Systematic characterization of genome editing in primary T cells reveals proximal genomic insertions and enables machine learning prediction of CRISPR-Cas9 DNA repair outcomes

Leenay, R. T.; Aghazadeh, A.; Hiatt, J.; Tse, D.; Hulquist, J.; Krogan, N.; Wu, Z.; Marson, A.; May, A. P.; Zou, J.

doi:10.1101/404947

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bioRxiv · 10.1101/404947

Systematic characterization of genome editing in primary T cells reveals proximal genomic insertions and enables machine learning prediction of CRISPR-Cas9 DNA repair outcomes

Leenay, R. T.·Aghazadeh, A.·Hiatt, J.·Tse, D.·Hulquist, J.·Krogan, N.·Wu, Z.·Marson, A.·May, A. P.·Zou, J.

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Abstract

The Streptococcus pyogenes Cas9 (SpCas9) nuclease has become a ubiquitous genome editing tool due to its ability to target almost any location in DNA and create a double-stranded break1,2. After DNA cleavage, the break is fixed with endogenous DNA repair machinery, either by non-templated mechanisms (e.g. non-homologous end joining (NHEJ) or microhomology-mediated end joining (MMEJ)), or homology directed repair (HDR) using a complementary template sequence3,4. Previous work has shown that the distribution of repair outcomes within a cell population is non-random and dependent on the targeted sequence, and only recent efforts have begun to investigate this further5-11. However, no systematic work to date has been validated in primary human cells5,7. Here, we report DNA repair outcomes from 1,521 unique genomic locations edited with SpCas9 ribonucleoprotein complexes (RNPs) in primary human CD4+ T cells isolated from multiple healthy blood donors. We used targeted deep sequencing to measure the frequency distribution of repair outcomes for each guide RNA and discovered distinct features that drive individual repair outcomes after SpCas9 cleavage. Predictive features were combined into a new machine learning model, CRISPR Repair OUTcome (SPROUT), that predicts the length and probability of nucleotide insertions and deletions with R2 greater than 0.5. Surprisingly, we also observed large insertions at more than 90% of targeted loci, albeit at a low frequency. The inserted sequences aligned to diverse regions in the genome, and are enriched for sequences that are physically proximal to the break site due to chromatin interactions. This suggests a new mechanism where sequences from three-dimensionally neighboring regions of the genome can be inserted during DNA repair after Cas9-induced DNA breaks. Together, these findings provide powerful new predictive tools for Cas9-dependent genome editing and reveal new outcomes that can result from genome editing in primary T cells.

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Leenay, R. T., Aghazadeh, A., Hiatt, J., Tse, D., Hulquist, J., Krogan, N., Wu, Z., Marson, A., May, A. P., Zou, J.. 2018-08-31. Systematic characterization of genome editing in primary T cells reveals proximal genomic insertions and enables machine learning prediction of CRISPR-Cas9 DNA repair outcomes. https://doi.org/10.1101/404947

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