Prime Editing (PE) Efficiency Detection

Prime Editing (PE) Efficiency Detection

1. Background

Prime Editing (PE) is a next-generation precision gene editing technology developed by the laboratory of David R. Liu in 2019. Unlike conventional CRISPR-Cas9, Prime Editing does not require the introduction of double-strand breaks (DSBs). Instead, the pegRNA directs the Prime Editor to nick the target strand of genomic DNA, after which the reverse transcription template encoded within the pegRNA serves as the blueprint for de novo synthesis of the desired sequence; the resulting genomic modification is subsequently completed via endogenous DNA repair mechanisms. In principle, Prime Editing is capable of mediating all 12 possible base conversions, as well as precise insertions and deletions, conferring substantially greater editing versatility and precision relative to preceding technologies while markedly reducing the risks of chromosomal rearrangements and large-fragment deletions.

Current CBE and ABE base editors are restricted to a limited repertoire of single-base conversions. The advent of Prime Editing has dramatically expanded the spectrum of addressable mutation types, providing novel therapeutic strategies for genetic diseases driven by complex mutational alterations, including sickle cell disease and Tay-Sachs disease. Companies such as Prime Medicine have advanced PE technology into preclinical and clinical trial stages, signaling the accelerating translation of this technology toward clinical application.

As gene editing technologies continue to evolve, regulatory agencies worldwide—including the U.S. Food and Drug Administration (FDA) and the Center for Drug Evaluation (CDE) under China's National Medical Products Administration (NMPA)—have established comprehensive and stringent safety evaluation requirements for gene editing products, including Prime Editing-based therapeutics. These requirements specifically address off-target effects, chromosomal structural variants, vector integration risks, and residual editing components. The FDA guidance document Human Gene Therapy Products Incorporating Human Genome Editing, published in January 2024, explicitly mandates comprehensive off-target risk assessments, emphasizing the synergistic use of bioinformatics, biochemical, and cellular methodologies for genome-wide analyses, and systematic evaluation of chromosomal integrity, clonal expansion risks, and the biological consequences of editing products (Figure 1).

Figure 1. Core safety assessment requirements for gene editing products as stipulated in the FDA guidance: Human Gene Therapy Products Incorporating Human Genome Editing

In assessing PE-mediated editing outcomes, conventional PCR-based Sanger sequencing methods are limited by low sensitivity (limit of detection: approximately 10%–20%), rendering them inadequate for accurate quantification of low-frequency editing events, and their low throughput precludes comprehensive characterization of multi-site nucleotide changes within the editing window. To address these analytical limitations, Generulor has developed a specialized on-target amplicon sequencing platform optimized for Prime Editing, based on high-throughput next-generation sequencing (NGS). By designing target-specific primers flanking the editing locus for high-depth amplicon sequencing, this platform enables precise quantification of intended editing efficiency and systematic characterization of the types and proportions of all unintended mutations—meeting both the detailed editing product characterization requirements of the research stage and providing comprehensive assessment data in compliance with regulatory requirements for IND submissions.

2. Principles of PE Prime Editing Efficiency Detection

Prime Editing efficiency detection technology leverages the ultra-high throughput and single-nucleotide resolution capabilities of next-generation sequencing (NGS), integrated with experimental workflows and bioinformatic analysis pipelines specifically designed for the complex editing patterns of Prime Editing. The platform employs site-specific amplification of genomic regions encompassing the editing target and its flanking sequences (typically 150–300 bp), followed by ultra-deep sequencing (≥1,000,000×) and advanced sequence alignment algorithms to achieve precise identification and quantification of all editing products.

The analytical workflow encompasses the following steps: (i) design of highly specific amplification primers based on the spacer sequence of the pegRNA and the reverse transcription (RT) template, ensuring complete coverage of the editing target, insertion/deletion boundaries, and flanking sequences; (ii) high-fidelity PCR amplification and library construction; (iii) ultra-deep paired-end sequencing (PE150) on the MGI platform, ensuring acquisition of ≥1,000,000 effective reads per editing locus; and (iv) application of a purpose-built Prime Editing analysis algorithm for precise identification and quantification of intended edits (precise substitutions/insertions/deletions), unintended indels, unintended base substitutions, and the frequencies and sequence characteristics of various complex editing combinations.

