Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning

Wang, Daqi; Zhang, Chengdong; Wang, Bei; Li, Bin; Wang, Qiang; Liu, Dong; Wang, Hongyan; Zhou, Yan; Shi, Leming; Lan, Feng; Wang, Yongming

doi:10.1038/s41467-019-12281-8

Cited by 211 publications

(304 citation statements)

References 54 publications

(68 reference statements)

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“…Although more specific, these variants suffer from reduced cutting rates when compared to wild type Cas9. 9,10,24,29 Using the same set of gRNAs and pNT-15, we compared the killing efficiency of wild type and high fidelity Cas9 variants with and without the D1135E mutation.…”

Section: Reducing the Non-target Pool Increases On-target Activity Wimentioning

confidence: 99%

CRISPR/Cas “non-target” sites inhibit on-target cutting rates

Moreb¹,

Hutmacher²,

Lynch³

2020

Preprint

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CRISPR/Cas systems have become ubiquitous for genome editing in eukaryotic as well as bacterial systems. Cas9 associated with a guide RNA (gRNA) searches DNA for a matching sequence (target site) next to a protospacer adjacent motif (PAM) and once found, cuts the DNA.The number of PAM sites in the genome are effectively a non-target pool of inhibitory substrates, competing with the target site for the Cas9/gRNA complex. We demonstrate that increasing the number of non-target sites for a given gRNA reduces on-target activity in a dose dependent manner. Furthermore, we show that the use of Cas9 mutants with increased PAM specificity towards a smaller subset of PAMs (or smaller pool of competitive substrates) improves cutting rates. Decreasing the non-target pool by increasing PAM specificity provides a path towards improving on-target activity for slower high fidelity Cas9 variants. These results demonstrate the importance of competitive non-target sites on Cas9 activity and, in part, may help to explain sequence and context dependent activities of gRNAs. Engineering improved PAM specificity to reduce the competitive non-target pool offers an alternative strategy to engineer Cas9 variants with increased specificity and maintained on-target activity.

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Section: Reducing the Non-target Pool Increases On-target Activity Wimentioning

confidence: 99%

CRISPR/Cas “non-target” sites inhibit on-target cutting rates

Moreb¹,

Hutmacher²,

Lynch³

2020

Preprint

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“…Empirical essential sgRNAs were selected based on inducing a viability phenotype, empirical nonessential sgRNAs based on the absence of a toxic phenotype. (F-H) Calculated sequence scores applying either the rule set 2 (Doench et al 2016) , the DeepHF (Wang et al 2019) or the Hart et al algorithms. Score performance of the HD CRISPR sub-libraries A and B was benchmarked against the libraries whose design is based on respective scores (Brunello for rule set 2, TKOv3 for Hart et al) if available as well as the GeCKOv2 library and a random sample of sgRNAs from published libraries.…”

Section: Figure 2: Empirically Selected Sgrnas In the Hd Crispr Libramentioning

confidence: 99%

“…For scoring, we applied the rule set 2 design rules, based on which the Brunello library was designed (Doench et al 2016) , as well as the sequence score developed for the design of the TKOv3 library . As an independent metric, we also evaluated sgRNAs using the more recently published DeepHF score (Wang et al 2019) , an sgRNA activity prediction score based on deep learning algorithms, which has not set the basis for the design of either of the evaluated libraries. For each of the selected scores, the two HD CRISPR sub-libraries outperformed the GeCKOv2 library and the randomly picked…”

Section: Figure 2: Empirically Selected Sgrnas In the Hd Crispr Libramentioning

confidence: 99%

“…Ruleset 2 scores were then calculated using the previously published software (Doench et al 2016) . Similarly, DeepHF scores were computed using published software (Wang et al 2019) according to the instructions on the corresponding GitHub page ( https://github.com/izhangcd/DeepHF ). Finally, optimized sgRNA scores according to Hart et al were calculated based on the position weight matrix published with the corresponding manuscript .…”

Section: Calculation Of Sgrna Sequence Scoresmentioning

confidence: 99%

“…Furthermore, higher knockout efficiency is likely to increase the dynamic range of CRISPR screens, which in turn further allows to separate weak signals from screening noise and thus hit calling. In recent years, the design of sgRNAs and CRISPR libraries was improved based on design rules derived from comparing the nucleotide composition of active and non-active guides (Chen et al 2018;Tzelepis et al 2016;Hart et al 2017) , training models to identify predictive features of efficiently editing sgRNAs (Doench et al 2014(Doench et al , 2016 and more recently also deep learning algorithms (Wang et al 2019) . While these efforts have greatly improved library performance, we reasoned that sequence predictions might not always fully reflect knockout performance and that many factors that influence sgRNA efficacy likely remain unknown and have thus not been considered in previous library designs.…”

Section: Introductionmentioning

confidence: 99%

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Pooled CRISPR screening at high sensitivity with an empirically designed sgRNA library

Henkel

Rauscher

Schmitt

et al. 2020

Preprint

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Given their broad utility in functionally annotating genomes, the experimental design of genome-scale CRISPR screens can vary greatly and criteria for optimal experimental implementation and library composition are still emerging. In this study, we report advantages of conducting viability screens in selected Cas9 single cell clones in contrast to Cas9 bulk populations. We further systematically analyzed published CRISPR screens in human cells to identify single-guide (sg)RNAs with consistent high on-target and low off-target activity. Selected guides were collected in a new genome-scale sgRNA library, which efficiently identifies core and context-dependent essential genes. In summary, we show how empirically designed libraries in combination with an optimised experimental design increase the dynamic range in gene essentiality screens at reduced library coverage.

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Manipulating and studying gene function in human pluripotent stem cell models

Balmas,

Sozza,

Bottini

et al. 2023

FEBS Letters

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Human pluripotent stem cells (hPSCs) are uniquely suited to study human development and disease and promise to revolutionize regenerative medicine. These applications rely on robust methods to manipulate gene function in hPSC models. This comprehensive review aims to both empower scientists approaching the field and update experienced stem cell biologists. We begin by highlighting challenges with manipulating gene expression in hPSCs and their differentiated derivatives, and relevant solutions (transfection, transduction, transposition, and genomic safe harbor editing). We then outline how to perform robust constitutive or inducible loss‐, gain‐, and change‐of‐function experiments in hPSCs models, both using historical methods (RNA interference, transgenesis, and homologous recombination) and modern programmable nucleases (particularly CRISPR/Cas9 and its derivatives, i.e., CRISPR interference, activation, base editing, and prime editing). We further describe extension of these approaches for arrayed or pooled functional studies, including emerging single‐cell genomic methods, and the related design and analytical bioinformatic tools. Finally, we suggest some directions for future advancements in all of these areas. Mastering the combination of these transformative technologies will empower unprecedented advances in human biology and medicine.

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Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning

Cited by 211 publications

References 54 publications

CRISPR/Cas “non-target” sites inhibit on-target cutting rates

CRISPR/Cas “non-target” sites inhibit on-target cutting rates

Pooled CRISPR screening at high sensitivity with an empirically designed sgRNA library

Manipulating and studying gene function in human pluripotent stem cell models

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