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Updated: Feb 21, 2026

CIRCLE-Seq for Interrogation of Off-Target Gene Editing
Published on: November 1, 2024
Crossing enhanced and high fidelity SpCas9 nucleases to optimize specificity and cleavage
Péter István Kulcsár1,2,3, András Tálas1,4, Krisztina Huszár1,2,5
1Institute of Enzymology, Research Centre for Natural Sciences of the Hungarian Academy of Sciences, Budapest, Hungary.
Background:
The propensity for off-target activity of Streptococcus pyogenes Cas9 (SpCas9) has been considerably decreased by rationally engineered variants with increased fidelity (eSpCas9; SpCas9-HF1). However, a subset of targets still generate considerable off-target effects. To deal specifically with these targets, we generated new "Highly enhanced Fidelity" nuclease variants (HeFSpCas9s) containing mutations from both eSpCas9 and SpCas9-HF1 and examined these improved nuclease variants side by side to decipher the factors that affect their specificities and to determine the optimal nuclease for applications sensitive to off-target effects.
Results:
These three increased-fidelity nucleases can routinely be used only with perfectly matching 20-nucleotide-long spacers, a matching 5' G extension being more detrimental to their activities than a mismatching one. HeFSpCas9 exhibit substantially improved specificity for those targets for which eSpCas9 and SpCas9-HF1 have higher off-target propensity. The targets can also be ranked by their cleavability and off-target effects manifested by the increased fidelity nucleases. Furthermore, we show that the mutations in these variants may diminish the cleavage, but not the DNA-binding, of SpCas9s.
Conclusions:
No single nuclease variant shows generally superior fidelity; instead, for highest specificity cleavage, each target needs to be matched with an appropriate high-fidelity nuclease. We provide here a framework for generating new nuclease variants for targets that currently have no matching optimal nuclease, and offer a simple means for identifying the optimal nuclease for targets in the absence of accurate target-ranking prediction tools.
Insights
Highly enhanced fidelity Streptococcus pyogenes Cas9 (SpCas9) variants (HeFSpCas9s) improve specificity for challenging targets. Optimal nuclease selection is crucial for precise gene editing applications sensitive to off-target effects.
Area of Science:
- Molecular Biology
- Biotechnology
- Genetics
Background:
- Engineered variants like eSpCas9 and SpCas9-HF1 reduced off-target activity of Streptococcus pyogenes Cas9 (SpCas9).
- A subset of targets still exhibit significant off-target effects even with high-fidelity SpCas9 variants.
- New Highly enhanced Fidelity SpCas9 (HeFSpCas9) variants were developed by combining mutations from eSpCas9 and SpCas9-HF1.
Purpose of the Study:
- To evaluate HeFSpCas9 variants for improved specificity in gene editing.
- To identify factors influencing nuclease specificity and determine optimal variants for sensitive applications.
- To compare the performance of HeFSpCas9s against existing high-fidelity SpCas9 variants.
Main Methods:
- Generation of HeFSpCas9 nuclease variants incorporating mutations from eSpCas9 and SpCas9-HF1.
- Side-by-side examination of engineered nuclease variants.
- Analysis of target cleavability and off-target effects.
- Assessment of DNA-binding and cleavage activities of SpCas9 variants.
Main Results:
- HeFSpCas9 variants demonstrate substantially improved specificity for targets problematic for eSpCas9 and SpCas9-HF1.
- These high-fidelity nucleases generally require perfectly matching 20-nucleotide spacers for optimal activity.
- Mutations in HeFSpCas9 variants can reduce cleavage but not DNA-binding affinity.
- Targets can be ranked based on cleavability and off-target effects with these nucleases.
Conclusions:
- No single high-fidelity nuclease variant is universally superior; optimal choice depends on the specific target.
- A framework is provided for developing new nuclease variants for challenging targets.
- A method is offered for identifying the best nuclease for a given target without predictive tools.
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