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. 2015 Sep;12(9):823-6.
doi: 10.1038/nmeth.3473. Epub 2015 Jul 13.

Unraveling CRISPR-Cas9 Genome Engineering Parameters via a Library-On-Library Approach

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Free PMC article

Unraveling CRISPR-Cas9 Genome Engineering Parameters via a Library-On-Library Approach

Raj Chari et al. Nat Methods. .
Free PMC article

Abstract

We developed an in vivo library-on-library methodology to simultaneously assess single guide RNA (sgRNA) activity across ∼1,400 genomic loci. Assaying across multiple human cell types and end-processing enzymes as well as two Cas9 orthologs, we unraveled underlying nucleotide sequence and epigenetic parameters. Our results and software (http://crispr.med.harvard.edu/sgRNAScorer) enable improved design of reagents, shed light on mechanisms of genome targeting, and provide a generalizable framework to study nucleic acid-nucleic acid interactions and biochemistry in high throughput.

Figures

Figure 1
Figure 1
Schematic of the library-on-library approach employed in our study. (A) sgRNA sequences corresponding to ~1,400 endogenous target sites were synthesized and cloned to make a sgRNA plasmid library. In parallel, replicas of the ~1,400 target sites were synthesized and cloned to make a lentiviral plasmid library. Subsequently, this lentiviral target library was integrated in our cells. Next, the sgRNA plasmid library was transiently transfected and cells were harvested for DNA 72 h post-transfection. Primer sequences designed against the constant sequences surrounding the target sites were used for PCR and libraries were prepared for Illumina sequencing. (B) In target site naïve cells, the above mentioned sgRNA plasmid library was transiently transfected and DNA was harvested 72 h post-transfection. Agilent SureSelect enrichment was performed on the genomic DNA with a custom set of probes specific to regions encompassing the ~1,400 target sites and libraries were prepared for Illumina sequencing.
Figure 2
Figure 2
Versatility of the library-on-library approach. (A) Scatter plot assessing the reproducibility of observed relative mutation rates across biological replicates. Data compared were from two independent sgRNA library transfections. (B) Comparison of activity between Cas9Sp nuclease vs. Cas9Sp nickase across all of the assessed sites. Box plots depicting the range of activities for (C) Cas9Sp nuclease in K562 cells. In total, 1,206 sites are represented for the control and 1,228 for the Cas9Sp nuclease treated cells. (D) Cas9St1 nuclease in 293T cells. In total, data for 1,172 sites are shown for the control and 1,169 for the nuclease treated cells. (E) Impact of TREX2 on altering patterns of NHEJ. A heavy bias towards deletions and slightly away from insertions is observed upon addition of TREX2. Data shown is for 293T using Cas9Sp nuclease. For all box plots, the size of the boxes represent the interquartile range (IQR) of the data. Whiskers are drawn to 1.5 * IQR. Red horizontal line represents the median. ‘+’ represent outlier data points that are beyond 1.5 * IQR.
Figure 3
Figure 3
(A) Position by position comparison of the base distributions between high and low activity sgRNA sequences. Position 20 exhibited the most striking difference. P values were calculated using a 2×4 Fisher’s exact test. Comparison of (B) DNase I hypersensitivity and (C) H3K4-trimethylation between regions of high and low sgRNA activity. P-values were calculated by employing a t-test comparing the values from each group. (D) Using the top 30 sgRNA sequences corresponding to regions with low activity and high DNase I sensitivity, the sequences were scored by the SVM for predicted activity. Strikingly, the majority of the sequences were predicted to have low activity. (E) Frequency of off-target activity and predicted activity. Using a recently published dataset, we scored all off-target sites with our SVM and compared those scores with the frequency of off-target activity observed. No discernable relationship was observed, suggesting these two aspects are independent.

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