iPSCs PCR-free WGS Gene Editing Safety Assessment

iPSCs PCR-free WGS Gene Editing Safety Assessment

1. Background

1.1 Applications and Challenges of iPSCs in Regenerative Medicine

Induced pluripotent stem cells (induced pluripotent stem cells,   iPSCs) since their first successful establishment by Yamanaka et al. in 2006, have become an important tool in regenerative medicine, disease modeling, and drug screening [1]. iPSCs possess unlimited proliferation capacity and pluripotent differentiation potential, capable of differentiating into almost all cell types in the human body, providing an ideal cell source for cell replacement therapy and personalized medicine. However, iPSCs may accumulate genetic variations during reprogramming and long-term culture, including single nucleotide variants (SNVs), insertions/deletions (Indels), copy number variants (CNVs), and structural variants (SVs) [6]. These variations may affect the differentiation capacity and functional characteristics of iPSCs, or even trigger tumorigenicity risks. Therefore, rigorous genomic safety assessment must be conducted before iPSCs enter clinical applications.

1.2 Applications of Gene Editing Technology in iPSCs Research

Gene editing technologies represented by the CRISPR/Cas9 system have been widely applied in genetic modification of iPSCs for disease gene correction, gene function research, and cell engineering [2]. Although CRISPR/Cas9 technology possesses high efficiency and specificity, off-target effects remain one of the major safety concerns for clinical applications [3]. Particularly in iPSCs used for cell therapy, even low-frequency off-target events may pose significant safety risks after cell expansion.

1.3 The Role of Whole Genome Sequencing (WGS) in iPSCs Safety Assessment

Compared to candidate site detection methods (such as GUIDE-seq, CIRCLE-seq, SITE-seq), whole genome sequencing (WGS) can unbiasedly detect variations across the entire genome, including off-target sites beyond predictions, and is the officially recommendedmethod.

1.4 Advantages of PCR-free WGS Technology

Traditional WGS library preparation processes include PCR amplification steps, which may introduce amplification bias, GC bias, and PCR errors, affecting the accuracy of variant detection. PCR-free library construction technology has significant advantages by omitting the PCR amplification step and directly sequencing genomic DNA fragments:

(1) Reduced sequence bias: Uniform coverage of high and low GC regions, avoiding coverage unevenness caused by PCR amplification bias.

(2) Lower false positive rate: Eliminates errors and chimeric sequences introduced by PCR, improving the specificity of variant detection.

(3) Accurate detection of structural variants: Better identification of complex variant types such as large fragment insertions/deletions and chromosomal rearrangements.

(4) Accurate reflection of allele frequencies: Avoids the impact of PCR amplification bias on variant abundance and accurately quantifies mosaicism.

Figure 1. Schematic Comparison of PCR-free and Standard WGS Library Preparation

This figure demonstrates the workflow differences between the two library preparation methods. Standard WGS (left) includes PCR amplification steps, which may introduce sequence bias, GC bias, PCR errors, and uneven coverage. PCR-free WGS (right) omits the PCR amplification step and proceeds directly to sequencing, thereby achieving higher accuracy and more uniform coverage.

2. Technical Principles and Analysis Workflow

2.1 CRISPR/Cas9 Editing Principle

The CRISPR/Cas9 system guides Cas9 nuclease to specific target sites in the genome through sgRNA, recognizes the PAM sequence, binds to DNA, and generates DNA double-strand breaks (DSBs). Cells repair DSBs through two main pathways: non-homologous end joining (NHEJ) or homology-directed repair (HDR), thereby achieving editing purposes such as gene knockout (KO), gene knock-in (KI), or point mutation repair.

2.2 iPSCs PCR-free WGS Detection Workflow

This protocol adopts“off-target effect analysis”and“genome stability assessment”combined dual analysis strategy, comprehensiveassessmentiPSCsgenomic safety.

