Comprehensive Assessment of the Genomic Stability of Human Induced Pluripotent Stem Cells for Clinical Applications

Comprehensive Assessment of the Genomic Stability of Human Induced Pluripotent Stem Cells for Clinical Applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive Assessment of the Genomic Stability of Human Induced Pluripotent Stem Cells for Clinical Applications Kehua Zhang, Tao Na, Chuncui Jia, Xianghe Yuan, Meichen Guo, Xu Yang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7522863/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Stem Cell Research & Therapy → Version 1 posted 7 You are reading this latest preprint version Abstract Background Human induced pluripotent stem cells (hiPSCs) acquire genomic instability during reprogramming and culture, which poses significant risks for clinical applications. Current detection methods, such as karyotyping analysis, often fail to identify critical submicroscopic variations. This highlights an urgent need for comprehensive genomic surveillance strategies. Methods Three human iPSC lines were continually cultured in vitro for 50 passages, with genome stability evaluated every 10 passages. The evaluation methods included karyotyping to detect chromosomal abnormalities, optical genome mapping (OGM) to identify copy number variations (CNVs) and structural variants (SVs), whole-exome sequencing (WES) to detect coding mutations, and RNA sequencing (RNA-seq) to detect the changes of gene expression. Results We detected accumulating chromosomal abnormalities (e.g., trisomy 12), SVs, CNVs, and sequence mutations in three hiPSC lines during extended culture. OGM effectively identified SVs and CNVs below karyotyping resolution, particularly recurrent genome abnormalities such as gains on chr17q, chr12p and chr20q. WES revealed coding mutations, including germline short variants and newly acquired somatic mutations, some of which were associated with tumors or diseases, such as CDH1 , BCOR . Transcriptional changes correlated with genomic alterations, including dysregulation of oncogenes such as BCL2L1 , KRAS and MDM2 . Results demonstrate that each method had unique detection capabilities and limitations, and only integrative approaches can comprehensively identify genomic abnormalities. Conclusions This study established a comprehensive strategy for evaluating the genetic stability of hiPSCs by integrating karyotyping, OGM, WES, and RNA-seq. This comprehensive strategy can be applied to scenarios such as hiPSC clone screening, establishment of cell bank passages, and quality control of hiPSC-derived products. It provides a reliable genetic stability evaluation protocol to support the safe clinical application of hiPSC-related products. Human induced pluripotent stem cells genome stability quality control optical genome mapping cell therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Induced pluripotent stem cells (iPSCs) are a population of cells with pluripotency, obtained by reprogramming adult cells, and capable of differentiating into various cell types. Since Shinya Yamanaka and his colleagues first reported in 2006 that fibroblasts could be induced into pluripotent stem cells by introducing four transcription factors (OCT4, SOX2, KLF4, and c-MYC), iPSCs have been widely used in disease modeling, drug screening, and regenerative medicine [ 1 – 3 ]. In the field of cell therapy drug research, products derived from human iPSCs (hiPSCs), such as retinal pigment epithelial cells, neural precursor cells, islet cells, and natural killer cells, have entered the clinical trial phase and show promising prospects for application [ 4 – 7 ]. However, hiPSCs are susceptible to genomic instability throughout the reprogramming and extended culture processes, which poses a potential risk to the clinical application of cell-based therapies products derived from them [ 8 , 9 ]. Such genomic variations might originate from the cells’ initial state, or they could emerge during the reprogramming of somatic cells into iPSCs, a process that involves significant epigenetic and transcriptional reconfigurations, thereby increasing the likelihood of genomic instability [ 10 – 12 ]. During subsequent amplification and the establishment of cell banks, hiPSCs are also vulnerable to culture-induced genomic alterations, particularly some recurrent genomic abnormalities, which lead to selective growth advantage of mutant cells in culture conditions, potentially due to the altered expression or function of one or more mutated genes [ 13 , 14 ]. Genomic instability encompasses a range of alterations, including single-nucleotide variations (SNVs) and small insertions and deletions (Indels), as well as more extensive rearrangements such as copy number variations (CNVs) and structural variations (SVs), which involve the gain or loss of entire chromosomes or segments, leading to chromosomal aberrations like deletions, duplications, amplifications, inversions, and translocations [ 10 ]. Genomic instability not only affects the differentiation capacity of iPSCs but may also trigger safety issues such as tumorigenesis. Genomic instability may lead to a decrease or deviation in the differentiation capacity of iPSCs. For instance, the loss or amplification of certain chromosomal segments can affect the expression of genes related to differentiation via interactions with epigenetic variations, resulting in a reduced potential for iPSCs to differentiate into specific cell types [ 15 , 16 ]. Chromosomal abnormalities may also lead to increased heterogeneity, making the differentiation outcomes of iPSCs unpredictable. Moreover, genomic instability is closely associated with cellular transformation into cancer. Numerous studies have indicated that iPSCs with genomic instability may exhibit uncontrolled proliferation after transplantation, increasing the risk of tumor formation [ 15 , 17 – 20 ]. In particular, chromosomal amplifications of oncogenes such as BCL2L1 , c-MYC , and mutation or loss of TP53 , are associated with the transformation of transplanted iPSCs into tumor cells [ 20 – 22 ]. In addition, the genetic alterations present in iPSCs are inherited by their derived cell products throughout the differentiation process, which may introduce latent disease risks associated with the clinical use of these products. To ensure the safety of hiPSC-derived products, it is crucial not only to conduct genetic variation testing on differentiated end products and intermediates but also, more importantly, to select iPSC clones with normal chromosomes and genomes at the source for downstream cell banking. Additionally, determining the maximum number of permissible passages during continuous subculturing is essential. This necessitates the establishment of accurate and sensitive detection methods that can truly reflect genetic variations. Karyotyping by G banding analysis is a standard approach for