Protocol for performing pooled CRISPR-Cas9 loss-of-function screens
Phenotypic screens involving pooled CRISPR-Cas9 libraries offer a powerful, rapid yet affordable approach to evaluate gene functions on a global scale. Here, we present a protocol for performing pooled CRISPR-Cas9 loss-of-function screens to identify genetic modifiers using either fluorescence-based...
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Veröffentlicht in: | STAR protocols 2023-06, Vol.4 (2), p.102201, Article 102201 |
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Sprache: | eng |
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Zusammenfassung: | Phenotypic screens involving pooled CRISPR-Cas9 libraries offer a powerful, rapid yet affordable approach to evaluate gene functions on a global scale. Here, we present a protocol for performing pooled CRISPR-Cas9 loss-of-function screens to identify genetic modifiers using either fluorescence-based or cell death phenotypic readouts. We describe steps for designing and amplifying the library and generating and screening cells. We then detail deep sequencing and statistical analysis using cas9 High Throughput maximum Likelihood Estimator.
For complete details on the use and execution of this protocol, please refer to Bersuker et al. (2019),1 Li et al. (2022),2 and Roberts et al. (2022).3
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•Detailed instructions for pooled CRISPR-Cas9 screen design•Identify genetic modifiers using cell-death-based and fluorescence-based readouts•Detailed steps for deep sequencing and statistical analysis using casTLE
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Phenotypic screens involving pooled CRISPR-Cas9 libraries offer a powerful, rapid yet affordable approach to evaluate gene functions on a global scale. Here, we present a protocol for performing pooled CRISPR-Cas9 loss-of-function screens to identify genetic modifiers using either fluorescence-based or cell death phenotypic readouts. We describe steps for designing and amplifying the library and generating and screening cells. We then detail deep sequencing and statistical analysis using cas9 High Throughput maximum Likelihood Estimator. |
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ISSN: | 2666-1667 2666-1667 |
DOI: | 10.1016/j.xpro.2023.102201 |