From Nucleases to Prime Editing: CleanFinder Protocol for Precise Analysis of Heterogeneous Gene Editing Results
Background
Since the development of CRISPR-Cas9 nucleases, the scope of applications has rapidly expanded to include Base Editing (BE) for precise single-nucleotide correction, Prime Editing (PE) for direct DNA writing and erasing, and mitochondrial genome editing technologies. While attempts to target specific sequences continue in laboratories worldwide, actual cellular responses are far from simple. Cell populations following gene editing often form complex allele mixtures, including random insertions/deletions (indels), unintended adjacent base substitutions, and partial inversions, rather than the intended single outcome.
Amplicon sequencing technology is used as the standard analytical method to identify these heterogeneous editing results. However, existing bioinformatics tools are often biased toward specific editing methods or rely on Linux command-line interfaces (CLI), creating high entry barriers for general biologists. Furthermore, uploading large-scale FASTQ sequencing data to overseas cloud servers frequently raises concerns regarding the leakage of patient genetic information and network bottlenecks. There have also been persistent criticisms that these tools fail to objectively filter out allelic dropout—a phenomenon where specific alleles are lost during PCR amplification—thereby distorting actual mutation frequencies.
Key Findings
A research team led by Dr. Alessandro Rossi of the Leibniz Research Institute for Environmental Medicine (IUF) in Germany has published detailed analytical procedures for the genome editing analysis platform 'CleanFinder' in the Cell Press journal STAR Protocols. Alessandro Rossi at the Leibniz Institute for Environmental Medicine (IUF) in Germany published the detailed analytical procedures for 'CleanFinder,' a genome editing analysis platform, in the Cell Press journal 'STAR Protocols.' The team provides systematic guidance covering the entire process, from constructing reference sequence data and FASTQ preprocessing to web browser-based execution and large-scale command-line processing.
CleanFinder employs a constrained semi-global alignment method. Based on this, it identifies micro-deletions and point mutations in mitochondrial targets, as well as multiple variants from prime editing, with high precision. The research team has formalized step-by-step interpretation criteria to objectively derive genotype distribution curves, DNA repair patterns, and editing efficiencies.
As a key device for enhancing reliability, an allele detection module utilizes heterozygous single nucleotide polymorphisms (SNPs) as markers. This module identifies allelic dropout caused by amplification errors to prevent statistical distortion. The protocol also includes detailed instructions for using a high-speed computation (Turbo) mode to support large-scale screening. The analysis speed and reproducibility of this mode have already been verified in large-scale experiments involving the treatment of 1,849 small molecule compounds on human embryonic kidney cells (HEK293T).
Significance and Outlook
The greatest advantage of the CleanFinder protocol is the coexistence of data security and analytical accessibility. All computations are completed using only the web browser engine of a personal computer without the need for separate software installation. This structure allows sensitive patient-derived cell sequencing data to be processed on internal laboratory equipment without being exported to external networks.
Thanks to its user-friendly interface, laboratories without professional bioinformaticians can obtain high-quality visualization graphs and quantified data within minutes. The protocol is expected to contribute to standardizing the Quality Control (QC) standards required in preclinical and clinical research for Cell and Gene Therapies (CGT). It will also significantly accelerate the selection of candidate substances by allowing various editing platforms to be compared within a single analytical framework.
Technical challenges remain. There are clear physical limitations to single-amplicon analysis when attempting to capture large-scale structural variants, such as large chromosomal deletions or translocations of several kilobases (kb) or more. Future expansion into next-generation analytical protocols that organically integrate with long-read sequencing platforms is necessary.
Genome editing experiments often produce heterogeneous allele mixtures that require structured genotyping. Here, we present a protocol for analyzing amplicon sequencing data from nuclease, base, prime, and mitochondrial base editing experiments using CleanFinder. We describe steps for preparing reference sequences and FASTQ files and running browser-native or command-line analyses. We then detail interpretation of genotype distributions, repair signatures, editing efficiencies, and allelic dropout. For complete details on the use and execution of this protocol, please refer to Ramachandran et al.
The CleanFinder protocol provides an immediate quality assurance tool for cell and gene therapy developers and clinical researchers. In the production of Chimeric Antigen Receptor T-cell (CAR-T) therapies, there is an increasing trend of performing simultaneous multiple gene knockouts and therapeutic gene insertions to prevent immune rejection. In such cases, it is impossible to ensure the safety of therapeutic agents if micro-mutations occurring differently at each target site and unintended bystander mutations cannot be accurately tracked.
Applying CleanFinder in Good Manufacturing Practice (GMP) facilities allows for the rapid cross-verification of raw data from on-site sequencers without external server connections. By tracking heterozygous SNPs to exclude allelic dropout errors, it becomes easier to strictly control quality bias between batches. This is evaluated to significantly advance the commercialization timeline of new drugs by reducing the costs of generating genome integrity reports for clinical trials and shortening the duration of outsourced analysis.