identifying chromosomal anomalies in hiPSCs, yet it is limited to detecting alterations exceeding 5 Mb in size and lacks the precision to pinpoint the exact sites of variation. Minimum mosaicism detected is 12% by routine analysis of 50 metaphase spreads [ 23 ]. Whole-genome sequencing (WGS) and Whole Exome Sequencing (WES) are capable of detecting SNVs, Indels, and CNVs. However, limited by the depth of sequencing, WGS has poor detection capabilities for low-frequency variants. Furthermore, the short read length of WGS and WES technologies also impede the accurate identification of SVs. Optical Genome Mapping (OGM) creates large-sized marked DNA fragments which can be assembled into whole genomes map quickly and efficiently [ 24 , 25 ]. This technology can detect chromosomal structural variants with higher resolution than karyotyping (≥ 500 bp), FISH and chromosomal microarray, and with higher accuracy than next generation sequencing techniques [ 26 ]. Therefore, it is an emerging technology that has shown significant promise in the diagnosis of hematological tumors and genetic disorders [ 27 – 31 ]. In this study, we conducted a comprehensive assessment of the variations at the chromosomal, sub-chromosomal, and genomic sequence levels across three distinct passages of hiPSC lines using OGM, WES, RNA sequencing (RNA-seq), and karyotypic analysis. We observed that with the increase in cell passages, all three hiPSC lines exhibited a diversity of genetic variations. Utilizing OGM, WES, RNA-seq and karyotypic analysis, we detected these genetic variations across various genomic scales, with further elucidating the expression alterations of the mutated genes, as well as stemness-related genes. The synergy of these methodologies provides an integrated approach to genetic variation profiling. This study has established a strategy for assessing the genetic stability of hiPSCs, offering technical solutions for the selection of healthy hiPSC clones and the evaluation of clone passage stability, thereby ensuring the safety of cell therapy products derived from hiPSCs in clinical applications. Methods human iPSCs culture Human iPSC lines, iPSC#1, iPSC#2, and iPSC#3 were obtained from National Stem Cell Translational Resource Center of China. These cell lines were generated by reprogramming human umbilical cord blood mononuclear cells (UCBMCs) using the CTS™ CytoTune™-iPS 2.1 reprogramming kit (Thermo Fisher Scientific Inc.). The UCBMCs were isolated from different adult healthy donors. When the three iPSC lines were cultured from P0 to P8, clones were mechanically picked using a syringe needle and grown in 12-well plates coated with Laminin (LN-521, BioLamina), and then expanded in E8 complete medium (Gibco). From passage 9 onward, the iPSC lines were continuously cultured in StemFit™ Basic03 medium (Ajinomoto Co., Inc., Japan) and passaged using ReLeSR™ (STEMCELL Technologies). The iPSCs were cryopreserved at passages P8, P18, P28, P38, and P48, using CryoStor CS10 (STEMCELL Technologies) as the cryopreservation medium. Before tests, cells were thawed in a water bath and then passaged once after they reached confluence in the culture flasks for subsequent experiments. Karyotyping Before cell harvesting, colchicine (Invitrogen) was added directly to the plate of cells, achieving a final concentration of 100 ng/ml for 40 min. Subsequently, the cells were trypsinized, treated with a hypotonic solution for 20 min, and then fixed. Metaphases were spread on microscope slides and stained using the standard G-banding technique. For chromosomal number analysis, 500 metaphases were counted. Then 50 metaphases were photographed and classified in accordance with the International System for Human Cytogenetic Nomenclature. Bionano optical genome mapping Bionano optical genome mapping was conducted following the manufacturer's instructions. In summary, ultra-high molecular weight (UHMW) genomic DNA was extracted from 1.5 × 10 6 cells using the Prep SP-G2 Blood & Cell Culture DNA Isolation Kit (Bionano Genomics). DNA quantification was performed using the Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific). A total of 750 ng of UHMW DNA was labeled using the Bionano Prep DLS-G2 Labeling Kit (Bionano Genomics). Subsequently, the labeled UHMW DNA was quantified using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). The labeled DNA, at a concentration of 4 to 15 ng/mL, was loaded onto the Saphyr Chip G2.3 (Bionano Genomics) and run on a Bionano Saphyr Gen2 instrument. A total of 2,000 GB of data were collected per sample. DNA molecules with an average mapping rate greater than 70% and a minimum length of 150 kbp were selected for subsequent analysis. The Rare Variant Analysis pipeline was applied using Bionano Solve version 3.6.1, with hg19_DLE1_0kb_0labels.cmap as the reference. Aneuploidy, CNV, and SV calling were filtered using the recommended parameters by Bionano Solve. SVs not identified in the control database samples with the same enzyme were considered true SVs. RNA sequencing and gene expression analysis Total RNA was extracted from 2 × 10 6 cells using TRIzol™ reagent (ThermoFisher Scientific). After digestion with DNase I, the RNA concentration and integrity were detected using Agilent 2100 Bioanalyzer. Then, according to the instructions of VAHTS Universal V6 RNA-seq Library Prep Kit for Illumina® (NR604-01/02), cDNA strand synthesis and sequencing library construction were carried out. Sequencing was performed using the Illumina HiSeq X Ten sequencer and HiSeq X Ten Reagent Kit v2.5, with a sequencing strategy of PE150. 150bp paired-end sequencing reads were obtained. The raw data obtained from sequencing were filtered using the Fastp software to obtain clean reads, which were then aligned to the reference sequence hg19 by Hisat2 software [ 32 ]. Gene expression quantification analysis was conducted based on the alignment results by Stringtie software [ 33 ], and differential expression analysis was performed according to the TPM values of genes in different sample groups. Heatmaps of gene expression in specific CNV regions were generated using genes with TPM values greater than 5 in cells across different passages. This was accomplished using the R package “pheatmap” with the parameter scale = “row”. The oncogenes classified as Tie1 listed in the COSMIC database (v96) [ 34 ] in these regions were selected for further heatmap analysis. Whole-exome sequencing (WES) Total DNA was extracted from 2 × 10 6 cells using the TIANamp Genomic DNA Kit (Tiangen). DNA concentration was measured using the Qubit® 3.0 Fluorometer (Thermo Fisher Scientific). A total of 200 ng of DNA was used as the template. Library preparation was performed using the VAHTS Universal Plus DNA Library Prep Kit for Illumina V2 (Vazyme). Hybridization capture was conducted using the SureSelect XT target enrichment system (Agilent SureSelect XT Reagent Kit). Library