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Increasing the specificity of CRISPR systems with engineered RNA secondary structures

Nature Biotechnology volume 37, pages 657–666 (2019)Cite this article

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Abstract

CRISPR (clustered regularly interspaced short palindromic repeat) systems have been broadly adopted for basic science, biotechnology, and gene and cell therapy. In some cases, these bacterial nucleases have demonstrated off-target activity. This creates a potential hazard for therapeutic applications and could confound results in biological research. Therefore, improving the precision of these nucleases is of broad interest. Here we show that engineering a hairpin secondary structure onto the spacer region of single guide RNAs (hp-sgRNAs) can increase specificity by several orders of magnitude when combined with various CRISPR effectors. We first demonstrate that designed hp-sgRNAs can tune the activity of a transactivator based on Cas9 from Streptococcus pyogenes (SpCas9). We then show that hp-sgRNAs increase the specificity of gene editing using five different Cas9 or Cas12a variants. Our results demonstrate that RNA secondary structure is a fundamental parameter that can tune the activity of diverse CRISPR systems.

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Fig. 1: Engineered RNA secondary structures tune the activity of dCas9-P300.
Fig. 2: Spacer secondary structure improves the performance of a kinetic model of R-loop formation.
Fig. 3: hp-sgRNAs increase the specificity of SpCas9 in human cells.
Fig. 4: hp-sgRNAs retain binding activity at off-target loci.
Fig. 5: hp-sgRNAs and -crRNAs increase the specificity of various Cas effectors.
Fig. 6: RNA secondary structure drives the specificity increases observed with hp-sgRNAs.

Data availability

Sequencing data are available through the National Center for Biotechnology Information Sequence Read Archive (SRA) database (PRJNA524383), including all deep sequencing, 5′ RACE RNA-seq and CIRCLE-seq files. All other relevant raw data are available from the corresponding author on reasonable request.

Code availability

Custom scripts used to analyze 5′ RACE experiments and conduct kinetic modeling are available upon reasonable request.

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Acknowledgements

We thank C. E. Nelson and T. S. Klann for useful discussions related to experimental design and execution. This work was supported by an Allen Distinguished Investigator Award from the Paul G. Allen Frontiers Group; a US National Institutes of Health (NIH) Director’s New Innovator Award (no. DP2OD008586); NIH grant nos. R01DA036865, R01AR069085 and P30AR066527; and National Science Foundation grant nos. DMR-1709527 and EFMA-1830957.

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Author notes

  1. Eric A. Josephs

    Present address: Department of Nanoscience, University of North Carolina at Greensboro, Greensboro, NC, USA

Authors and Affiliations

  1. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    D. Dewran Kocak, Vidit Bhandarkar, Shaunak S. Adkar & Charles A. Gersbach

  2. Center for Genomic and Computational Biology, Duke University, Durham, NC, USA

    D. Dewran Kocak, Vidit Bhandarkar, Shaunak S. Adkar, Jennifer B. Kwon & Charles A. Gersbach

  3. Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA

    Eric A. Josephs

  4. University Program in Genetics and Genomics, Duke University Medical Center, Durham, NC, USA

    Jennifer B. Kwon

  5. Department of Surgery, Duke University Medical Center, Durham, NC, USA

    Charles A. Gersbach

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Contributions

D.D.K., E.A.J. and C.A.G. designed the experiments. D.D.K., E.A.J., V.B., S.S.A. and J.B.K. performed the experiments. D.D.K., E.A.J. and C.A.G. analyzed the data. D.D.K., E.A.J. and C.A.G. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Charles A. Gersbach.

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Competing interests

D.D.K., E.A.J. and C.A.G. have filed for a patent related to this work. C.A.G. is an advisor for Sarepta Therapeutics and a cofounder of and advisor for Element Genomics and Locus Biosciences.

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Integrated supplementary information

Supplementary Figure 1 Engineered RNA secondary structures tune the activity of dCas9-P300.

a,b, Gene activation of HBG1 and IL1B using hp-sgRNAs with varying stem lengths, measured by qRT–PCR. Gene activation is plotted as a function of the predicted folding energy of engineered secondary structure for each hp-sgRNA. WT-sgRNA is shown in black, ns-sgRNAs are shown in gray and hairpin sgRNAs are shown in blue. Data are shown as fold increase relative to the control sample. The control sample was transfected with dCas9-P300 only. The mean is the measure of center and error bars represent s.e.m. for n = 3. The sequences of all sgRNAs are listed in Supplementary Table 1.

