Detect-seq: CBE Off-Target Detection

Detect-seq: CBE Off-Target Detection

1. Background Introduction

With the rapid advancement of gene editing technologies, base editing has emerged as a novel precision gene editing tool. Compared to traditional double-strand break (DSB)-based editing technologies such as CRISPR-Cas9, the Cytosine Base Editor (CBE) system offers more precise single-base modification capabilities without relying on DSB repair processes, thereby reducing the risk of large-scale genomic structural instability. Currently, CBE technology shows great promise in the treatment of various monogenic inherited diseases, particularly those involving point mutations, such as sickle cell anemia, β-thalassemia, hereditary deafness, and hypercholesterolemia. The precision of CBE makes it a potentially ideal therapeutic approach for these diseases.

As the clinical translation of CBE technology accelerates, the FDA and CDE have explicitly required comprehensive off-target risk assessment for gene editing products in relevant guidelines, emphasizing the use of multiple methods to identify potential off-target sites. Against this backdrop, Detect-seq has been developed as an off-target detection method specifically designed for the CBE system, providing a critical safety assessment tool for the clinical translation and IND filing of CBE editing technology.

Shutong Technology launches the Detect-seq (dU-detection enabled by C-to-T transition during sequencing) base editing off-target detection platform, which achieves precise detection of C-to-T base transitions induced by the CBE system based on NGS technology. To ensure technical reliability and accuracy, the Shutong Technology team has conducted systematic method validation in accordance with the ICH Q2(R1) guideline and the FDA's "Bioanalytical Method Validation: Industry Guidance," establishing a comprehensive technical evaluation system. Shutong Technology has successfully provided regulatory-compliant gene editing safety assessment services for numerous domestic and international gene therapy companies, fully supporting IND filings and clinical translations.

2. Project Principle

Detect-seq is an in vitro off-target detection technology specifically developed for CBEs (Cytosine Base Editors). It ingeniously utilizes deoxyuridine (dU), an intermediate product generated during CBE editing, to identify editing sites. The working principle involves first extracting genomic DNA from base-edited cells, then labeling dU produced by CBE with biotin to enrich DNA fragments containing editing sites. Simultaneously, downstream of the dU site, normal cytosine (C) is replaced with 5-formylcytosine (5fC), which is subsequently chemically labeled with malononitrile to form thymine (T). This process enables the observation of distinct tandem C-to-T mutation signals during sequencing, thereby accurately localizing all base editing sites in the genome and achieving a comprehensive assessment of the off-target effects of CBEs across the entire genome.

Figure 1. Detection Principle of Detect-seq

3. Project Advantages

3.1 Comprehensive and Accurate Detection of Multiple Types of Off-Target Events

Detect-seq technology can efficiently detect the following types of off-target events induced by single-base editing:

(1) sgRNA-dependent off-targets: Precisely identifies editing effects caused by incorrect recognition and binding of the Cas9 RNP complex to similar sequence sites;

(2) sgRNA-independent off-targets: Detects genome-wide off-target mutations resulting from random editing of DNA substrates by the cytidine deaminase component of BE;

(3) Novel off-target type identification: Capable of detecting two novel sgRNA-dependent off-targets: out-of-protospacer edits and target-strand edits.

3.2 Excellent Detection Performance

Based on strict verification standards and internationally recognized evaluation methods, Detect-seq technology has been comprehensively validated across four key dimensions:

3.3 Service Advantages

(1) Comprehensive method validation: Detect-seq has undergone systematic validation, featuring high sensitivity and specificity across the full linear range of 0.1% to 50%;

(2) Regulatory compliance guarantee: Strictly adheres to ICH Q2(R1) and FDA guideline requirements, ensuring detection results meet international regulatory standards;

(3) Leading technical platform: Detect-seq can comprehensively detect various off-target events induced by single-base editing, including sgRNA-dependent off-targets, sgRNA-independent off-targets, out-of-protospacer edits, and target-strand edits, meeting safety assessment needs at all stages of drug development;

(4) Rich successful cases: Has successfully provided services for numerous gene therapy companies, assisting in the smooth completion of IND filings;

(5) End-to-end service model: Provides comprehensive single-base editing safety solutions from target safety pre-evaluation, sample testing to IND filing data support.

