scDNA-seq Multiplex Editing Heterogeneity Assay

scDNA-seq Multiplex Editing Heterogeneity Assay

1. Background

The advent of CRISPR-Cas9 gene editing technology has fundamentally transformed the landscape of gene therapy. By precisely cleaving double-stranded DNA and leveraging the cell’s endogenous repair machinery to introduce targeted modifications, this technology has been successfully applied across multiple domains, including the treatment of hereditary diseases, immune cell engineering, and tumor immunotherapy. The FDA approval of the first CRISPR-based gene editing therapy in 2023 marked the formal entry of this revolutionary technology into a new era of clinical application[1,2].

However, while CRISPR-Cas9 technology provides precise editing capabilities, it also faces safety challenges including off-target effects and structural variations. Studies have demonstrated that the Cas9 nuclease may generate unintended insertions and deletions (indels) at non-target sites, or induce severe structural variations such as chromosomal translocations, large-segment deletions, and chromosome loss between multiple cleavage sites[3,4].These potential risks necessitate comprehensive and precise genetic safety assessment of gene editing products.

Although conventional bulk sequencing methods can quantify editing efficiency at the population level, they are unable to resolve editing outcomes at the single-cell level. Particularly in multiplex editing scenarios, conventional methods cannot determine the co-occurrence patterns of edits across multiple target sites, allelic zygosity status, or clonal heterogeneity. To address this technological bottleneck, Generulor has introduced and optimized single-cell DNA sequencing technology based on the Tapestri platform, providing comprehensive genetic characterization of CRISPR gene editing products at single-cell resolution[5,6].

2. Principles of Single-Cell DNA Sequencing

The core principle of single-cell DNA sequencing technology involves encapsulating individual cells within discrete reaction compartments using microfluidic droplet technology, followed by cell lysis, DNA release, targeted amplification, and cell barcode labeling, ultimately enabling high-throughput parallel analysis of thousands to tens of thousands of single cells.

The detection workflow comprises the following steps: first, the cell suspension is co-encapsulated with lysis buffer in microdroplets for cell lysis and genomic DNA release; subsequently, the lysate is combined with PCR reagents containing cell-specific barcodes to form barcode-labeled droplets, followed by multiplex PCR-based targeted amplification of genomic regions of interest (including on-target editing sites and predicted off-target sites); after library construction and high-throughput sequencing, data are analyzed through a specialized bioinformatics pipeline, achieving editing characterization at single-cell resolution.

Figure 1. Schematic workflow of single-cell DNA sequencing for CRISPR gene editing detection

3. Technological Innovations and Advantages of Single-Cell DNA Sequencing

3.1 Core Technological Innovations

3.1.1 High-Throughput Single-Cell Analysis Capability

Based on the Tapestri V3 chemistry system, high-efficiency single-cell capture and analysis are achieved:

(1)   A single experiment can analyze 4,000–10,000 single cells, yielding statistically robust datasets;

(2) The optimized droplet chemistry ensures uniform amplification efficiency and high cell capture rates;

(3) Supports multiplex targeted sequencing panel designs encompassing over 100 targets.

3.1.2 Editing Analysis at Single-Cell Resolution

Overcoming the limitations of conventional bulk sequencing, multidimensional single-cell editing characterization is achieved:

(1) Precise quantification of the editing status at each target site per cell (unedited/monoallelic/biallelic);

(2) Resolution of co-occurrence patterns and combinatorial features of multiplex editing;

(3) Identification of cell clones harboring specific indel combinations and their heterogeneity distribution.

3.1.3 Chromosomal Translocation Detection

Quantitative detection of structural variations is achieved through chimeric read identification algorithms:

(1) Detection of chromosomal translocation events between on-target sites;

(2) Identification of translocations between on-target and off-target sites;

(3) Provision of quantitative translocation frequency data at the single-cell level.

3.2 Methodological Validation and Performance Metrics

Generulor conducted comprehensive systematic validation using isogenic clonal cell lines. Through systematic comparison with bulk NGS methods (rhAmpSeq), the detection performance of the single-cell DNA sequencing platform was verified:

4. Application Scenarios and Service Advantages

4.1 Application Scenarios

Single-cell DNA sequencing technology has broad application scenarios throughout the development and regulatory processes of CRISPR gene editing products:

(1)Quality control of CAR-T/TCR-T cell product editing: Precise characterization of the editing status and zygosity distribution of engineered cells at single-cell resolution, identifying editing non-uniformity and potentially aberrant cell populations;

(2)Safety evaluation of multiplex editing products: Analysis of gRNA editing co-occurrence patterns within the same cell, assessing the cumulative effects of editing and potential genotoxicity risks;

(3)IND filing support for gene editing products: Providing single-cell level editing characterization, off-target activity, and safety assessment reports compliant with regulatory requirements, supporting the completeness and traceability of filing documentation;

(4)Preclinical research data support: Systematic comparison of differences in efficiency and safety across various gRNAA designs and editing strategies, providing data-driven evidence for process optimization and strategy selection;

(5)Clinical sample analysis: Supporting quality testing and safety monitoring of engineered cell products during clinical trials, enabling continuous assessment of editing status.