2.3 Core Logic of iPSCs Safety Assessment

Figure 2. iPSCs PCR-free WGS Detection Workflow

This flowchart shows the complete path from WGS sequencing to final analysis results. Leftmost branch: GRIDSS detects KI integration sites, including both on-target and off-target KI integrations. Left-center branch: SNV/InDel analysis, simultaneously performs sgRNA-dependent off-target analysis and stability assessment of independent variants. Right-center branch: SV analysis, used only for genome stability assessment, without sgRNA-dependent screening. Rightmost branch: CNV analysis, used only for genome stability assessment. These four analysis pathways together constitute a complete iPSCs genomic safety assessment system.

Figure 3. iPSCs Genome Stability Assessment Framework

This framework diagram shows the four core dimensions of iPSCs genome stability assessment: SNV/InDel (including sgRNA-dependent and independent types), SV, CNV, and KI integration. These four dimensions together constitute a comprehensive iPSCs safety assessment system.

2.4 Key Differences Between iPSCs Protocol and General DSB Protocol

3. Technical Advantages

4.   Application Scenarios

(1) iPSCs Gene Editing Safety Assessment: Conduct comprehensive genomic safety assessment of CRISPR/Cas9-edited iPSCs to ensure genetic stability of cells.

(2) iPSCs Quality Control: Perform genomic monitoring during iPSCs culture and passage to promptly identify and eliminate clones with genomic instability.

(3) Cell Therapy Product Development: Provide genomic safety data for iPSCs-based cell therapy products to meet regulatory requirements.

(4) Disease Model Construction: Verify the accuracy and safety of gene editing when constructing iPSCs-based disease models.

(5) Basic Research: Provide data support for iPSCs gene editing strategy optimization and sgRNA design improvement.

5.Example Report

5.1 Data Quality Control and Alignment

Table 5.1-1 Raw Data Quality Control Table Statistics

Statistical Visualization of Raw Sequencing Data Alignment to Reference Genome:

Figure 4. Sample Genome Sequencing Depth and Coverage Statistics

5.2 Important Results Display:

Figure 5. Sample Intersection SNV Venn Diagram

Table 5.2-1 Exonic Variant Annotation Results

Genome-Wide Variant Visualization:To intuitively display the genome-wide distribution features of somatic variants detected in experimental group samples, we use Circos for visualization analysis. This figure shows from outer to inner circles: chromosome karyotype bands, SNV distribution density, InDel distribution density, copy number variants (CNV), and inter-chromosomal connection relationships of structural variants (SV).

Figure 6. Genome-Wide Variant Circos Plot

Dependent Off-Target Visualization:svgFigureupper left shows the sample name, with the sgRNA PAM at the endsequence, sequenceaboveasposition labels, belowasall dependent off-target sites, According to the allele frequency of off-target sites( Editing efficiency)fromlargeto⼩arranged, eachsvgFiguredisplaysEditing efficiencyranked top50 off-target sites.

Figure 7. sgRNA-Dependent Mismatch Visualization

6. Service Content and Sample Requirements

6.1 AssessmentService Content

6.2 AssessmentSample Submission Requirements

7.References

[1] Takahashi K, Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006;126(4):663-676.

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

[3] Kim D, Luk K, Wolfe SA, Kim JS. Evaluating and enhancing target specificity of gene-editing nucleases and deaminases. Annu Rev Biochem. 2019;88:191-220.

[4] Kosicki M, Tomberg K, Bradley A. Repair of double-strand breaks induced by CRISPR-Cas9 leads to large deletions and complex rearrangements. Nat Biotechnol. 2018;36(8):765-771.

[5] Nakanishi M, Otsu M. Development of Sendai virus vectors and their potential applications in gene therapy and regenerative medicine. Curr Gene Ther. 2012;12(5):410-416.

[6] Yoshihara M, Hayashizaki Y, Murakawa Y. Genomic instability of iPSCs: challenges towards their clinical applications. Stem Cell Rev Rep. 2017;13(1):7-16.