Improving the sensitivity of CRISPR off-target detection through modulation of DNA repair

Case ID:
C16818

Value Proposition:

·        Utilizes components of existing workflow to better detect off-target gene editing

·        Capable for use in in vivo or in vitro settings

·        Maximizes impact of CRISPR-Cas9 in therapeutic development

Technology Description

·        Researchers at Johns Hopkins have developed a method to detect off-target editing with high sensitivity following CRISPR-Cas9 genetic editing. By elongating the duration of MRE11 localization, a critical component of CRISPR-Cas9 editing, subsequent ChIP-seq allows for detection of all CRISPR-Cas9 binding sites. Off-target sites can then be distinguished from intended targets via results obtained from the MRE11 ChIP-seq.

Unmet Need

·        CRISPR-Cas9 is a gene editing technology that has revolutionized therapeutics for genetic diseases such as cystic fibrosis, muscular dystrophy, hemophilia, and more. However, the technology is limited by potential off-target effects. Current methods of off-target detection are limited and either have low sensitivity or cannot be performed directly in vivo. Therefore, there is a need to develop a system that can more accurately detect off-target effects to maximize the impact of gene editing for genetic disease therapies.

Stage of Development

·        Researchers have conducted in vivo experiments.

Data Availability

Data available in the publication below.

Publication

Zou, R.S. et al. Improving the sensitivity of in vivo CRISPR off-target detection with DISCOVER-Seq+. Nat Methods

 

Patent Information:
Title App Type Country Serial No. Patent No. File Date Issued Date Expire Date Patent Status
METHODS AND COMPOSITIONS FOR GENOME EDITING PCT: Patent Cooperation Treaty United States 18/710,522   5/15/2024     Pending
Inventors:
Category(s):
Get custom alerts for techs in these categories/from these inventors:
For Information, Contact:
Lisa Schwier
lschwie2@jhu.edu
410-614-0300
Save This Technology:
2017 - 2022 © Johns Hopkins Technology Ventures. All Rights Reserved. Powered by Inteum