quantification was performed using the Qubit 3.0 Fluorometer, and library size was assessed using the Agilent 2100 Bioanalyzer. The library was sequenced on NovaSeq X Plus sequencing platform (Illumina) using the Paired-end 150 bp (PE150) sequencing strategy. Identification of germline variants and somatic mutations The quality control, adapter trimming, and quality filtering of WES fastq files were performed using fastp (v0.22.0). The clean reads were then aligned to the GRCh38 genome using bwa mem (v0.7.17). The resulting bam files were analyzed using bamdst (v1.0.9), followed by sorting, duplication marking, and Base Quality Score Recalibration (BQSR) using the GATK toolkit (v4.3) [ 35 ]. Germline short variants were identified using GATK’s HaplotypeCaller (v4.3). Variant Quality Score Recalibration (VQSR) was performed using GATK VariantRecalibrator, based on the following databases: hapmap_3.3.hg38, 1000G_omni2.5.hg38, 1000G_phase1.snps.high_confidence.hg38, and dbsnp_138.hg38 for single nucleotide variants (SNVs) calling; Mills_and_1000G_gold_standard.indels.hg38, dbsnp_138.hg38, and Axiom_Exome_Plus.genotypes.all_populations.poly.hg38 for indels calling. Somatic mutations were identified and filtered using GATK's Mutect2 with the parameters "min-allele-fraction = 0.02", "normal-p-value-threshold = 0.001", and "unique-alt-read-count = 3". The corresponding human PBMCs used for the generation of induced pluripotent stem cells (iPSCs) were used as the normal sample for Mutect2. The germline and somatic variants were annotated using ANNOVAR (v Date: 2020-06-08) with the databases esp6500siv2_all, ALL.sites.2015_08, EAS.sites.2015_08, and gnomad_exome [ 36 ]. Variants with read counts greater than 7 and allele frequencies less than 1% in the above databases were filtered out using the R package tidyverse (v2.0.0). Long-Range PCR and Sanger sequencing To validate the breakpoints between the translocation of chr17 with chrX, and the translocation of chr7 with chr8, long-range PCR were conducted with the primers as shown in Table 1 . Table 1 Primer sequence using in long-range PCR Translocation position Start position (hg19) Primer sequence Size of amplicon Chr17:ChrX chr17:76726676 GTCAGAAATTGAGCTTCCATGATTCCTCTGT ~ 10 kb chrX:11053007 GTTTTCAAGGTGACTGACTCGATGTAGGGA Chr7:Chr8 chr7:104258051 TCAGATATTTCCAGATGCCTGGGTCACAA ~ 13.3 kb chr8:113279925 AATAAACGCTCTGTCCTCTCTTTTGGTGG PCR amplification products of long-range PCR were conducted next-generation sequencing. Genomic breakpoints were visualized using the IGV software. Based on the IGV results, Genomic breakpoints were further validated with Sanger sequencing with the primers as shown in Table 2 . Table 2 Primer sequence using in Sanger sequencing Translocation position Start position (hg19) Primer sequence Size of amplicon Chr17:ChrX chr17:76726682 AATTGAGCTTCCATGATTCCTCTG 1885bp chrX:11043693 AGTGGTATTCACCAGCAATGTGA Chr7:Chr8 chr7:104265894 CAAGCCCAGCAGCAGTAATAAG 1493bp chr8:113275103 TGTCTCAGTTGCTTAGGACCCTC eSNP karyotyping eSNP karyotyping was performed as previously described, with minor modifications. The raw RNA-seq reads were aligned to the human reference genome (hg19) using TopHat2 (v2.1) [ 37 , 38 ]. Reads containing Ns in their CIGAR string were split to process spanning splicing events in the RNA-seq data. Subsequently, SNPs were called using GATK HaplotypeCaller (v4.3). SNPs with a minor allele frequency of less than 0.2 in the total allele depth, or with a read depth below 20, were discarded to reduce errors and noise. For visualization, the moving medians of the major-to-minor allele ratios were plotted against the moving medians of the chromosomal positions, using a window size of 100–150 SNPs. The p-value was calculated using a one-tailed t-test, comparing the major/minor values of SNPs within the window to those of the total SNP pool, with correction for multiple testing using the False Discovery Rate (FDR) method. Results Chromosome abnormalities were identified by karyotyping in three different hiPSCs lines Three human iPSC lines reprogrammed by Sendai viral were used in the study. The three iPSC lines were continually cultured in vitro for 50 passages and evaluated the pluripotent abilities and genome stability every 10 passages. All the 3 iPSC lines keep the characteristics of expression of pluripotent-related markers and the potential of differentiation ability (Fig. S1). Although the cells of passage (P) 10 displayed norm karyotype, however, several karyotyping abnormalities occurred in all the 3 iPSC lines during in vitro amplification (Fig. 1). A chromosome duplication between q13 and q31 in chromosome (chr) 5 appeared in 5.9% (3 of 51) metaphase chromosomes of iPSC#1 cells at P50. The iPSC#2 showed 3 changes of karyotype at P40 and P50, ie, chromosome addition at p22.1 of chrX in cells, translocation between q13 of chr9 and q13 of chr22, and duplication of chr17. All three abnormalities stared to appear from P40. In iPSC#3, trisomy of chromosome 12, a common aneuploidy in iPSC lines and ESC lines, was observed from P20 in 28.0% (14 of 50) metaphase chromosomes. Upon extended culture to P30, the frequency of trisomy of chromosome 12 increased to 92% (46 of 50 mitotic cells exhibited the abnormality). The data suggest that trisomy of chromosome 12 confers a significant proliferative advantage, as evidenced by a more than threefold increase in the proportion of aneuploid cells following 10 passages. At passages 40 and 50, the prevalence of trisomy of chr12 had reached 100%. Another chromosome abnormality in iPSC#3 was chr20 duplication (q11.2 - q13.3). Despite the absence of this variant in P30 cells, the incidence of the variant had risen to 75% by P40, with 39 of 52 mitotic cells exhibiting the variant. By P50, the variant was present in all mitotic cells (60 of 60), indicating a complete fixation of this variant. Detection of aneuploidy and gene copy number variations in iPSCs by OGM To delve deeper into chromosomal variations at a submicroscopic level, we employed OGM technique and the Rare Variant Analysis pipeline to examine three iPSC line samples across passages from P10 to P50. Our findings revealed that trisomy 12 in iPSC#3 was detectable starting from passage P30 (Table 3 and Fig. 2 A). While the OGM’s sensitivity for detecting aneuploidies is somewhat lower compared to karyotyping, suggesting a more conservative algorithm in OGM for identifying aneuploidies, the fractional copy number of chromosome 12, ranging from 3.14 to 3.03, suggests a high level of accuracy in aneuploidy detection. Table 3 Chromosome aneuploidy of iPSC#3 identified by OGM cells chr types fractChrLen fractCN P10 / / / / P20 / / / / P30 12 gain 0.9874603 3.135326 P40 12 gain 0.9942027 3.142477 P50 12 gain 0.9846582 3.029844 The OGM method was able to identify CNVs that were determined by karyotyping as either chromosome duplication or gain, including CNVs on chr17q