Author information

Authors and Affiliations

  1. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK

    Thomas Burgold, Emre Karakoc, Emanuel Gonçalves, Inigo Barrio-Hernandez, Lisa Dwane, Romina Silva, Emily Souster, Mamta Sharma, Alexandra Beck, Gene Ching Chiek Koh, Mathew J. Garnett & Andrew R. Bassett

  2. OpenTargets, Wellcome Genome Campus, Hinxton, Cambridge, UK

    Emre Karakoc, Inigo Barrio-Hernandez, Romina Silva, Emily Souster, Alexandra Beck, Lykourgos-Panagiotis Zalmas, Mathew J. Garnett & Andrew R. Bassett

  3. Instituto Superior Técnico (IST), Universidade de Lisboa, Lisboa, Portugal

    Emanuel Gonçalves

  4. INESC-ID, Lisboa, Portugal

    Emanuel Gonçalves

  5. Instituto de Agrobiotecnología, IdAB, CSIC-Gobierno de Navarra, Mutilva, Spain

    Inigo Barrio-Hernandez

  6. Sir Jeffrey Cheah Sunway Medical School, Faculty of Medical and Life Sciences, 24 Sunway University, Sunway City, Malaysia

    Gene Ching Chiek Koh

  7. Department of Genomic Medicine, University of Cambridge, Cambridge, UK

    Gene Ching Chiek Koh

Authors

  1. Thomas Burgold
  2. Emre Karakoc
  3. Emanuel Gonçalves
  4. Inigo Barrio-Hernandez
  5. Lisa Dwane
  6. Romina Silva
  7. Emily Souster
  8. Mamta Sharma
  9. Alexandra Beck
  10. Gene Ching Chiek Koh
  11. Lykourgos-Panagiotis Zalmas
  12. Mathew J. Garnett
  13. Andrew R. Bassett

Contributions

A.R.B., M.J.G., T.B. and E.G. conceived and designed the study; A.R.B., M.J.G. and L-P.Z. supervised the work; T.B., L.D., R.S., E.S., M.S., A.B., G.C.C.K. performed and interpreted experiments; E.K., E.G., I.B-H. performed and interpreted computational analysis. T.B., I.B-H., E.K., E.G., A.R.B. drafted paper and all authors contributed to editing.

Corresponding author

Correspondence to Andrew R. Bassett.

Ethics declarations

Competing interests

A.B. is a founder of and consultant for Ensocell therapeutics. M.G. is a founder of and consultant for Mosaic Therapeutics, receives research funding from GSK and Astex Pharmaceuticals and is a consultant for Bristol-Myers Squibb. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

Additional information

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

Supplementary information

Source data

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Burgold, T., Karakoc, E., Gonçalves, E. et al. A next-generation dual guide CRISPR system for genetic interaction library screening. Nat Commun (2025). https://doi.org/10.1038/s41467-025-67256-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41467-025-67256-9