Compared to standard NGS amplicon sequencing, this platform offers four cardinal advantages: (1) Ultra-high sensitivity—with a detection limit of 0.1%, enabling identification of extremely low-frequency editing events and rare unintended mutations; (2) Complex editing resolution capacity—a specially optimized algorithm enabling accurate discrimination and quantification of multiple editing types (precise edits, partial edits, complex edits, etc.); (3) Editing purity assessment—precise calculation of the ratio of intended edits to total editing events, evaluating the overall precision of the Prime Editing system; (4) High-throughput processing—supporting simultaneous analysis of hundreds of samples and multiple target loci within a single experimental run, suited for large-scale candidate evaluation and quality control.

Figure 2. Schematic diagram of the PE editing efficiency detection workflow

3. Technical Innovations and Advantages in PE Prime Editing Efficiency Detection

3.1 Core Technical Innovations

3.1.1 Prime Editing–Specific Primer Design Strategy

A specialized primer design strategy has been developed to accommodate the complex editing mechanism of Prime Editing:

(1) Amplification regions provide complete coverage of the RT template-encoded region of the pegRNA and potential insertion/deletion boundaries, ensuring comprehensive capture of all possible editing products.

(2) Primer positions are placed outside regions potentially affected by editing, preventing amplification failure or bias attributable to target site mutations.

(3) Amplicon length is optimized (150–300 bp) to balance complete overlap coverage of the editing site by paired-end reads with comprehensive acquisition of editing information.

(4) High-fidelity polymerase and optimized PCR conditions are employed to minimize the introduction of artifactual mutations during amplification.

3.1.2 Prime Editing–Specific Bioinformatics Analysis

Analytical algorithms specifically designed for Prime Editing patterns have been developed:

(1) Precise identification of intended edits: reads are aligned against the designed editing sequence (derived from the RT template) to quantitatively determine the frequency of reads perfectly matching the intended edit.

(2) Classification of unintended mutations: systematic detection and classification of all editing products deviating from the intended outcome, including unintended indels, unintended base substitutions, partial edits (incomplete editing), and complex edits (multiple concurrent mutation types).

(3) Editing purity analysis: calculation of the ratio of intended edits to the sum of intended edits plus unintended mutations, evaluating the overall precision of the Prime Editing system.

(4) Sequence feature analysis of editing products: identification of sequence patterns among high-frequency unintended mutations, enabling characterization of mutational signatures potentially attributable to reverse transcription errors, template jumping, or DNA repair.

(5) Visualization output: provision of editing pattern heatmaps for intuitive representation of the comprehensive Prime Editing outcome landscape.

3.2 Visualization output: provision of editing pattern heatmaps for intuitive representation of the comprehensive Prime Editing outcome landscape.

Generulor has completed a comprehensive and systematic methodological validation. The technical performance metrics are as follows:

4. Applications and Service Advantages

4.1 Application Scenarios

The Prime Editing on-target amplicon sequencing technology encompasses broad applications across the entire continuum of gene editing therapeutic product development and regulatory evaluation:

(1) Prime Editor variant screening: Comparative evaluation of editing efficiency and precision across different PE versions (PE1, PE2, PE3, PE4, PE5, etc.) and engineered reverse transcriptases, guiding selection of the optimal editing system.

(2) pegRNA design optimization: Systematic assessment of the influence of RT template length, primer binding site (PBS) length, and nick position on editing efficiency and precision, enabling identification of optimal pegRNA design parameters.

(3) Editing condition optimization: Evaluation of the impact of delivery modality (AAV, LNP, RNP, etc.), cell type, and culture conditions on editing efficiency, facilitating process optimization.

(4) Quality control and batch release: Detection of editing efficiency and editing purity during the manufacture of cell therapy products to ensure product quality consistency.

(5) IND submission support: Provision of on-target editing data and methodological validation reports compliant with regulatory requirements for clinical trial applications.

(6) Preclinical safety evaluation: Assessment of editing efficiency, durability, and unintended mutation rates in animal models and organoids.

(7) Clinical sample monitoring: Detection of editing efficiency and stability in patient samples to support evaluation of clinical efficacy and safety.

4.2 Service Advantages

(1) Technical leadership: An assay platform optimized specifically for the complex editing patterns of Prime Editing, enabling accurate identification and quantification of all intended and unintended editing products.