of iPSC#2, and chr12p and chr20q of iPSC#3 (Fig. 2 B). Additionally, the OGM method detected CNVs that were not identified by karyotyping, such as those on chromosomes 2 and 20 of iPSC#1 from P30 to P50, with sizes of 2.2 Mb and 3.4 Mb respectively, which exceeded the detection threshold of karyotyping (Fig. 2 C). The duplication of chromosome 17 in iPSC#2, which was also detected by karyotyping, was confirmed by OGM, with the breakpoints ranging from 40.56 Mb to 74.17 Mb (Fig. 2 D). In iPSC#3, the duplication and inversion of a fragment on chromosome 20 occurred at 30.9 Mb and 48.4 Mb, and the OGM method was able to pinpoint the three breakpoints, as illustrated in Fig. 3 E. Chromosome SV analysis calling by OGM Chromosome structural variations (SVs), including insertions, deletions, and duplications, are identified by the Rare Variant Analysis pipeline of the OGM method. In iPSC#1 and iPSC#3, the number of SVs remains relatively stable across different passages, with approximately 20 to 30 SVs observed. However, in iPSC#2, the number of SVs increases with cell passage, reaching 78 SVs by passage 50 (Fig. 3 A). The average size of SVs varies among the iPSC lines. The sizes of insertions and deletions are consistent across the three iPSC lines, averaging about 690 kb. In contrast, the size of duplications varies significantly. For iPSC#1 cells, the average size of duplications ranges from 33 kb to 1.88 Mb (Fig. 3 A). An inter-chromosomes translocation involving chromosomes 17 and X was detected in iPSC#2 from individuals P40 and P50 using the OGM technique, as depicted in Fig. 3 B. This rearrangement was not completely defined by conventional karyotyping, which only indicated an additional copy of the X chromosome (add(X)) in the same iPSCs (Fig. 1). The OGM data revealed that a segment of chromosome 17, spanning from 41.4 Mb to 76.7 Mb, had moved to the short arm of chromosome X at the 11.1 Mb position, occurring both in an inverted orientation (variant allele frequency (VAF) of 0.33) and in a direct sequence (VAF of 0.06) (Fig. 3 C). To confirm the inter-chromosomal translocation, long-range PCR and Sanger sequencing were employed. The analysis confirmed the fusion of DNA from chromosomes 17 and X, with exact breakpoints at 76,727,298 bp and 11,042,448 bp, respectively, and an intervening 12 bp insertion (Fig. 3 E). Additionally, the OGM technique identified a translocation between chromosome 7 at 104.3 Mb and chromosome 8 at 113.2 Mb in iPSC#2 from individual P50, with a high confidence level of 0.96 and a VAF of 0.01 (Fig. 3 B and D). This translocation was not observed through karyotyping, possibly due to its low frequency. To verify the authenticity of this genetic variant, long-range PCR and Sanger sequencing were again utilized. The results confirmed the presence of a fusion between chromosomes 7 and 8 at the specific sites of 104,266,943 bp and 113,274,683 bp, with a 6 bp DNA insertion at the breakpoints (Fig. 3 F). Gene expression changes correlated with Aneuploidy and CNV To further validate chromosomal and submicroscopic anomalies and to assess their genetic impacts, gene expression profiles were examined through RNA-seq of the aforementioned iPSC lines. Figure 4 A illustrates that the relative expression levels of genes in cells from various passages, compared to P10, exhibited significant alterations at chromosomal regions where CNVs were present. Notably, in chromosome 12 of iPSC#3, gene expression levels were approximately 1.5 times higher in P40 and P50 than in P10, suggesting a correlation with the trisomy 12 chromosome count in iPSC#3. The heatmap of gene expression across CNV regions, depicted in Fig. 4 B, revealed a progressive increase in expression levels for all five CNVs with increased gene copy numbers as cell passages advanced. Furthermore, the list of altered genes included numerous oncogenes, as shown in Fig. 4 C, such as BCL2L1 on chromosome 20, and KRAS , KMT2D , MDM2 on chromosome 12, as well as RNF43 on chromosome 17, which are potential drivers of chromosomal variations and may be linked to cellular transformation. We also conducted eSNP Karyotyping analysis using RNA-seq data (Fig. 4 D), which reaffirmed the presence of chromosomal abnormalities, including trisomy 12 and duplication of chromosome 17. The absence of CNV detection for chromosomes 20 and 2 in the eSNP Karyotyping analysis suggests that its sensitivity is not superior to that of the OGM method. Additionally, serial RNA-seq profiling revealed nuanced transcriptional drift with passages: NANOG expression rose progressively after P30, whereas POU5F1 exhibited transient fluctuation at P40; nonetheless, overall levels of POU5F1 and other core pluripotency factors ( SOX2, LIN28A ) remained within a narrow range, indicating preservation of the undifferentiated state. Identification of SNVs and indels by WES and evaluation the risks Germline variants refer to genetic alterations carried by the donor who provided the original umbilical cord blood cells for reprogramming. These variants are inherited by all spring iPSC lines and iPSC-derived products. Specific germline mutations related to genetic diseases and cancers can increase risks after the clinical application of iPSC -derived products. Germline short variants, including SNVs and Indels, were identified using the GATK HaplotypeCaller tool based on WES data of donor cells and iPSCs with different passages. The three iPSC lines carried 312, 302, and 296 SNVs and 60, 57, and 57 Indels, respectively. Most SNVs were heterozygous (82.4%, 82.1%, and 79.1%), whereas only about half of the Indels were heterozygous (55.0%, 57.9%, and 45.6%) (Fig. 5 A). For SNVs, GC > AT and GC > TA transitions were relatively higher in all three iPSC lines, followed by AT > GC transitions, which were correlated with previous reports [ 39 ] (Fig. 5 B). Most SNVs were nonsynonymous. For Indels, nonframeshift deletions, nonframeshift insertions, and frameshift deletions were the dominant variant types (Fig. 5 C). Pathogenic variants associated with genetic disorders in the OMIM database accounted for approximately one-quarter of all identified mutations in any of the three iPSC lines. There were 16 tumor-related mutations for iPSC#1, 23 for iPSC#2, and 21 for iPSC#3. Some mutations were at high risk for developing aggressive tumors, such as CDH1 and RELN . The types of mutations annotated in the ClinVar database were similar among the three iPSC lines (Fig. 5 D). Taken together, these data indicate that germline variants occurred randomly in the three donors. Some variants might be related to tumors or diseases. More attention should be given to evaluating the risk of these mutations based on the cells derived from iPSCs and clinical conditions. Somatic mutations in iPSCs are not present in the germline and can arise during the reprogramming process or subsequent culturing of iPSCs. In this study, we