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. 2017 Jun;14(6):573-576.
doi: 10.1038/nmeth.4225. Epub 2017 Mar 20.

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions

Affiliations

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions

John Paul Shen et al. Nat Methods. 2017 Jun.

Abstract

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies.

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Conflict of interest statement

Competing financial interests. All authors report no competing financial interests.

Figures

Figure 1
Figure 1
Experimental & analytical framework for identification of genetic interactions with combinatorial CRISPR knockout. (A) Schematic of overall experimental approach: Array-based oligonucleotide synthesis is used to create dual-gRNA libraries containing all gene-gene (double gene perturbation) and gene-scramble (single gene perturbation) combinations, which can then be assayed for effects on cell growth. (B) Schematic of computational analysis workflow: CRISPR screens are run as two independent replicate experiments, with cells harvested at four time points and gRNA frequencies determined by high throughput sequencing. All gRNAs below a threshold (red dash) are excluded from further analysis. Fitness is determined from a fit of log relative abundance over time; probes are subsequently ranked by absolute fitness and weighted, then a numerical Bayesian method is used to test for presence of a genetic interaction.
Figure 2
Figure 2. Genetic interactions in HeLa, A549 and 293T cancer cells
(A) Genes selected for study included tumor suppressor genes (TSG) and cancer relevant drug targets (DT), which included many oncogenes. (B) Scatterplot of fitness of single gene knockout (KO) in HeLa vs. A549. (C) Scatterplot of interaction scores in HeLa vs. A549, using the smoothScatter R function with default settings. Density of gene pairs at each x.y location is represented by darkness of blue shading; single gene pairs in low density regions are marked by black dots. (D) Proportional Venn diagram summarizing the number of synthetic lethal interactions per cell line and the number conserved between each cell line pair. (E) Combined synthetic-lethal network for all three cell lines. Circles indicate TSG, squares DTs. Node colors indicate single gene knockout fitness effect, red: positive fitness effect, blue: negative fitness effect. Black edge around node indicates that the protein product of the gene is the target of an FDA approved drug. Color of edge indicates the cell line in which the interaction was identified, blue: HeLa, red: A549, green: 293T. Black edges were identified in multiple cell lines.

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References

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