±õ±·¶Ù±«°ä·¡-²õ±ð±ç®: A New Standard for Genome-Wide DNA Break Characterisation in Gene Editing 

Gene editing safety is ultimately a question of DNA break behaviour. 

Where do breaks occur? 
How often do they occur? 
Under what conditions do they persist or resolve? 
And can those events be measured reproducibly in the cell types that matter? 

As gene editing technologies move from research settings toward clinical translation, answering these questions with precision is no longer optional. It’s foundational.

Despite the rapid evolution of CRISPR-Cas systems, base editors, and prime editors, the tools used to characterise off-target activity have not always kept pace. Many widely adopted methods rely on indirect readouts, PCR-amplified libraries, or fragmented multi-assay workflows. Others measure the final genomic outcome long after editing has occurred, rather than capturing the break event itself.

The consequence is familiar to many teams: incomplete visibility, ambiguous interpretation, and difficulty standardising data across discovery, optimisation, and IND-enabling studies.

±õ±·¶Ù±«°ä·¡-²õ±ð±ç® has been developed to address this gap directly. 

Why direct DNA break characterization matters

Nuclease-based gene editing systems are designed to introduce targeted DNA damage at specific genomic loci. However, no editing system operates with perfect specificity. In addition to the intended on-target modification, unintended off-target activity can occur across the genome.

For nuclease-based systems, these events often manifest as double-strand breaks (DSBs), which represent a significant genotoxic risk. Off-target DNA breaks can lead to large-scale genomic rearrangements, activation of oncogenes, disruption of tumour suppressor genes, and other adverse outcomes.

Importantly, even newer approaches such as base editing and prime editing, often positioned as avoiding DSBs—have been shown to induce DNA breaks and other forms of genotoxicity under certain conditions.

As regulatory expectations evolve, empirical, genome-wide evidence of editing activity is increasingly required. Regulatory agencies including the FDA and EMA now expect unbiased, genome-wide characterisation in clinically relevant cell types, rather than reliance solely on in silico or in vitro approaches.

There is also a clear shift toward earlier assessment during discovery, enabling teams to eliminate suboptimal candidates before significant time and cost are invested.

Despite this, many programmes still lack a scalable, sensitive, cell-based method capable of directly measuring both on-target and off-target DNA break activity early enough to influence decision-making. 

A different approach: capturing breaks at their point of occurrence

±õ±·¶Ù±«°ä·¡-²õ±ð±ç® is a scalable, genome-wide, in cellulo platform for the direct detection and quantification of DNA breaks.

Rather than extracting genomic DNA first and labelling break ends later, ±õ±·¶Ù±«°ä·¡-²õ±ð±ç® performs in situ break labelling within fixed and permeabilised cells. This preserves the genomic context of break events as they existed inside the cell and avoids distortions introduced by post-extraction manipulation and PCR amplification.

Each break end is directly labelled with sequencing adapters, enabling sequencing to initiate from the break itself. Because the workflow is PCR-free, each sequencing read corresponds to a single captured break event, providing a quantitative and unbiased representation of DNA break frequency.

This PCR-free design is central to quantitative confidence, particularly when measuring low-frequency off-target events. 

From break labelling to sequencing

Following in situ labelling, genomic DNA is extracted and mechanically fragmented to generate sequencing-compatible fragments. A partially functional sequencing adapter is ligated to fragmented ends, creating a selective library architecture.

Only fragments that carry both the break-labelled adapter and the complementary sequencing adapter form functional constructs capable of binding to the sequencing flow cell. Unlabelled genomic fragments are rendered non-functional.

This selective library design enriches specifically for break-labelled fragments, dramatically improving sensitivity while reducing the sequencing depth required to detect rare events.

The output is genome-wide, single-nucleotide resolution mapping of DNA breaks across the entire genome.  

Integrated bioinformatics for decision-ready outputs

Sequencing data generated by ±õ±·¶Ù±«°ä·¡-²õ±ð±ç® are processed through an integrated bioinformatics platform purpose-built for genome-wide break mapping.

Reads are mapped to the reference genome, break sites are resolved at base-level precision, and candidate on- and off-target sites are identified through a dual analytical framework combining frequency-based and homology-based analysis.

This approach enables:

• Quantitative assessment of break frequency (enabled by PCR-free design)

• Cross-referencing with predicted cleavage sites

• Nomination and ranking of candidate off-target sites based on evidence of nuclease-induced activity

Each candidate site is assigned a probability score reflecting the likelihood of true induction versus background noise.

Outputs are delivered through structured, interpretable reports, including nomination tables, break site plots, mismatch plots, and supporting datasets suitable for both discovery optimisation and regulatory documentation.

The emphasis is not simply on detection, but on clarity, prioritisation, and decision-making.

What ±õ±·¶Ù±«°ä·¡-²õ±ð±ç® delivers

At its core, ±õ±·¶Ù±«°ä·¡-²õ±ð±ç® provides:

• Genome-wide, single-nucleotide resolution mapping of DNA breaks

• Simultaneous characterisation of on-target and off-target activity within a single workflow

• Measurement of both induced and endogenous background DNA breaks

• Compatibility with major gene editing systems, including CRISPR, TALENs, Zinc-Fingers, as well as base and prime editing

• Broad applicability across primary cells, stem cells, T cells, iPSCs, and immortalised cell lines

• A standardised, in-house workflow capable of delivering results within days

Running the platform in-house ensures full control of data, auditability, and programme confidentiality—an increasingly important consideration as programmes move toward regulatory submission.

Applications across the gene editing pipeline

One of the strengths of ±õ±·¶Ù±«°ä·¡-²õ±ð±ç® is its flexibility across development stages. 

Discovery
±õ±·¶Ù±«°ä·¡-²õ±ð±ç® enables rapid, side-by-side comparison of guide RNAs, nuclease variants, and editing conditions, generating full on- and off-target profiles to support early candidate selection and programme de-risking.

Lead characterisation and optimisation
By sampling multiple timepoints post-editing, teams can capture break formation and repair dynamics, providing insight into editing kinetics, nuclease behaviour, and cell-type specific responses. These data inform optimisation strategies, delivery approaches, and nuclease engineering decisions.

Translational and IND-enabling studies
Regulatory expectations increasingly require unbiased, genome-wide data generated in clinically relevant systems using well-characterised methods. ±õ±·¶Ù±«°ä·¡-²õ±ð±ç® provides reproducible, standardised outputs suitable for inclusion in IND data packages, with clear structure and traceability.

Across each stage, the workflow remains consistent, reducing variability and enabling continuity from discovery through to clinical translation.

Raising the standard for break analysis

As gene editing technologies become more powerful, the standard for genomic safety assessment rises alongside them.

Empirical, genome-wide evidence of editing activity is no longer a late-stage requirement—it is an expectation throughout development.

±õ±·¶Ù±«°ä·¡-²õ±ð±ç® addresses this need by directly capturing DNA breaks at their point of formation, within intact cells, without PCR amplification. The result is quantitative precision, single-nucleotide resolution, and an integrated analytical framework that translates complex sequencing data into clear, decision-ready outputs.

For gene editing teams seeking to de-risk programmes, accelerate iteration cycles, and build robust datasets aligned with modern regulatory expectations, ±õ±·¶Ù±«°ä·¡-²õ±ð±ç® represents a shift from indirect inference to direct measurement.

And in genome editing, direct measurement is what ultimately builds confidence.