identified somatic mutations in iPSCs using the GATK Mutect2 tool, comparing these cells to their parental umbilical cord blood cells, as shown in Fig. 6 A. Across all three iPSC lines, the number of somatic mutations ranged from 3 to 22. Notably, iPSC#2 exhibited a higher number of somatic mutations compared to the other lines. Specifically, the mean number of somatic mutations was 14 in iPSC#2, 6.8 in iPSC#1, and 6.8 in iPSC#3. When comparing iPSCs to their parental cells, the distribution of new somatic mutations across passages revealed distinct patterns. In iPSC#1, new somatic mutations were predominantly observed at passage 10 (11 mutations). In contrast, iPSC#2 and iPSC#3 exhibited somatic mutations at every 10 passages. Notably, in iPSC#2, more than 5 new somatic mutations occurred at each 10-passage interval (Fig. 6 B). Among these mutations, BCOR was mutated in both iPSC#1 and iPSC#2, harboring frameshift deletion, stop-gain, and short-insertion variants (Fig. 6 C). As a key regulator of pluripotency and mesodermal/ectodermal differentiation, BCOR is among the most frequently mutated genes in hiPSC lines; its disruption is linked to widespread transcriptional alterations and impaired lineage commitment [ 40 ]. Evaluation strategies of the genetic stability of iPSCs for clinical applications The above research demonstrates that genetic alterations in iPSCs can manifest at multiple levels, including chromosomal abnormalities, submicroscopic structural variations, and gene sequence variations (Table 4 ). Specifically, aberrations such as trisomy 12 and extensive CNVs affecting chromosomes 17 and 20 may confer a survival or growth advantage to these cells during in vitro culture and passaging. Consequently, these genetic changes significantly increase the tumorigenicity of differentiated cell products derived from these iPSC lines. Table 4 Summary of genomic aberrations detected in iPSC Lines Methods Identified aberrations Karyotyping (G-band analysis) Aneuploidy: Trisomy 12 SV: dup(17)(q21q25), t(9;22)(q13;q13), add(X)(p22.1), dup(20)(q11.2q13.3), dup(5)(q13q31) Optical genome mapping (OGM) Aneuploidy: Trisomy 12 SV: fus(17;17)(q21.2;q25.1), t(7;8)(q22.2;q23.3), fus(20;20)(q11.21;q13.13), dup(2)(p23.3p23.2), dup(20)(q11.21q11.22) CNV: 17q21.31q24.3(44780236_67659743)x2 ~ 3, 20q11.2120q13.13 (31460244_51029691)x2 ~ 3, 2p23.3p23.2(27058745_29202355)x3 ~ 4, 20q11.21q11.22(29651348_33301277)x2 ~ 3 Whole-exome sequencing (WES) germline mutations: such as CDH1, RELN somatic mutations: such as BCOR, SMAD1, ELP2 Bulk RNA-seq increase in expression levels: such as BCL2L1, KRAS, KMT2D, MDM2, RNF43 To meet the requirements of clinical translation, we suggested an integrated framework for assessing the genomic stability of iPSCs, systematically evaluating chromosomal, structural, coding, and transcriptomic integrity (Table 5 ). G-banded karyotyping (≥ 50 metaphase spreads) screens for whole-chromosome aneuploidy, polyploidy, and large structural variants (5–10 Mb resolution). IPSCs intended for clinical use should exhibit no recurrent chromosomal abnormalities; any sporadic alterations must remain within the allowable limits defined by the Pharmacopoeia. OGM at 300–600× effective coverage resolves sub-chromosomal variants. This method detects aneuploidies with VAF > 10%, as well as structural variants (insertions ≥ 5 kb, deletions ≥ 7 kb, duplications ≥ 150 kb, inversions ≥ 70 kb, translocations ≥ 70 kb) and hPSC-specific recurrent CNVs ≥ 500 kb. All identified variants should undergo risk assessment to determine clinical acceptability. Recurrent lesions encompassing high-risk driver oncogenes must not be present. WES at 300–500× depth interrogates coding regions at single-nucleotide resolution. Pathogenic SNVs and Indels must be systematically analyzed and risk-assessed; high-risk mutations—such as TP53 mutations—should be absent. Bulk RNA-seq (> 6 Gb clean reads per sample) quantifies the transcriptional impact of genomic alterations, bridging a critical gap in current iPSC quality-control paradigms and providing further evidence of genomic integrity and clinical suitability. Table 5 Methods for evaluating iPSC genetic stability in clinical applications Methods Recommended technical specifications Variant calling performance Recommended acceptable standards Karyotyping (G-band analysis) examining at least 50 metaphase spreads Polyploidy and aneuploidy; SVs (such as insertion, deletion, duplication, inversion, and translocation et al.), with resolution of ~ 5–10 Mb No recurrent chromosomal abnormalities were observed. All non-recurrent chromosomal abnormalities fell within the limits specified by the Pharmacopoeia. Optical genome mapping (OGM) 300–600× depth Aneuploidy > 10% VAF; Insertion between 5–50 kbp, deletion > 7 kbp, duplication > 150 kbp, and inversion > 70 kbp, translocation > 70 kbp; CNVs, especially those recurrent CNV of hPSCs, >500 kbp No recurrent genomic abnormalities encompassing high-risk driver oncogenes were detected. Tumor- or disease-associated variants underwent systematic risk analysis and assessment. Whole-exome sequencing (WES) 300–500× depth SNVs and Indels, 1 bp resolution Tumor- and disease-associated variants were systematically evaluated for pathogenic risk; no high-risk driver mutations—such as TP53 alterations—were detected. Bulk RNA-seq At least 6 Gb clean data Gene expression level FoldChange > 2 Tumor- or disease-associated gene expression underwent systematic risk analysis and assessment. Discussion HiPSCs, while holding immense potential for regenerative medicine, are increasingly recognized to acquire genetic aberrations during their derivation and expansion. Similar to embryonic stem cells, these pluripotent cells demonstrate heightened susceptibility to chromosomal instability and nucleotide-level variations. This vulnerability stems from two critical phases: (1) the epigenetic remodeling process during somatic cell reprogramming, which may compromise DNA repair mechanisms, and (2) the selective pressure exerted during prolonged in vitro culture (typically 20–30 passages) required for cell banking and differentiation protocols. Notably, transient suppression of tumor suppressor pathways – particularly p53 inactivation during reprogramming – has been implicated in permitting the survival of genetically abnormal clones [ 10 , 41 ]. Of particular concern are subpopulations harboring mutations that confer proliferative advantages, which may progressively dominate the culture through serial passaging. Such clonal dynamics not only threaten genomic integrity but also raise substantial safety concerns for therapeutic applications. Current evidence suggests that undetected genetic variations in iPSC-derived products could lead to teratoma formation or functional abnormalities in differentiated cells. These risks collectively underscore the urgent need for comprehensive genomic surveillance