Transcriptomics Technology Tracking-Seq Reveals Off-Target Effect Heterogeneity Mediated by CRISPR-Cas9

Research Background

With the rapid development of genome editing technologies, the CRISPR-Cas9 system has been widely used in biomedical research due to its high efficiency and ease of operation. However, a significant issue with the CRISPR-Cas9 system is the potential for off-target effects—unintended cuts at genomic sites other than the target location. Comprehensive identification and assessment of these off-target effects have become essential both in research and application. Previous studies typically employed methods such as cell-free assays to detect off-target effects. Nevertheless, these methods tend to be dependent on specific editing tools or cell types, require large numbers of cells, have high false-positive rates, and are mostly limited to in vitro genome editing. Therefore, there is an urgent need to develop a universal method that can directly detect off-target effects in situ within cells. Additionally, current research somewhat neglects the targeting heterogeneity of editing tools; that is, different editing tools may exhibit varying off-target effects in different cell types. This aspect is particularly crucial for clinical treatments. Hence, new technological platforms should not only be applicable for detecting off-target effects of various editing tools but also compatible with different cell types to address diverse research and application scenarios.

Source of the Paper

This research was collaboratively conducted by Ming Zhu, Runda Xu, Junsong Yuan, and others, affiliated with multiple prestigious institutions including the Tsinghua–Peking Joint Center for Life Sciences at Tsinghua University, the Department of Basic Medicine at the School of Medicine, the Key Laboratory of Bioinformatics of the Ministry of Education, the IDG/McGovern Institute for Brain Research, the Center for Systems and Synthetic Biology, the School of Pharmaceutical Sciences, the School of Life Sciences, and the Beijing Molecular Oncology Joint Laboratory. This paper was published in the journal Nature Biotechnology in 2024.

Research Details

Operational Procedure

The research involved four main steps:

a) Capturing RPA-bound single-stranded DNA (ssDNA) using Cut&Run technology. b) Utilizing HL-dsDNase to specifically remove double-stranded DNA (dsDNA), thereby enriching ssDNA. c) Constructing specific ssDNA libraries. d) Performing next-generation sequencing (NGS) and bioinformatics analysis via Offtracker software.

Research Results

Through sequencing analysis of the edited cells, the research team demonstrated that Tracking-Seq effectively captures RPA-bound ssDNA. Even with low cell input, it successfully detected editing activities at both target and off-target sites. Time-series analysis revealed dynamic differences in Tracking-Seq signals across different editing tools.

Research Conclusion

The proposed Tracking-Seq technology showcases high sensitivity and practicality, being amenable to a wide range of common genome editing tools. It is applicable not only for in vitro experiments but also for both in vitro and in vivo genome editing scenarios.

Research Highlights

Tracking-Seq technology enables direct detection of off-target effects with low cell input and reveals the heterogeneity of off-target effects of the same guide RNA across different editing models and cell types. These findings underscore the necessity of in situ measurements of off-target effects within the original system.

Additional Information

The study also developed corresponding algorithms for data analysis and proposed bidirectional trends (incremental enhancement or reduction) in off-target effects for various editing tools. Additionally, the research found heterogeneity in editing efficiency across different cell types, suggesting that assessments of off-target effects in surrogate cell lines may be unreliable, posing potential risks to in vitro or in vivo applications.

Conclusion

This study not only provides an effective technology for detecting off-target effects of CRISPR-Cas9 and other genome editing tools but also offers crucial evidence for researchers to conduct comprehensive risk assessments before high-risk clinical applications—facilitating a smoother transition of genome editing technologies to clinical practice.