Abstract
While well studied in yeast, mapping genetic interactions in mammalian cells has been limited due to many technical obstacles. We have recently developed a new one-step tRNA-CRISPR method called TCGI (tRNA-CRISPR for genetic interactions) which generates high-efficiency, barcode-free, and scalable pairwise CRISPR libraries to identify genetic interactions in mammalian cells. Here we describe this method in detail regarding the construction of the pairwise CRISPR libraries and performing high throughput genetic interacting screening and data analysis. This novel TCGI dramatically improves upon the current methods for mapping genetic interactions and screening drug targets for combinational therapies.
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References
Jia J, Zhu F, Ma X et al (2009) Mechanisms of drug combinations: interaction and network perspectives. Nat Rev Drug Discov 8(2):111–128. https://doi.org/10.1038/nrd2683
Jackson AL, Linsley PS (2010) Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application. Nat Rev Drug Discov 9(1):57–67. https://doi.org/10.1038/nrd3010
Koike-Yusa H, Li Y, Tan EP et al (2014) Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat Biotechnol 32(3):267–273. https://doi.org/10.1038/nbt.2800
Cong L, Ran FA, Cox D et al (2013) Multiplex genome engineering using CRISPR/Cas systems. Science (New York, NY) 339(6121):819–823. https://doi.org/10.1126/science.1231143
Jinek M, Chylinski K, Fonfara I et al (2012) A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science (New York, NY) 337(6096):816–821. https://doi.org/10.1126/science.1225829
Shalem O, Sanjana NE, Hartenian E et al (2014) Genome-scale CRISPR-Cas9 knockout screening in human cells. Science (New York, NY) 343(6166):84–87. https://doi.org/10.1126/science.1247005
Meyers RM, Bryan JG, McFarland JM et al (2017) Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat Genet 49(12):1779–1784. https://doi.org/10.1038/ng.3984
Yu JSL, Yusa K (2019) Genome-wide CRISPR-Cas9 screening in mammalian cells. Methods 164–165:29–35. https://doi.org/10.1016/j.ymeth.2019.04.015
Du D, Roguev A, Gordon DE et al (2017) Genetic interaction mapping in mammalian cells using CRISPR interference. Nat Methods 14(6):577–580. https://doi.org/10.1038/nmeth.4286
Wong AS, Choi GC, Cui CH et al (2016) Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM. Proc Natl Acad Sci U S A 113(9):2544–2549. https://doi.org/10.1073/pnas.1517883113
Erard N, Knott SRV, Hannon GJ (2017) A CRISPR resource for individual, combinatorial, or multiplexed gene knockout. Mol Cell 67(6):1080. https://doi.org/10.1016/j.molcel.2017.08.027
Han K, Jeng EE, Hess GT et al (2017) Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat Biotechnol 35(5):463–474. https://doi.org/10.1038/nbt.3834
Hill AJ, McFaline-Figueroa JL, Starita LM et al (2018) On the design of CRISPR-based single-cell molecular screens. Nat Methods 15(4):271–274. https://doi.org/10.1038/nmeth.4604
Xie K, Minkenberg B, Yang Y (2015) Boosting CRISPR/Cas9 multiplex editing capability with the endogenous tRNA-processing system. Proc Natl Acad Sci U S A 112(11):3570–3575. https://doi.org/10.1073/pnas.1420294112
Dong F, Xie K, Chen Y et al (2017) Polycistronic tRNA and CRISPR guide-RNA enables highly efficient multiplexed genome engineering in human cells. Biochem Biophys Res Commun 482(4):889–895. https://doi.org/10.1016/j.bbrc.2016.11.129
Port F, Bullock SL (2016) Augmenting CRISPR applications in Drosophila with tRNA-flanked sgRNAs. Nat Methods 13(10):852–854. https://doi.org/10.1038/nmeth.3972
Doench JG, Fusi N, Sullender M et al (2016) Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol 34(2):184–191. https://doi.org/10.1038/nbt.3437
Ewels P, Magnusson M, Lundin S et al (2016) MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32(19):3047–3048. https://doi.org/10.1093/bioinformatics/btw354
Warde-Farley D, Donaldson SL, Comes O et al (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38(Web Server issue):W214–W220. https://doi.org/10.1093/nar/gkq537
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. https://doi.org/10.1101/gr.1239303
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Zhang, D.Y., Gui, X., Yang, X. (2021). Mapping Genetic Interactions in Human Cancer Cells Using a One-Step tRNA-CRISPR System. In: Vizeacoumar, F.J., Freywald, A. (eds) Mapping Genetic Interactions. Methods in Molecular Biology, vol 2381. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1740-3_9
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DOI: https://doi.org/10.1007/978-1-0716-1740-3_9
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