strategies capable of detecting variants across multiple scales – from chromosomal rearrangements to single-nucleotide alterations. As the gold standard for chromosomal analysis, karyotyping retains unique advantages in clinical-grade iPSC characterization, particularly in detecting whole-cell genomic abnormalities that elude molecular-based techniques. Crucially, it identifies polyploidization events (e.g., tetraploidy) that OGM and WES/WGS cannot discern, as these methods analyze extracted DNA without preserving cellular genomic boundaries. While demonstrating lower sensitivity than OGM for small structural variants (< 5 Mb), evidenced by its failure to detect a 2.1 Mb duplication on chr2 and a 3.6 Mb duplication on chr20 in iPSC#1, karyotyping showed 100% concordance with OGM in identifying large-scale translocations, inversions, and aneuploidies (as observed in iPSC#2 and iPSC#3). Notably, karyotyping resolved genomic alterations in repetitive regions where OGM’s label density approach falters, such as pericentromeric translocation at t(9;22)(q13;q13).These regions, comprising ~ 8% of the genome, remain challenging for current molecular mapping technologies. While the FDA guidelines recommend karyotyping of at least 20 cells in highly expanded primary cells[ 42 ], this study shows that karyotyping over 50 cells can identify more low - frequency abnormalities. In iPSC#2 cells at P40, a translocation involving chr9 and 22 was identified in 2 out of 50 metaphase cells, while a duplication of chr17 was detected in 3 out of 50 metaphase cells. Additionally, a duplication of chr5 in iPSC#1 cells was observed in 3 out of 51 metaphase cells (Fig. 1). Examination of fewer than 50 metaphase spreads, such as 20 or 30, would likely fail to detect the presence of these abnormalities. OGM overcomes critical resolution limitations of conventional cytogenetics by enabling genome-wide SV detection at 500 bp resolution, effectively bridging the gap between karyotyping (> 5 Mb) and sequencing-based methods. In our study, OGM identified 2 translocations, 5 CNVs, and SVs smaller than 4.6 Mb that escaped karyotypic detection, including a clinically significant 3.6 Mb duplication at 20q11.21 (spanning BCL2L1 and ASXL1 , Fig. 4 C) associated with apoptosis resistance in hematopoietic differentiation assays. Its high-throughput capacity (6 samples/run) and rapid turnaround time (3 days vs. 14 days for karyotyping) proved particularly advantageous for analyzing post-differentiation cells lacking metaphase populations. Additionally, OGM method has remarkable but imperfect breakpoint precision. For example, translocations between chr17 (76,727,058 bp) and chrX (11,048,769 bp) were mapped within a 6.3 kb window of long-range PCR-validated breakpoints (chr17:76,727,298; chrX:11,042,448). However, OGM has persistent limitations requiring methodological awareness. It failed to detect some balanced translocations confirmed by karyotyping especially in repeat-rich region. It cannot detect ploidy as described above. Therefore, OGM complements rather than replaces karyotyping. While structural variants dominate discussions of iPSC genomic instability, WES reveals a parallel landscape of coding sequence alterations with profound biological consequences. Our analysis identified 1084 high-confidence exonic germline variants (mean 361 ± 9.7 per line), including 167 tumor-related, monogenic disorders related or pathogenic mutations (mean 55.7 ± 3.2 per line). Crucially, these coding variants resided in genomic regions without detectable SVs by OGM or karyotyping, underscoring WES’s unique role in capturing small-scale mutagenesis events. WGS provides a nominally comprehensive approach for detecting sequence variations, CNVs, and SVs. However, its utility in hiPSC genomic surveillance remains constrained by inherent technical and interpretative limitations. Compared to WES, the lower sequencing depth of typical WGS workflows (30–50× vs. 300–500× for WES) significantly reduces sensitivity for low-frequency sequence variants (e.g., subclonal mutations with < 10% allele frequency), a critical drawback given the mosaic nature of iPSC cultures. While WGS captures abundant non-coding variants, over 98% of these alterations currently lack well-established frameworks for clinical interpretation, particularly in the context of pluripotency or differentiation risks. Furthermore, WGS underperforms in SV detection compared to OGM, and it cannot reliably detect balanced translocations or ploidy changes resolvable by karyotyping. These limitations, compounded by higher computational costs and storage demands, render WGS suboptimal as a standalone solution for hiPSC genomic stability assessment. Recurrent genetic abnormalities in iPSCs typically manifest as whole-chromosome or segmental aneuploidies. These CNVs are readily detectable in RNA-seq data as concordant increases in the transcript abundance of the affected genes. In the present study, we observed a strong positive correlation between gene-level CNV gains and their corresponding mRNA expression, indicating that RNA-seq can serve as an orthogonal validation of CNVs initially identified by OGM. Importantly, RNA-seq simultaneously reports the expression levels of recurrent oncogenes—such as BCL2L1 and KRAS —thereby providing a direct read-out of malignant transformation risk. Furthermore, comparative RNA-seq across serial passages enables longitudinal assessment of genetic stability at the transcriptional level. Expression of core pluripotency regulators ( POU5F1 , SOX2 and LIN28A ) remained relatively stable over the passages examined except NANOG (Fig. 4 E), whereas lineage-priming genes associated with the three germ layers—including NES and PAX6 (ectoderm), SOX17 and FOXA2 (endoderm), and NODAL and BMP2 (mesoderm)—exhibited consistent basal expression without aberrant up- or down-regulation (data not shown). Collectively, these data demonstrate that transcriptomic profiling is a sensitive and quantitative tool for monitoring both oncogenic potential and developmental competence in iPSC cultures intended for clinical application. The multimodal framework combining karyotyping, OGM, and WES achieves unparalleled resolution across genomic scales through complementary detection capabilities. Karyotyping provides a whole-genome cytogenetic overview to identify chromosomal aneuploidies (e.g., trisomy 12) and balanced rearrangements, while OGM bridges the resolution gap by detecting SVs below the 5 Mb karyotyping threshold (e.g. Chr20 CNV gain). WES complements these approaches by pinpointing coding-region mutations undetectable by chromosomal analyses, such as BCOR , TP53 mutations. Crucially, the technologies’ limitations are reciprocally addressed: OGM’s inability to resolve polyploidy is counterbalanced by karyotyping, while RNA-seq compensates for WES’s non-coding blindness by capturing regulatory consequences of intergenic