(2) Certified quality management system: The laboratory simultaneously operates under the ISO 9001 quality management system and ISO/CNAS accreditation standards, ensuring data reliability and regulatory traceability.

(3) Comprehensive methodological validation: Full validation covering sensitivity, specificity, accuracy, linearity, and precision; validation reports are directly applicable to IND submissions.

(4) Standardized reporting: Analytical reports fully compliant with the latest NMPA-CDE and FDA guidance documents, comprehensively supporting regulatory submissions and inspections.

(5) Expert technical support: A technical team with extensive experience in gene editing product development and regulatory submissions, providing end-to-end consulting from experimental design through data interpretation.

(6) Proven track record: Established Prime Editing detection services for multiple leading gene therapy companies and research institutions.

5. Representative Report Examples for PE Prime Editing Efficiency Detection

Generulor provides comprehensive Prime Editing on-target analysis reports compliant with regulatory requirements, encompassing detailed sequencing data quality assessment, sample sequencing depth statistics, and alignment statistics. In addition, reports include the following core components:

(1) Sequencing data quality and alignment statistics: The report provides a detailed assessment of sequencing data quality, including the number of clean reads and clean ratio following raw data filtration, paired-end read merging efficiency (merge ratio), and amplicon reference sequence alignment efficiency (align ratio), comprehensively reflecting library quality and data reliability to ensure subsequent editing efficiency analyses are based on high-quality data

Figure 3. Summary table of read merging and alignment statistics (representative example)

(2) Quantitative analysis of gene editing efficiency: Employing the CRISPResso2 software framework, sequencing reads are aligned to reference sequences to enumerate the number of modified reads (Modified read counts) and calculate editing efficiency (Modified rate) in both experimental and control groups. The report outputs editing efficiency data both prior to (Raw) and following (Filtered) quality filtration; background noise is removed by computing the differential between experimental and control groups, enabling precise quantification of on-target editing efficiency.

Figure 4. Gene editing efficiency summary table (representative example)

(3) Classification of editing events: All detected editing events are subjected to fine-grained classification by mutation type, with separate enumeration of the frequencies of sole insertions (Only Ins), sole deletions (Only Del), sole substitutions (Only Sub), and various complex editing combinations, along with aggregated InDel rates. This analysis provides comprehensive characterization of the intended precise substitutions generated by PE editing and the distribution of all classes of unintended mutation products, providing data support for editing specificity assessment.

Figure 5. Editing classification summary table (representative example)

(4) Visualization of editing product sequences: All high-frequency editing events within the editing locus region (constituting >0.2% of total reads) are displayed at single-nucleotide resolution. The first row depicts the unedited reference amplicon sequence; subsequent rows represent the spectrum of detected editing sequences. The four nucleotides are distinguished by color; substitutions are marked in bold, insertions are highlighted with rectangular boxes, deletions are indicated with dashed lines, and the predicted cleavage site is denoted by a vertical dashed line. The right-hand panel displays, in sequential order, the experimental group read proportion, experimental group read count, control group read proportion, and control group read count—providing an intuitive visualization of the frequencies of each editing event and the differential between experimental and control groups, serving as direct sequence-level evidence for editing pattern analysis and specificity assessment.

Figure 6. Editing product visualization (representative example)

6. Service Content for PE Prime Editing Efficiency Detection

*Standard turnaround time: 20–30 business days.

7. Sample Requirements

*Notes: ① All samples must conform to the quality standards described above. ② Clients may also submit tissue or cell pellet samples for DNA extraction; tissue samples must weigh >50 mg, and cell pellets must contain >2×10⁷ cells per locus. ③ For non-standard sample types, please consult with the Generulor technical team in advance (Tel: 400-6309596; service@generulor.com).

8. References

[1] Anzalone AV, Randolph PB, Davis JR, et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature. 2019;576(7785):149–157.

[2] Chen， P. J.， et al. (2021). Enhanced prime editing systems by manipulating cellular determinants of editing outcomes. Cell， 184(22)， 5635-5652.

[3] Nelson， J. W.， et al. (2022). Engineered pegRNAs improve prime editing efficiency. Nature Biotechnology， 40(3)， 402-410.

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[7] U.S. Food and Drug Administration. Human Gene Therapy Products Incorporating Human Genome Editing: Guidance for Industry [EB/OL]. Silver Spring, MD: FDA, January 2024.