Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform

Abstract

Genetic interactions mediate the emergence of phenotype from genotype, but technologies for combinatorial genetic perturbation in mammalian cells are challenging to scale. Here, we identify background-independent paralog synthetic lethals from previous CRISPR genetic interaction screens, and find that the Cas12a platform provides superior sensitivity and assay replicability. We develop the in4mer Cas12a platform that uses arrays of four independent guide RNAs targeting the same or different genes. We construct a genome-scale library, Inzolia, that is ~30% smaller than a typical CRISPR/Cas9 library while also targeting ~4000 paralog pairs. Screens in cancer cells demonstrate discrimination of core and context-dependent essential genes similar to that of CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes. Importantly, the in4mer platform offers a fivefold reduction in library size compared to other genetic interaction methods, substantially reducing the cost and effort required for these assays.

Fig. 1. Comparative analysis of paralog synthetic lethality screens.

A Different multiplex CRISPR perturbation methods applied to assay paralog synthetic lethality. B Tested paralog pairs in each study. Upset plot shows the intersection of pairs across different studies. C Quantifying synthetic lethality between paralog pairs. Single mutant fitness (SMF) is the mean log fold change of gRNAs that target an individual gene. Expected double mutant fitness (DMF) is calculated as the sum of SMF of gene 1 and gene 2. Delta Log Fold Change (dLFC) is the difference between observed and expected fold change and is used as a measure of genetic interaction. D dLFC vs. Cohen’s D in one data set, A549 screen in Dede. E Comparison of union of hits across all cell lines in each study. F Jaccard coefficient comparing hits across all pairs of cell lines within each study. G The “paralog score” is the weighted sum of hits minus the weighted sum of misses; i.e. where the gene pair is assayed but not a hit. Weights are the median of the platform-level Jaccard coefficients from F. H Histogram of paralog scores of 388 hits across all 5 studies. I Histogram of paralog scores across 26 hits in >1 study. J Thirteen candidate “paralog gold standards” with paralog score >0.25 and hit in more than one study. Source data are provided as a Source Data file.

Fig. 2. Multiplexing beyond 2 guides with Cas12a.

A 7mer arrays were constructed with all combinations of either an essential or non-essential guide at each position (2^7 = 128 species), using the same DR sequences at each position, in three independent sets with unique gRNA sequences targeting the same genes at each position (n = 384 total). B Guide sets were evenly represented in the combined pool before and after packaging and transduction (C) 7mer guide array representation is consistent across replicates and variation is consistent with high quality screens. D Fold change of guide arrays vs. number of essential guides on the array (n = 384 arrays). E Fold change vs. position of essential guide on array, for all arrays encoding one essential guide (six nonessentials). F Fold change of guide arrays encoding one essential per array, forward vs. reverse orientation. Essential guides expressed at positions 6 and 7 deviate from the diagonal, indicating position-specific loss of editing. G A linear regression model that learns single gene knockout effects can be used to predict combinatorial target phenotype, an accurate null model for genetic interactions.

Fig. 3. In4mer platform for whole-genome screening.

A Inzolia human whole-genome library targets single genes and paralog pairs, triples, and quads with arrays of 4 Cas12a gRNA. Each gene or gene family is targeted by two arrays encoding the same gRNA in different order. Commercially synthesized oligo pools are cloned into the one-component pRDA_550 lentiviral vector; schematic created in Biorender. B–E Screening in K-562 CML cells (prototype library), A549 lung cancer cells (prototype library) and A375, MELJUSO melanoma cancer cells (Inzolia library). B Read counts from the plasmid and experimental timepoints after lentiviral transduction. C Correlation of sample read counts. Endpoint replicates are highly correlated. D Fold change distributions of arrays targeting reference essential (red) and non-essential genes (blue) in four cell lines. E Precision/recall analysis from ranked mean fold change of arrays targeting each gene, calculated against reference essential and non-essential genes.

Fig. 4. Paralog synthetic lethality with Inzolia.

A Fold change vs. dLFC for >4000 paralog families in MELJUSO cells. B dLFC vs. Paralog Score from meta-analysis of published paralog screens. Red, Paralog Score > 0.25. Blue, Paralog Score <0.25. Of 12 paralogs with score > 0.25, 9 show dLFC < −1 in MELJUSO cells. C Fold change vs. paralog score in MELJUSO cells, color as in B. Most scored paralogs are essential, regardless of synthetic lethality. D Selected synthetic lethals in MELJUSO cells showing single and double knockout fitness phenotype. Bar chart, mean fold change. Points indicate fold change of single array of gRNA (mean of 2 replicates). E Fraction of synthetic lethal paralogs by amino acid sequence similarity in MELJUSO cells. F Pathway activation by BCR-ABL1 fusion in K-562 cells. Red, essential gene in in4mer screen; blue, non-essential; orange, synthetic lethal paralog pair. G Fraction of dead cells, normalized to controls, for single, double, and triple knockouts of RAS genes in K-562. Two clones were used for each gene/gene combination, and three technical replicates were maintained for each clone, n = 6 for each condition/group. KRAS-NRAS double and KRAS-NRAS-HRAS triple knockout show significantly increased cell death compared to negative control (**P < 0.01, one-way ANOVA). Data represented as mean ± SEM. ARCN1, control essential gene. ADH7, control non-essential gene. H Single, double, and triple knockout phenotype of RTK/MAP kinase pathway genes in all four cell lines. White, target not in library. Green outline, known oncogenic mutation. Source data are provided as a Source Data file.

Fig. 5. Synthetic chemogenetic interactions.

MELJUSO cells were cultured in the presence of MEK inhibitor selumetinib and screened for chemogenetic interactions (A) DrugZ scores of single gene knockouts. Selected genes in the MAPK and Hippo pathways highlighted. Red, synergistic; blue, suppressor. B Selected GSEA results and significance tests (one-sided weighted Kolmogorov–Smirnov statistic) for gene sets conferring sensitivity (ERK signaling) or resistance (Hippo signaling) to MEKi. C Comparing DrugZ scores of paralogs (x-axis) vs. the most extreme Z score of the single gene knockout (y-axis) shows that most pairwise perturbagens yield similar phenotype as singletons. Outliers in red (synergistic) or blue (suppressor). D Synergistic and suppressor paralog knockouts from C. Asterisk indicates functional buffering in the Hippo pathway, masking phenotype in monogenic knockout screens.

Fig. 6. Library size comparison.

A Representative Cas9 and Cas12a whole-genome libraries. Inzolia library targets 19k protein-coding genes and additionally includes 9822 guide arrays targeting paralog doubles, triples, and quads. B Five recent publications screening for genetic interactions between paralogs. Bar plot shows number of reagents per paralog pair tested, including single and double knockouts. C Comparison of library efficiency. Number of paralog pairs tested vs. library size for recent publications. For this plot, Inzolia library only includes paralog doubles (4435), triples (376), quads (100), and plus corresponding single gene knockouts (8870).