4.2 Service Advantages

(1) Technological leadership: Leveraging the established Tapestri single-cell DNA sequencing platform integrated with the scEDIT specialized analytical pipeline, delivering industry-leading single-cell editing analysis capabilities;

(2) Accredited quality management: The laboratory operates under both ISO 9001 quality management system and ISO/IEC 17025 accreditation standards for testing and calibration laboratory competence;

(3) Comprehensive methodological validation: Comprehensive methodological validation has been completed, encompassing sensitivity, specificity, accuracy, and precision;

(4) Standardized reporting system: Analytical reports compliant with the latest FDA and CDE guidance requirements;

(5) Extensive track record: Successfully assisted multiple leading companies in completing single-cell analysis and safety evaluation of CRISPR-edited products.

5. Exemplary Report of Single-Cell DNA Sequencing for CRISPR Editing Detection

Generulor provides comprehensive single-cell CRISPR editing analysis reports that meet regulatory requirements, encompassing detailed sequencing data quality assessment, cell capture statistics, and target coverage analysis as foundational information. Furthermore, the report systematically analyzes on-target and off-target editing events at the single-cell level, quantitatively evaluating editing efficiency, allelic status, multiplex co-occurrence relationships, and potential chromosomal translocation risks, while integrating functional risk assessment of off-target sites for tiered safety evaluation. The overall analytical workflow and key decision nodes are summarized in flowchart format (Figure 2)..

This flowchart systematically illustrates the overall analytical pathway for single-cell CRISPR gene editing safety assessment. Centered on the target gene intended for editing, potential off-target sites are predicted in silico based on sequence features, algorithmic models, and literature reports, with high-probability off-target sites selected for inclusion in the detection scope. On this basis, a customized amplicon panel covering both on-target and off-target sites is designed, followed by single-cell sequencing library construction and data acquisition. Upon obtaining sequencing data, multi-layered bioinformatics analysis is conducted: single-cell editing status is resolved through scEDIT, quantitatively assessing the editing efficiency, allelic status, and multiplex editing co-occurrence at both on-target and off-target sites, with visualization analysis illustrating the distribution characteristics of cell populations with different editing states; concurrently, utilizing the intermediate read files generated by scEDIT, a customized analytical pipeline is constructed for the detection and quantification of potential chromosomal translocation events, with particular emphasis on evaluating structural variation risks between on-target and off-target sites. Building upon these results, a comprehensive risk stratification of off-target events is performed by integrating off-target editing efficiency and the functional risk of associated genes, culminating in an integrated single-cell gene editing safety assessment report that provides the basis for risk evaluation and decision-making for gene editing products.

Figure 2. Single-Cell DNA Sequencing for CRISPR Gene Editing Detection Data Analysis Pipeline Schematic

The report encompasses the following core contents:

(1) On-Target Editing Efficiency and Zygosity Analysis:Provides editing efficiency statistics for each target site at the cell and allele levels, differentiating the proportions of unedited, monoallelically edited, and biallelically edited cells, enabling precise assessment of the purity and uniformity of edited products.

Figure 3. On-target Editing Efficiency and Zygosity Analysis

(2)Multiplex Editing Co-occurrence Analysis:Displays the combinatorial patterns of multiple editing targets at the single-cell level, calculates the cell frequency of each editing combination, and evaluates the actual proportion of target cell populations (e.g., cells with simultaneous TCR and PD-1 knockout).

Figure 4. Heatmap Analysis of Multiplex Editing Co-occurrence

(3) Off-Target Activity Detection and Assessment:Performs editing detection at the single-cell level for predicted off-target sites, quantitatively analyzing the editing frequency at each off-target site. For off-target sites with elevated editing frequencies and higher risk levels (e.g., off-target-6 in Table 1), further zygosity analysis is conducted along with assessment of co-occurrence relationships with on-target editing events, thereby providing critical data support for gene editing safety evaluation.

Table 1 Off-target Site Editing Activity Detection Results

(4)Chromosomal Translocation Detection:Circos plots are used to visualize detected chromosomal translocation events, including translocations between on-target sites and between on-target and off-target sites, providing quantitative data on translocation frequencies and statistics on the proportion of cells harboring translocations.

Figure 5. Circos Plot Visualization of Chromosomal Translocations

(5) Cell Clone Diversity Analysis:Shannon diversity index and other statistical metrics are employed to evaluate the clonal composition of edited products, identifying dominant clones and heterogeneity distribution, providing a basis for product batch consistency assessment.

Figure 6. Treemap Visualization of Editing Outcome Diversity

6. Service Scope for Single-Cell DNA Sequencing-Based CRISPR Editing Detection

*Turnaround time: standard workflow30-40 business days;

7. Sample Requirements

*Note: (1) All samples must meet the quality standards described above to ensure the accuracy and reliability of detection results; (2) For special sample types, please consult with the Generulor technical team in advance (Tel: 400-6309596; Product ordering/Technical support: service@generulor.com).

8. References

[1] Doudna JA, Charpentier E. (2014). Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science, 346(6213):1258096.

[2] Gillmore JD, et al. (2021). CRISPR-Cas9 In Vivo Gene Editing for Transthyretin Amyloidosis. N Engl J Med, 385:493-502.

[3] Nahmad AD, et al. (2022). Frequent aneuploidy in primary human T cells after CRISPR-Cas9 cleavage. Nat Biotechnol, 40:1807-1813.

[4] Tsuchida CA, et al. (2023). Mitigation of chromosome loss in clinical CRISPR-Cas9-engineered T cells. Cell, 186:4567-4582.

[5] ten Hacken E, et al. (2020). High throughput single-cell detection of multiplex CRISPR-edited gene modifications. Genome Biology, 21:266.

[6] Kalter N, et al. (2025). Precise measurement of CRISPR genome editing outcomes through single-cell DNA sequencing. Mol Ther Methods Clin Dev, 33:101449.