SVs. Furthermore, cross-validation reinforces result reliability—in the 3 hiPSC lines, concordant detection of fus(17;17)(q21;q25), fus(20;20)(q11;q13) duplications by both OGM and karyotyping confirmed their technical robustness. This tiered approach achieved 50% concordance for SVs and CNVs, demonstrating that no single technology suffices for comprehensive hiPSC genomic surveillance. Conclusions Our integrative approach combining karyotyping, optical genome mapping, WES, and transcriptomic profiling establishes a robust framework for interrogating the genomic stability of iPSCs across multiple biological scales. By systematically addressing the limitations of conventional single-technology workflows, this multimodal strategy not only enhances detection sensitivity for clinically relevant variants but also pioneers functional annotation of genetic alterations through multi-omics correlation. While challenges persist in resolving epigenetic drift, mitochondrial heteroplasmy, and low frequency subclonal mosaicism, the methodological foundation laid here provides a critical roadmap for advancing iPSC quality control. These efforts collectively underscore that ensuring genomic integrity is not merely a technical prerequisite but an ethical imperative for safe and effective regenerative medicine. Abbreviations CNVs copy number variations hiPSCs human induced pluripotent stem cells Indels insertions and deletions OGM optical genome mapping RNA-seq RNA sequencing SNVs single-nucleotide variations SVs structural variants UHMW ultra-high molecular weight VAF variant allele frequency WES whole-exome sequencing WGS whole-genome sequencing Declarations Acknowledgement Funding This work was supported by the Natural Key Research and Development Program (No: 2021YFA1101601) and State Key Laboratory of Drug Regulatory Science Project (2023SKLDRS0122). Author information Authors and Affiliations Cell Collection and Research Center, National Institutes for Food and Drug Control, Beijing 102629, China Kehua Zhang, Tao Na, Chuncui Jia, Xianghe Yuan, Meichen Guo, Xu Yang, Min Li, Shufang Meng State Key Laboratory of Drug Regulatory Science, Beijing 102629, China Kehua Zhang, Tao Na, Meichen Guo, Xu Yang, Min Li, Xianghe Yuan, Shufang Meng Beijing Key Laboratory of Quality Control and Non-clinical Research and Evaluation for Cellular and Gene Therapy Medicinal Products, Beijing 102629, China Kehua Zhang, Tao Na, Meichen Guo, Xu Yang, Min Li, Xianghe Yuan, Shufang Meng Key Laboratory of the Ministry of Health for Research on Quality and Standardization of Biotech Products ， Beijing 102629, China Kehua Zhang, Tao Na, Meichen Guo, Xu Yang, Min Li, Xianghe Yuan, Shufang Meng National Stem Cell Translational Resource Center, Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China. Wenwen Jia, Zhihui Bai, Jizhen Lu, Zhongmin Liu Contributions Kehua Zhang collected and analyzed the OGM, WES, and RNA-seq data. Tao Na assisted with data interpretation and provided critical insights. Chuncui Jia and Meichen Guo performed cell expansion and banking. Xu Yang prepared ultra-high-molecular-weight DNA and executed the OGM experiments. Min Li carried out WES library preparation and bioinformatic analysis. Xianghe Yuan conducted karyotyping assessments. Wenwen Jia, Zhihui Bai, and Jizhen Lu reprogrammed somatic cells and established the iPSC lines. Zhongmin Liu offered valuable guidance throughout the project. Kehua Zhang drafted the manuscript. Shufang Meng conceived the study and oversaw all experimental work. All authors have read and approved the final manuscript. Corresponding author Correspondence to Shufang Meng. Ethics declarations Consent for publication Preparation and banking of human iPSC lines were approved by Human Cell Clinical Research Ethics Committee of Shanghai East Hospital, Tongji University. The approved project title is “Preparation and Banking of Clinical-grade Human HLA High-matched iPS Cells” (Approval number: [2018] Tilinshen No. (002). Date of approval: August 22, 2018.). Artificial intelligence (AI) The authors declare that they have not used AI-generated work in this manuscript. Conflicts of Interest The authors declare no competing interests. Additional information The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA012852 & HRA012814) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human. 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05:22:14","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132286,"visible":true,"origin":"","legend":"","description":"","filename":"830b98fe62cc4fca8d3cd75e04ecaf4b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/14fbb913c6d5dbd12fd9062d.xml"},{"id":93456572,"identity":"fca660e6-066d-4d6c-9b88-469494653715","added_by":"auto","created_at":"2025-10-14 05:22:14","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144145,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/18f7b134ee1dfabf60ab9bc7.html"},{"id":93456556,"identity":"6e9bc8b4-acac-4662-8a83-bc7859fab493","added_by":"auto","created_at":"2025-10-14 05:22:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaryotypic analysis of the three iPSC lines.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe percentage of abnormal karyotypes detected among 50 analyzed metaphase spreads is shown for iPSC#1, iPSC#2, and iPSC#3. Dup(5) means duplication in chr5. Add(X) means additional chromosomal fragment of unknown origin on the X chromosome. t(9;22) means translocation between chr9 and chr22. +12 means gain of chr12.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/49ad42124ec4009fbdc3beca.png"},{"id":93456550,"identity":"9f92f790-07fe-4f6e-a7a7-47293d7d5941","added_by":"auto","created_at":"2025-10-14 05:22:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209272,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAneuploidy and CNVs calling by OGM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Trisomy 12 identified by OGM in iPSC#3 (passages P30-P50). A representative circos plot showing chromosome 12 gain, as well as a translocation in chr20 in iPSC#3 P50. (B) Genome-wide CNV profiles of all three iPSC lines. (C) Subchromosomal duplications undetected by karyotyping: chr2:27.03-29.22 Mb (iPSC#1, P30-P50) and chr20:29.89-33.31 Mb (iPSC#3, P30-P50). (D) Chr17q duplication (40.56-74.17 Mb) in iPSC#2. Arrow indicates breakpoint position. (E) \u0026nbsp;Inverted duplication on chr20q (30.92-48.46 Mb) in iPSC#3. Arrow indicates breakpoint position.