Abstract
CRISPR-Cas technology has revolutionized gene editing, but concerns remain due to its propensity for off-target interactions. This, combined with genotoxicity related to both CRISPR-Cas9-induced double-strand breaks and transgene delivery, poses a significant liability for clinical genome-editing applications. Current best practice is to optimize genome-editing parameters in preclinical studies. However, quantitative tools that measure off-target interactions and genotoxicity are costly and time-consuming, limiting the practicality of screening large numbers of potential genome-editing reagents and conditions. Here, we show that flow-based imaging facilitates DNA damage characterization of hundreds of human hematopoietic stem and progenitor cells per minute after treatment with CRISPR-Cas9 and recombinant adeno-associated virus serotype 6. With our web-based platform that leverages deep learning for image analysis, we find that greater DNA damage response is observed for guide RNAs with higher genome-editing activity, differentiating even single on-target guide RNAs with different levels of off-target interactions. This work simplifies the characterization and screening process of genome-editing parameters toward enabling safer and more effective gene-therapy applications.
Original language | English |
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Pages (from-to) | 80-94 |
Number of pages | 15 |
Journal | CRISPR Journal |
Volume | 5 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2022 |
Bibliographical note
Publisher Copyright:© Daniel Allen, et al. 2022; Published by Mary Ann Liebert, Inc. 2022.
Funding
This work was supported by the European Research Council (ERC) under the European Union Horizon 2020 research and innovation program (grant no. 802567, Y.S.; grant no. 755758, A.H.), the Israel Innovation Authority through the CRISPR-IL consortium, the Zuckerman foundation, and the POLAK Fund for Applied Research at the Technion (Y.S. and L.E.W.).
Funders | Funder number |
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Israel Innovation Authority | |
Zuckerman foundation | |
Horizon 2020 Framework Programme | |
European Commission | |
Technion-Israel Institute of Technology | |
Horizon 2020 | 802567, 755758 |