Supplementary Figure 2 Expression levels and in vivo processing of hp- and ns-sgRNAs.

a, Schema depicting experimental work-flow. Cells were transfected with plasmids encoding dCas9-P300 and sgRNA variants. RNA collected from cells was used to measure gene activation of IL1RN, sgRNA expression levels and spacer sequence identities. b, Schema depicting 5′ RACE applied specifically to sequence sgRNAs. Template switching of the reverse transcriptase ensures accurate profiling of the 5′ ends of sgRNA variants. c, Gene activation induced by each sgRNA variant. This experiment was performed as shown in Fig. 1, except that extraction of total RNA, rather than only mRNA, was performed. d, Replotting the mean of each group in c as a function of the predicted folding energy of each hp-sgRNA’s engineered secondary structure. e, Expression level of each sgRNA variant as measured by RT–qPCR. f, Replotting the mean of each hp-sgRNA’s activity in c against its mean expression level as shown in e. g, The distribution of spacer lengths in cells treated with various sgRNA variants, as determined by 5′ RACE followed by deep sequencing. The number next to the sgRNA alias indicates the number of nucleotides added to the 5′ end of the spacer (for example, hp17 and ns17 have 17 nucleotides (nt) added, and a total spacer length of 37 nt). The expected unprocessed length of an sgRNA variant is highlighted in an orange box. h, Percentage of processing to 20 nt for each sgRNA variant, as measured by RNA-seq. i, Replotting the mean of each hp-sgRNA’s activity in c against its mean degree of processing as shown in i. The mean is the measure of center and error bars represent s.e.m. for n = 3. The sequences of all sgRNAs are listed in Supplementary Table 1.

Supplementary Figure 3 Optimizing hairpin structures for VEGFA spacer 1.

a, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. b, Predicted structures of sgRNA variants. c, On-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an internal loop and stems form near the 5′ end of the spacer. d, Predicted structures of sgRNA variants. e, On-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize a truncated spacer, an external loop and a stem that forms near the 5′ end of the spacer. f, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 3 and Supplementary Fig. 6. The sequences of all sgRNAs are listed in Supplementary Table 1.

Supplementary Figure 4 Optimizing hairpin structures for EMX1 spacer 1.

a, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. b, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an internal loop and stems form on the 3′ end of the spacer. c, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 3 and Supplementary Fig. 6. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 5 Optimizing hairpin structures for VEGFA spacer 2.

a, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. b, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an internal loop and stems form near the 5′ end of the spacer. c, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins an external loop, non-canonical base pairing, and stems form near the 3′ end of the spacer. d,e, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 3 and Supplementary Fig. 6. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 6 hp-sgRNAs increase the specificity of SpCas9 in human cells.

a–c, On-target and off-target mutation rates for sgRNA variants targeting the EMX1 and VEGFA genes, measured by deep-sequencing. ‘Percent modified’ indicates percentage of reads mutated when compared with the wild-type loci. Significant differences in mutational activity were found at all off-target sites when comparing WT-sgRNA (‘WT’) with control samples, except for VEGFA spacer 2 at OT10 (P < 0.01, FDR). At all measured off-target sites, hp-sgRNAs show significant decreases in activity compared with that of WT-sgRNA (P < 0.05, FDR). Hypothesis testing using a one-sided Fisher exact test with pooled read counts, adjusting for multiple comparisons using the Benjamini–Hochberg method. The sequences of all sgRNAs used are listed in Supplementary Table 1. d, On-target editing rates for sgRNA variants plotted on a linear scale. e, Specificity metrics for sgRNA variants. Specificity metric defined as on-target indel rate divided by the sum of all off-target indel rates. The mean is the measure of center and error bars represent s.e.m. for n = 3. Hairpins A, B and C refer to the respective hp-sgRNAs characterized in panels a–c and are color matched. For example, hairpins A, B and C for spacer VEGFA.1 are hairpins 4, 5 and 6. The sequences of all sgRNAs used are listed in Supplementary Table 1. FDR, false discovery rate.

Supplementary Figure 7 Unbiased genome-wide detection of off-target activity using CIRCLE-seq.

a–d, Read counts for two replicates were plotted to demonstrate reproducibility of the assay. Activity at the on-target site shown in green. The experiment was performed with EMX1 spacer 1 and hp-sgRNA 2. Data points clustered along an axis have corresponding read counts of zero but were given a pseudocount for display purposes.

Supplementary Figure 8 Comparative genome-wide activity of sgRNA variants applied with SpCas9.

a–c, Plotting CIRCLE-seq read counts of WT-sgRNA against tru-, hp- and ns-sgRNAs, respectively. Only those off-target sites present in both replicates (Supplementary Fig. 7) were used for this analysis. The on-target sites are shown in green. Read counts represent the sum of two replicate experiments. The experiment was performed with EMX1 spacer 1 and hp-sgRNA 2. Data points clustered along an axis have a corresponding read count of zero but were given a pseudocount for display purposes. d, Venn diagram representing the overlap of all identified CIRCLE-seq cleavage sites. For each condition, only sites identified in both replicates were used. e, An UpSetR plot showing the number of sites that overlap between various intersections of sample groups. Sample group intersections are indicated by the matrix below.

Supplementary Figure 9 Off-target sites identified by CIRCLE-seq for SpCas9 using EMX1 spacer 1.

a–d, Sequence identity of off-target sites detected using CIRCLE-seq. WT- and tru-sgRNAs have truncated listings that are continued in Supplementary Fig. 11. Brackets indicate the same off-target site that has two same-scoring alignments to the on-target site. WT and Tru off-target lists were truncated due to space limitations.

