High-performance CRISPR-Cas12a genome editing for combinatorial genetic screening

Abstract

CRISPR-based genetic screening has revolutionized cancer drug target discovery, yet reliable, multiplex gene editing to reveal synergies between gene targets remains a major challenge. Here, we present a simple and robust CRISPR-Cas12a-based approach for combinatorial genetic screening in cancer cells. By engineering the CRISPR-AsCas12a system with key modifications to the Cas protein and its CRISPR RNA (crRNA), we can achieve high efficiency combinatorial genetic screening. We demonstrate the performance of our optimized AsCas12a (opAsCas12a) through double knockout screening against epigenetic regulators. This screen reveals synthetic sick interactions between Brd9&Jmjd6, Kat6a&Jmjd6, and Brpf1&Jmjd6 in leukemia cells.

Fig. 1. Optimization of an AsCas12a system to improve knockout efficiency in mammalian cells.

a Configuration of the optimized vectors used for lentiviral AsCas12a and CRISPR RNA (crRNA) transduction experiments in mammalian cells. A Puromycin (Puro) resistance gene was used to select AsCas12a-positive cells, and a GFP reporter was linked with crRNA expression to track the crRNA-positive population in cellular competition assays. b Experimental workflow of cellular competition assay to evaluate AsCas12a knockout efficiency. K562 cells were stably transduced with indicated AsCas12a vector, followed by secondary infection with crRNA virus. Flow cytometry-based tracking of the crRNA-positive cell population over a period of time was used to calculate the negative selection phenotype of each indicated crRNA. Cellular competition assay to compare the knockout efficiency of the AsCas12a system with a variable number of NLS sequences (c), with or without an additional direct repeat (DR) 3′ of the crRNA (d), and with an AsCas12a variant (E174R and S542R) (e). Plotted are the crRNA-positive cells (normalized to the day 3 measurement) at the indicated timepoints during culturing. Two crRNAs were designed targeting PCNA, an essential gene for DNA replication. “e” represents the exon. (n = 3). p values are indicated (two-tailed Welch’s unpaired t-test). Error bars smaller than symbol width not shown. All error bars shown represent mean ± SD. Source data are provided as a Source Data file.

Fig. 2. opAsCas12a performs similarly to SpCas9 in domain-focused single-gene knockout screens.

a Experimental schematic of pooled dropout screen. See “Methods” for detailed explanation. A protein domain CRISPR Score (CS) was calculated by averaging the log₂ fold-change of all CRISPR RNA targeting a given protein domain. Fold-change = (final CRISPR RNA abundance + 1)/(initial CRISPR RNA abundance). b Comparative functional knockout efficiency of AsCas12a crRNAs targeting conserved protein domains, nondomain coding regions, and noncoding regions of known AML dependencies and pan-essential genes. RN2c12 cells were lentivirally transduced with a customized pooled library containing 2298 total crRNAs (n indicates the number of crRNA in each crRNA class). Violin plot demonstrates median (thick line, value listed), interquartile (dashed lines), and distribution of guide-wise log₂ fold-change of two replicate screens. p values are indicated (Two-tailed Mann–Whitney U-test). c Individual examples of functional protein domain-targeting crRNAs that lead to a higher proportion of null mutations and an enhanced severity of dropout. The location of each crRNA cleavage site within the BRD4 and HBO1 coding sequences is indicated along the x-axis. Plotted is the mean log₂ fold-change value of two biological replicates (n = 2). crRNAs targeting inside or outside of conserved protein domains are represented by green or blue bars, respectively. BD=bromodomain, ET=extraterminal domain, CTD= C-terminal domain, ZF=zinc finger, HAT=histone acetyltransferase. d The result of pooled opAsCas12a dropout screen to identify epigenetic dependencies in RN2c12 cells. A customized opAsCas12a crRNA library against 155 protein domains involved in epigenetic regulation was constructed. crRNAs were designed to target epigenetic regulatory protein domains, yielding 787 crRNAs total including positive and negative controls. RN2c12 or RN2c9 cells were transduced with pooled opAsCas12a crRNA or SpCas9 sgRNA libraries. Scatter plot that compares the CSs of two independent replicates (r = 0.96, Pearson correlation). e Comparison of opAsCas12 and SpCas9 in pooled dropout screen against epigenetic regulators in RN2 cells. Plotted is the average CS of two biological replicates (n = 2). (r = 0.93, Pearson correlation). d, e Red dots label known cancer drug targets in this Mll-Af9 leukemia model. Source data are provided as a Source Data file.

Fig. 3. opAsCas12-based combinatorial knockout screen in Mll-Af9 leukemia identifies synthetic sick/lethal interaction of epigenetic regulators.

a Cloning strategy to construct pairwise combinatorial AsCas12a dual-crRNA library. Experimental workflow to construct a customized dual-crRNA library. Twenty-one domains of epigenetic regulators with moderate-to-no proliferative phenotype in RN2 cells were selected from the single-gene knockout screens performed in Fig. 2. A dual-crRNA library with 8281 pairwise combinations was generated using a simple one-step T4 ligation protocol. The screen was performed in RN2c12 cells, and the abundance of crRNA pairs was quantified by directly deep sequencing the dual-crRNA expression cassette. b An opAsCas12a-based double knockout screen reveals synthetic sick/lethal epigenetic regulator pairs in RN2c12 cells. Scatter plot compares the expected and observed dual-crRNA CSs. The expected dual-crRNA CS was calculated based on a Gaussian distribution model of the experimental crRNAs in all possible combinations with negative control crRNAs. The observed dual-crRNA CS was calculated based on a Gaussian distribution model of all crRNA combinations of the two experimental protein domains. Blue dots represent the [negative crRNA]–[negative crRNA] control pairs and red dots represent potential synthetic sick/lethal genetic interactions (log₂ differential > 2.5, qval < 0.05). c Cellular competition assay in RN2c12 cells to validate the screen identified potential synergistic interacting dual-crRNAs. Plotted are the dual-crRNA-positive cells (normalized to the day 2 measurement) at the indicated timepoints during culturing. Each data point is comprised of pairwise combinations of the two indicated crRNAs. p values are indicated (two-tailed Welch’s unpaired t-test). (n = 3). d Viability of RN2c12 cells transduced with either Jmjd6 or Rosa26 crRNA and exposed to increasing concentrations of either dBrd9 or WM-1119 for 5 days. dBrd9 is a selective BRD9 degrader and WM-1119 is a KAT6A/B inhibitor. (n = 3–5). e Venn diagram of RNA-seq data depicting the overlap of significantly upregulated and downregulated genes upon double knockout with indicated dual-crRNAs in RN2c12 cells. The second crRNA in the dual-crRNA cassette for single-crRNA samples targets the negative control Rosa26 locus. Error bars smaller than symbol width not shown. All error bars shown represent mean ± SD. Source data are provided as a Source Data file.