GloNeuro

Virtual laboratory / DNA and genes

CRISPR–Cas9 genome editing

Design of SpCas9 guides against a simulated gene, editing of cells or mouse zygotes, and validation of knockouts and point mutations by T7E1 assay and amplicon sequencing.

Guided experiment: Gene knockout in cultured cells with CRISPR–Cas9.

CRISPR–Cas9 genome editing

CRISPR–Cas9 is used to make targeted changes to the genome: to knock a gene out by disrupting its coding sequence, or to introduce a precise change such as a point mutation. A short guide RNA directs the Cas9 nuclease to a 20-nucleotide target that lies next to a protospacer-adjacent motif (PAM; NGG for SpCas9), where Cas9 makes a double-strand break about three base pairs from the PAM. Jinek and colleagues showed that a dual RNA guides Cas9 to cleave complementary DNA, with the HNH domain cutting the complementary strand and the RuvC-like domain the non-complementary strand, and that the two RNAs can be fused into a single guide (Jinek et al., 2012); the system was soon shown to cut endogenous sites in human and mouse cells (Cong et al., 2013). The break is repaired either by non-homologous end joining, which leaves small insertions or deletions that can shift the reading frame, or, if a donor template is supplied, by homology-directed repair, which can install a defined edit. The gene and genome in the simulator are artificial, not real sequences.

Design the experiment

Questions to investigate

  1. For an early exon, which protospacer has a good GC content, no poly-T, and the fewest close off-target matches? Why should a second independent guide still be tested?
  2. How does moving the cut from an early coding exon to a later one or out of the coding sequence change the chance of a loss-of-function knockout?
  3. Compare the indel percentage from the T7E1 assay with the exact figure from amplicon sequencing. When does the T7E1 estimate mislead?
  4. Add the single-stranded donor. What fraction of alleles carry the point mutation, and why is the HDR rate so much lower than the NHEJ rate? What do the silent PAM- or seed-blocking mutations in the donor achieve?
  5. In zygotes, run several founders. How many are mosaic rather than cleanly biallelic, and how should they be bred to obtain a stable line?
  6. Amplify the top predicted off-target sites. How does the editing rate there depend on the number and position of the mismatches?

In a real laboratory

A target region is chosen in an early coding exon. A design tool lists candidate protospacers with their GC content, any poly-T stretch (which terminates U6 transcription), the predicted cut site and genome-wide off-target matches. Two independent guides are usually taken forward, so that a shared phenotype cannot be explained by off-target cutting. The guide is delivered with Cas9 into cultured cells by transfection or electroporation, or injected as Cas9 messenger RNA or protein with the sgRNA into one-cell zygotes; a single-stranded donor oligonucleotide is added when a precise point mutation is wanted. Every experiment includes a non-targeting guide and an untreated control. Editing is estimated quickly by a T7 endonuclease I assay and exactly by amplicon sequencing, loss of the protein is confirmed by Western blot, and the top predicted off-target sites are sequenced. Co-injection of Cas9 messenger RNA and sgRNAs into mouse zygotes produced mice with biallelic mutations in two genes at high efficiency, and co-injection with oligonucleotides produced precise point mutations (Wang et al., 2013). Founders are often mosaic and are bred to separate single alleles. The work requires approval by an Institutional Biosafety Committee, and animal work requires approval by an Institutional Animal Ethics Committee (in India, under the CCSEA and the Prevention of Cruelty to Animals Act 1960; elsewhere, the equivalent national body), humane end points and attention to the 3Rs.

How the simulation works

A seeded artificial gene of 300 codons in six exons, with untranslated regions and introns, lies in a simulated genome of about 180 kilobases. Near-matching off-target sites are planted for some of its protospacers, with mismatches mostly at the PAM-distal end. Every SpCas9 protospacer (20 nucleotides followed by NGG) on both strands is listed with its position, GC content, poly-T, cut site three base pairs from the PAM, whether the cut lies in an early coding exon, and off-target counts for zero to three mismatches. The score shown is an illustrative model based on sequence features, not a published efficiency algorithm. Each allele is cut with a probability set by delivery and score, and repaired by NHEJ (mostly small deletions and 1-base insertions, with some larger deletions) or, with a donor, occasionally by HDR. Cells carry two alleles and zygote founders several, which produces mosaicism. The T7E1 estimate is computed from the allele distribution as 100×(1−√(1−cleaved)). Amplicon reads include sequencing errors and are aligned by an exhaustive small-indel search, and the edited coding sequence is translated. Mismatches far from the PAM reduce off-target editing less than those close to it (Hsu et al., 2013). All rates are approximate. Chromatin, guide secondary structure, base and prime editing and the biochemistry of delivery are not simulated.

  1. Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012). A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science, 337(6096), 816–821. https://doi.org/10.1126/science.1225829
  2. Cong, L., Ran, F. A., Cox, D., et al. (2013). Multiplex genome engineering using CRISPR/Cas systems. Science, 339(6121), 819–823. https://doi.org/10.1126/science.1231143
  3. Hsu, P. D., Scott, D. A., Weinstein, J. A., et al. (2013). DNA targeting specificity of RNA-guided Cas9 nucleases. Nature Biotechnology, 31(9), 827–832. https://doi.org/10.1038/nbt.2647
  4. Wang, H., Yang, H., Shivalila, C. S., et al. (2013). One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell, 153(4), 910–918. https://doi.org/10.1016/j.cell.2013.04.025

About the simulations

Each simulator is a simplified version of a published model of cells, signals, instruments or behaviour, and runs in the web browser. The simulations show how the techniques work and how design choices change the results; they do not replace training in a laboratory.

All data are simulated and generated afresh on each run, so results vary between runs, as in real experiments. Nothing is stored or sent: designs and results are lost when the page is closed, unless the protocol is copied or the data file is downloaded.

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