Highly Efficient and Comprehensive Genome Editing Tool: A High-Performance Prime Editing Platform for Multiplexed Dropout Screening

In the field of genome editing, Prime Editing has garnered significant attention for its precision and flexibility. This method allows for precise single-base substitutions and small insertions or deletions in the genome without inducing double-strand breaks (DSBs). However, its application in large-scale functional genomics has been hindered by low and variable editing efficiencies. To address this challenge, a research collaboration involving teams from Princeton University, the University of California San Diego, and other institutions developed a highly efficient Prime Editing platform that marks a breakthrough in solving the problem of multiplexed dropout screening. Their study, titled “A benchmarked, high-efficiency prime editing platform for multiplexed dropout screening,” was published in the January 2025 issue of Nature Methods.

Background and Motivation

Advances in human genome sequencing have led to the establishment of large-scale genetic variants databases, providing a foundation for studies linking genetic mutations to human diseases and traits. However, functional validation of these associations faces significant challenges with existing gene-editing technologies. For example, traditional homology-directed repair (HDR) approaches using Cas9 exhibit low efficiency and imprecise editing, making them unsuitable for high-throughput functional screening. Recent advancements in base editing provide higher efficiency for site-specific editing, but they are limited in editing scope (e.g., C>T or G>A conversions) and often introduce undesired bystander mutations. Prime Editing is considered a promising technology to address these limitations, offering greater compatibility and potential for precise editing. However, efficiency bottlenecks have prevented its widespread use.

This study aims to assess the potential of Prime Editing for high-throughput functional screening and to design a more operational editing platform to enable a streamlined workflow from editing to screening. The improved platform facilitates precise “multiplexed dropout” capabilities, offering an essential tool for studying the functional consequences of genetic mutations and for deepening our understanding of human genetic variations.

Research Team and Source

The study was conducted under the guidance of Professor Britt Adamson at the Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, in collaboration with researchers from the University of California San Diego and other institutions. The paper was published in the January 2025 issue of Nature Methods.

Experimental Workflow and Design

The study centered around the development of an optimized Prime Editing platform, using multiple steps and experimental methods to comprehensively evaluate the efficiency of multiplexed dropout screening. Key methods and workflows include:

1. Development of an Enhanced Prime Editing System

The team developed two K562 cell lines with stable expression of Prime Editor proteins: PE2 and PEmax. The PEmax cell line, with an optimized reverse transcriptase (RT) and Cas9 mutant, achieved higher editing efficiencies. To further improve precision, the researchers constructed a PEmax-derived cell line, PEmaxKO, in a mismatch repair (MMR)-deficient background.

Quantitative fluorescence analyses over a one-month period revealed that PEmax significantly outperformed PE2 in terms of editing efficiency, with a markedly higher proportion of precise edits and minimal unintended outcomes.

2. Design of Specialized gRNA Libraries and Optimization of epegRNA

The study engineered a novel enhanced prime editing guide RNA (epegRNA) with the TEVopreq1 motif to enhance stability. Two self-targeting sensor libraries were designed and tested: - +5 G>H Mutation Screening Library: Targeted 640 sites with three different mutations (+5 G>A, G>T, G>C) at the same +5 position to analyze the influence of different designs on editing efficiency. - Broad-Range Editing Detection Library: Targeted the mouse ZRS enhancer with diverse site-specific and variant-specific single-base substitutions.

Deep sequencing and predictive modeling identified significant factors affecting editing efficiency, including RNA template (RTT) length, PBS length, and mutation position. Notably, edits proximal to the Cas9(nickase) cleavage site (±5 positions) demonstrated much higher efficiencies.

3. Large-Scale Dropout Screening and Data Analysis

The researchers applied the optimized platform to design a massive 240,000-epegRNA multiplexed dropout screening library, named STOPPR. The library included: - 130,000 epegRNAs to induce 129,696 nonsense mutations. - 90,000 matched “synonymous mutation” controls. - Additional “no edit” and “non-targeting” controls for experimental calibration.

Targeting the STOPPR library to K562 cell lines demonstrated that under the PEmaxKO conditions, the system could robustly generate phenotypically significant mutations related to dropout effects, particularly in essential genes crucial for cell growth.

4. Independent Validation and High-Throughput Applications

The functional impact of 10 nonsense mutations was independently validated, with phenotypic effects reproducing growth inhibition, and on-target sequences supporting the attribution of observed phenotypes to intended edits. Additionally, matched synonymous mutation control experiments further verified the platform’s specificity.

Experimental Results and Key Findings

The research achieved the following major breakthroughs: 1. High Efficiency and Precision: For certain mutations (e.g., +5 nonsense mutations), success rates reached 81%, with the accuracy of precise edits as high as 95%. 2. Phenotype-Specific Validation: Growth-inhibitory phenotypes associated with nonsense mutations were observed in 89.3% of targeted genes, demonstrating high specificity compared to controls. 3. Design Principles: Long RTTs, edits targeting earlier regions of genes, and anti-sense spacer designs yielded overall superior results.

Significance and Value of the Research

This work signifies a major advance in genome editing. By optimizing the Prime Editing platform, the study significantly improved editing efficiency and showcased the technology’s applicability in functional genomics. The experiments resolved a critical barrier—inefficient editing rates—and provided guiding principles for designing high-performing epegRNAs. Additionally, the ability to detect nonsense mutations in essential genes offers a vital tool for annotating gene functions and identifying disease-related mutations.

Highlights of the Research

1. Technological Advancement: This is the first time Prime Editing has been adapted for large-scale, efficient screening workflows without requiring additional genome sequencing.
2. Broad Applicability: The study quantitatively evaluated tens of thousands of genetic variants, paving the way for personalized medicine applications and gene function screening.
3. Methodological Innovation: By optimizing the platform in an MMR-deficient background and incorporating high-throughput sensor libraries, the cost of data acquisition was significantly reduced.

Through this research, Prime Editing has made an important leap forward as a revolutionary tool in functional genomics. Further refinement of screening systems and adjustments for complex genomic backgrounds may enable artificial intelligence-assisted design models, advancing the precision of disease diagnostics and gene therapy.