CRISPRscan: designing highly efficient sgRNAs for CRISPR/Cas9 targeting in vivo

By M. Moreno-Mateos, Charles E. Vejnar, Jean-Denis Beaudoin, Juan P. Fernández, Emily Mis, M. Khokha, Antonio J. Giráldez. Nature Methods, 2015

1,173 citations recorded by the source corpus.

CRISPR-Cas9 technology provides a powerful system for genome engineering. However, variable activity across different single guide RNAs (sgRNAs) remains a significant limitation. We analyzed the molecular features that influence sgRNA stability, activity and loading into Cas9 in vivo. We observed that guanine enrichment and adenine depletion increased sgRNA stability and activity, whereas differential sgRNA loading, nucleosome positioning and Cas9 off-target binding were not major determinants. We also identified sgRNAs truncated by one or two nucleotides and containing 5′ mismatches as efficient alternatives to canonical sgRNAs. On the basis of these results, we created a predictive sgRNA-scoring algorithm, CRISPRscan, that effectively captures the sequence features affecting the activity of CRISPR-Cas9 in vivo. Finally, we show that targeting Cas9 to the germ line using a Cas9-nanos 3′ UTR led to the generation of maternal-zygotic mutants, as well as increased viability and decreased somatic mutations. These results identify determinants that influence Cas9 activity and provide a framework for the design of highly efficient sgRNAs for genome targeting in vivo.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b977fb99889d4e7fcc57218e545aea69dd9704fb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR (2016, methodology): “Without constant expression of the guide from a plasmid, the stability of guide RNA starts to play a bigger role [39].”
The UCSC Genome Browser database: 2018 update (2017, positive): “The list of scores provided includes a guide specificity score, an out-of-frame score (3) and efficiency scores (4,5).”
DeepCRISPR: optimized CRISPR guide RNA design by deep learning (2018, background): “These methods are categorized into three types: (1) alignment-based, where the sgRNAs are aligned from the given genome purely by locating the PAM ( CasFinder [8], etc.); (2) hypothesis-driven, where the sgRNA knockout efficacies are scored empirically by considering the impact of genome context fac”
Selenium Utilization by GPX4 Is Required to Prevent Hydroperoxide-Induced Ferroptosis. (2017, neutral): “org/ (Moreno-Mateos et al., 2015) and cloned into the ecotropic lentiviral pKLV-U6gRNA(Bbs1)-PGKpuro2ABFP vector (Addgene) using BbsI restriction sites.”
CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering (2016, background): “Moreno-Mateos et al . conducted similar screens and found that sgRNA stability, which depends on guanine enrichment and adenine depletion, was a major determinant of sgRNA efficiency (12).”
Delivering CRISPR: a review of the challenges and approaches (2018, background): “As an example, the PAM from Neisseria meningitidis Cas9 is reported to be 50-NNNNGATT-30 (Jiang et al., 2013; Ma et al., 2014).”