NEW YORK (GenomeWeb) – A US-UK research team combined machine learning with a multiplexed single-guide RNA (sgRNA) expression strategy that promotes the functional ablation of single genes and allows for combinatorial targeting in a bid to design and construct a genome-wide, sequence-verified, arrayed CRISPR library.
The University of Cambridge, Cold Spring Harbor Laboratory, Cedars-Sinai Medical Institute, and New York Genome Center researchers reported today in Molecular Cell that this resource allows for single-target or combinatorial genetic screens to be carried out at scale in a multiplexed or arrayed format. They also conducted parallel loss-of-function screens to compare their approach to existing sgRNA design and expression strategies.
"Genetic screens have played a fundamental role in charting genotype-phenotype interaction maps for a variety of organisms. However, confounding factors, such as non-uniformity in the efficacies of targeting molecules, have limited the depth of the interpretations of the data from such studies," the authors wrote. "These problems have been somewhat mitigated for short hairpin RNA-based gene silencing because, after several rounds of optimization, experimentally validated algorithms for selecting potent guide sequences have been developed."
But while similar approaches have been applied for selecting Cas9 sgRNAs for use with CRISPR, they added, Cas9-induced double-strand breaks (DSBs) leave a "genomic scar whose characteristics determine the phenotypic consequences of targeting a locus." Non-homologous end joining (NHEJ) was thought to repair Cas9-induced DSBs, but deep sequencing has revealed that some homologous end joining (HEJ) contributes to repair of Cas9 cleavage events.
Further, the authors noted, sgRNAs that focus Cas9 to functional domains provide a greater probability that the Cas9 enzyme will have an impact on phenotype, and that CRISPR knockout assays can be made more efficient through the implementation of optimized effector expression strategies. "Systems have been developed in which multiple RNA polymerase III promoters drive independent sgRNAs," they wrote. "Alternatively, others have shown that Cpf1 can be focused to multiple targets in cells that express crRNA arrays harboring independent sgRNAs."
But while these tools have primarily been used to characterize combinatorial gene interactions and to delete non-coding sequences, the researchers speculated that they could also be used in studies where single gene knockouts are desired in each cell.
They developed an sgRNA selection algorithm that identifies putative targets based on predictive nucleotide combinations, the likelihood of a frame shift mutation, and whether the target lies in a functional domain. They also developed an effector delivery system that allows for the simultaneous expression of two independent sgRNAs from each construct, and developed a computational algorithm that optimizes the likelihood of synergistic deleterious effects.
They then applied these tools to various datasets for training and assessment.
"We have demonstrated that a significant gain in efficacy is attained when two independent sgRNAs simultaneously focus Cas9 to the target gene. Thus, we have designed the library such that two sgRNAs with high prediction scores are expressed from each construct," the team concluded. "An added benefit of this strategy is that constructs can be easily manipulated to target gene pairs to interrogate synthetic interactions. This feature will be particularly useful for identifying parallel or related molecular pathways with combinatorial screens."