4. Application Scenarios

(1) Gene therapy safety assessment: Comprehensive evaluation of off-target effects of gene editing tools during preclinical research;

(2) Development of novel base editors: Providing evaluation tools for the development of base editors with higher specificity;

(3) sgRNA design optimization: Assisting in the design of sgRNAs with higher specificity;

(4) Off-target analysis in different cell types: Studying differences in off-target patterns across different cell backgrounds;

(5) Academic research: Exploring the molecular mechanisms of gene editing and off-target effects.

5. Detect-seq Test Report Example

The Detect-seq analysis report provided by Shutong Technology adopts a standardized format to ensure professional and complete data presentation. The initial part of the report includes an introduction to project background and experimental principles, Detect-seq technical principles, library construction process description, and an overview of bioinformatics analysis workflows, providing clients with necessary technical context. This is followed by the sample information and sequencing data statistics section, which details basic sample information (sample name, editing tool, target information, etc.), raw sequencing data quality control indicators, and alignment statistics against reference genomes to ensure data quality reliability. In addition, the report includes the following core content:

(1) Visualization of sgRNA on-target and off-target sites: Through Poisson distribution testing, potential sgRNA binding sites in the genome and their editing status are identified and analyzed. The first row of the table represents the target on-target site with no mismatches; the rows below display multiple potential off-target sites with mismatch counts ranging from 2.0 to 11.0, distributed across different chromosomes and positive/negative strands. This visualization intuitively presents the targeting specificity and potential off-target risks of the gene editing system, providing important basis for evaluating editing precision and experimental safety.

Figure 2. Visualization of On-target and Off-target Sites

(2) Nucleotide distribution at on-target/off-target sites: Presents a seqlogo plot of nucleotide frequency distribution at on-target and off-target sites, used to evaluate the conservation of sgRNA binding regions. The frequency and conservation of each nucleotide at each position are intuitively displayed through information entropy (bits), where higher nucleotide heights indicate stronger conservation at that position. This analysis helps clients understand the sequence characteristics and site specificity of sgRNA-genome binding, providing molecular-level evidence for evaluating the precision and potential off-target effects of the editing system.

Figure 3. Seqlogo Plot of Nucleotide Frequency Distribution at On-target and Off-target Sites

(3) Nucleotide editing status at on-target and off-target sites: Displays the frequency distribution and editing patterns of different nucleotides at each site, enabling evaluation of the specificity of the gene editing system, identification of actual off-target events, and comparison of editing efficiency differences across different sites, thereby comprehensively understanding the safety and precision of the editing tool. Through this comprehensive analysis, reliable evidence for gene editing experiments can be obtained, providing important basis for optimizing system design, reducing off-target risks, and ensuring the safety and effectiveness of experimental applications.

Figure 4. Nucleotide Frequency Distribution and Editing Patterns at Each Site

6. Service Content

*Service Cycle: 20-30 working days for standard processes;

*Service Features: Provide integrated service support from sgRNA design to result analysis.

7. Sample Requirements

8. References

[1] Anzalone AV, Koblan LW, Liu DR. Genome editing with CRISPR-Cas nucleases, base editors, transposases and prime editors. Nat Biotechnol. 2020;38(7):824-844. doi:10.1038/s41587-020-0561-9

[2] Rees HA, Liu DR. Base editing: precision chemistry on the genome and transcriptome of living cells [published correction appears in Nat Rev Genet. 2018 Oct 19;:]. Nat Rev Genet. 2018;19(12):770-788. doi:10.1038/s41576-018-0059-1

[3] Lei Z, Meng H, Lv Z, et al. Detect-seq reveals out-of-protospacer editing and target-strand editing by cytosine base editors. Nat Methods. 2021;18(6):643-651. doi:10.1038/s41592-021-01172-w

[4] Lei Z, Meng H, Rao X, Zhao H, Yi C. Detect-seq, a chemical labeling and biotin pull-down approach for the unbiased and genome-wide off-target evaluation of programmable cytosine base editors. Nat Protoc. 2023;18(7):2221-2255. doi:10.1038/s41596-023-00837-4