Supplementary Figure 10 Optimizing hairpin structures for SaCas9-KKH spacers.

a–c, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. d–f, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 6. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 11 Optimizing hairpin structures for Cas12a spacers.

Optimizing hairpin structures for LbCas12a. a–c, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. d–f, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 6. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 12 RNA secondary structure drives the specificity increases observed with hp-sgRNAs used with SpCas9.

a–c, Nuclease activity of hp-sgRNAs and corresponding non-structured (n.s.) controls in human cells; sgRNA variants were tested with SpCas9, as characterized in Supplementary Fig. 5. Deep sequencing was used to measure gene editing activity. Significant differences in mutational activity were found at all off-target sites when comparing WT-sgRNA with control samples (P < 0.01 × 10−129). At all examined off-target sites, hp-sgRNAs significantly reduced gene editing activity when compared with WT-sgRNA (P < 0.05 × 10−14). At all examined off-target (OT) sites, hp-sgRNAs significantly reduced editing activity when compared with the corresponding ns-sgRNA, except for VEGFA spacer 1 at OT3 and VEGFA spacer 2 at OT1 and OT2 (P < 0.05 × 10−9). Hypothesis testing was carried out using a one-sided Fisher exact test with pooled read counts, adjusting for multiple comparisons using the Benjamini–Hochberg method. The mean is the measure of center and error bars represent s.e.m. for n = 3. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 13 CRISPR off-target activity as a function of mismatch number and mismatch position.

a, The normalized indel activity for off-targets, grouped by the number of mismatches present, as measured by deep sequencing. Activity was normalized by the on-target activity of the corresponding WT- or hp-sgRNA. Each data point represents a different off-target site and is the mean value of editing from three biological replicates. Boxes represent quartiles, the median is the measure of center and n for each group is shown in the panel. Individual points with a normalized indel rate of zero are not shown. b, The positional dependence of mismatches in off-targets identified via CIRCLE-seq, as determined by off-targets shown in Supplementary Fig. 9.

Supplementary Figure 14 In vitro nuclease activity of Cas9 and Cas12 effectors with engineered sgRNA variants.

a–c, In vitro digests demonstrating the on-target and off-target activity of sgRNA variants. PCR amplicons containing various target sites were incubated with the purified Cas effector complexed with chemically synthesized sgRNAs. EMX1 spacer 1 was applied with SpCas9 using hp-sgRNA 2. EMX1 spacer 2 was applied with SaCas9 using hp-sgRNA 4. DNMT1 spacer 1 was applied with AsCas12a using hp-sgRNA 4. Each experiment was performed once. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 15 Single-molecule imaging reveals the effect of sgRNA spacer secondary structure on DNA-binding profiles of CRISPR effectors in vitro.

a, Example AFM image of SpCas9 bound to different points on a single, streptavidin-labeled DNA molecule. The DNA molecule has a target site and two known off-target sites distributed along its length. OT1 is a designed off-target with seven consecutive PAM-distal mismatches. OT2 is an off-target that was validated in human cells by deep-sequencing; it is the off-target with the highest mutational rate for the given spacer. (below) Aligned and averaged structures of CRISPR effectors by AFM. At least three preparations for each experimental condition were imaged and analyzed. b–d, Normalized binding profiles of (b) SpCas9 (apparent dissociation constants (Kd) at the on-target site for sgRNA: 8.8 ± 0.9 nM (s.e.m.); hp-sgRNA: 10.0 ± 0.9 nM; ns-sgRNA: 2.1 ± 0.1 nM), (c) SaCas9 (sgRNA: 5.1 ± 0.4 nM (s.e.m.); hp-sgRNA: 8.7 ± 1.1 nM; ns-sgRNA: 9.0 ± 0.9 nM) and (d) AsCas12a (sgRNA: 5.7 ± 1.2 nM; hp-sgRNA: 16.9 ± 3.8 nM; ns-sgRNA: 8.9 ± 1.6 nM). e, A specificity metric for the binding of each sgRNA variant. The metric is defined as the frequency of observed DNA molecules bound by Cas9/Cas12a proteins at the on-target site divided by the frequency of observed DNA molecules bound by Cas9/Cas12a with no Cas9/Cas12a proteins at the on-target site. The mean is the measure of center. EMX1 spacer 1 was applied with SpCas9 using hp-sgRNA 2. EMX1 spacer 2 was applied with SaCas9 using hp-sgRNA 4. DNMT1 spacer 1 was applied with AsCas12a using hp-sgRNA 4. The sequences of all sgRNAs used are listed in Supplementary Table 1.

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Supplementary Figs. 1–15 and Supplementary Tables 1 and 2

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Kocak, D.D., Josephs, E.A., Bhandarkar, V. et al. Increasing the specificity of CRISPR systems with engineered RNA secondary structures. Nat Biotechnol 37, 657–666 (2019). https://doi.org/10.1038/s41587-019-0095-1

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