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/86d6bd99e29c7f7cec789bb9.png"},{"id":93456558,"identity":"324b729b-e201-4b07-9286-1e4873b22db9","added_by":"auto","created_at":"2025-10-14 05:22:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSV profiling in iPSCs by OGM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) SV count and average size distribution across passages in all iPSC lines. (B) \u0026nbsp;Circos plot showing interchromosomal translocations: t(17;X) (\u003cem\u003ered arrow\u003c/em\u003e) and t(7;8) (\u003cem\u003eblue arrow\u003c/em\u003e) in iPSC#2. (C) OGM molecules view of t(17;X) breakpoints: Fusion 1 (chr17:76.7 Mb to chrX 11.1 Mb, VAF = 0.33) and fusion 2 (chr17:41.4 Mb to chrX 11.1 Mb, VAF = 0.06). (D) OGM molecules view of t(7;8) shows the breakpoint and fusion is chr7:104.26 Mb to chr8:113.28 Mb. (E) Breakpoint validation of primary t(17;X) fusion (chr17:76,727,298 to chrX:11,042,448) by long-range PCR and Sanger sequencing. (F) \u0026nbsp;t(7;8) breakpoint confirmation (chr7:104,266,943 to chr8:113,274,683) with orthogonal methods.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/fb7b1d7fcf70a464374d1231.png"},{"id":93457064,"identity":"18a293b4-0577-42f4-a8be-846bcd67e6a9","added_by":"auto","created_at":"2025-10-14 05:30:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":283207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptional consequences of genomic alterations in iPSCs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) \u0026nbsp;Chromosome-wide relative gene expression profiles in CNV regions. Dot plots show moving averages (vs. P10) across chromosomal positions. Red lines: LOESS regression curves; gray bars: CNV regions identified by OGM. (B) Heatmap of gene expression within CNV-containing chromosomal segments. (C) Oncogene expression heatmap within CNV regions. (D) Chromosomal aberrations were analysis using eSNP karyotyping methods. Top panel shows CNVs on chr12 in iPSC#3cell. Bottom panel shows CNVs on chr17 in iPSC#2 cells. \u003cem\u003eColored bars\u003c/em\u003e: -log₁₀(FDR-corrected p-values); \u003cem\u003eblack line\u003c/em\u003e: significance threshold (p\u0026lt;0.01). (E) Gene expression stability of stemness-related genes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/e78e6a2f91df78e3a604feef.png"},{"id":93457061,"identity":"2870afbd-be3b-4ac7-8424-e26eac8b2e0c","added_by":"auto","created_at":"2025-10-14 05:30:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGermline variant profiling in iPSCs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Germline variant burden: Counts of SNVs and Indels detected by WES/GATK HaplotypeCaller (v4.3) across iPSC lines. (B) SNV mutational spectrum: Proportion of transition/transversion substitutions. (C) Functional classification of coding variants. (D) Clinical annotation of pathogenic variants: OMIM (Mendelian genetic disorder), COSMIC (oncogenic) and ClinVar (pathogenic/likely pathogenic) classifications.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/99a253cc9864d903b8d37f59.png"},{"id":93457065,"identity":"59ef72cc-9b7c-477b-9a5b-4a9b7cc67ef3","added_by":"auto","created_at":"2025-10-14 05:30:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":168300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSomatic mutations analysis by WES.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u003cstrong\u003e \u003c/strong\u003eCircos plot showing somatic mutations identified by GATK Mutect2 in iPSC with different passages. From inner to outer, circles represent P10, P20, P30, P40 and P50. Every red line represents a mutation. The height of red line represents VAF of the mutation. (B) Classification of somatic mutations. inherited: mutations inherited from the parental cells. new: new mutations compared with their parental cells. (C) pathogenic somatic mutations in 3 hiPSC lines across P10 to P50. fs: frameshift.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/36673cd6e4ed8d91e1fa391b.png"},{"id":105755674,"identity":"878f1ce4-fe52-46b5-ba99-d71388a9180c","added_by":"auto","created_at":"2026-03-30 16:29:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2297257,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/836184c9-d38e-4230-a288-e89723068800.pdf"},{"id":93456553,"identity":"0568d4c0-e774-44b3-8939-2a7f241723a8","added_by":"auto","created_at":"2025-10-14 05:22:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":902511,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7522863/v1/781de9e0e5f60d0beb582b13.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive Assessment of the Genomic Stability of Human Induced Pluripotent Stem Cells for Clinical Applications","fulltext":[{"header":"Background","content":"\u003cp\u003eInduced pluripotent stem cells (iPSCs) are a population of cells with pluripotency, obtained by reprogramming adult cells, and capable of differentiating into various cell types. Since Shinya Yamanaka and his colleagues first reported in 2006 that fibroblasts could be induced into pluripotent stem cells by introducing four transcription factors (OCT4, SOX2, KLF4, and c-MYC), iPSCs have been widely used in disease modeling, drug screening, and regenerative medicine [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the field of cell therapy drug research, products derived from human iPSCs (hiPSCs), such as retinal pigment epithelial cells, neural precursor cells, islet cells, and natural killer cells, have entered the clinical trial phase and show promising prospects for application [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, hiPSCs are susceptible to genomic instability throughout the reprogramming and extended culture processes, which poses a potential risk to the clinical application of cell-based therapies products derived from them [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Such genomic variations might originate from the cells\u0026rsquo; initial state, or they could emerge during the reprogramming of somatic cells into iPSCs, a process that involves significant epigenetic and transcriptional reconfigurations, thereby increasing the likelihood of genomic instability [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. During subsequent amplification and the establishment of cell banks, hiPSCs are also vulnerable to culture-induced genomic alterations, particularly some recurrent genomic abnormalities, which lead to selective growth advantage of mutant cells in culture conditions, potentially due to the altered expression or function of one or more mutated genes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Genomic instability encompasses a range of alterations, including single-nucleotide variations (SNVs) and small insertions and deletions (Indels), as well as more extensive rearrangements such as copy number variations (CNVs) and structural variations (SVs), which involve the gain or loss of entire chromosomes or segments, leading to chromosomal aberrations like deletions, duplications, amplifications, inversions, and translocations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenomic instability not only affects the differentiation capacity of iPSCs but may also trigger safety issues such as tumorigenesis. Genomic instability may lead to a decrease or deviation in the differentiation capacity of iPSCs. For instance, the loss or amplification of certain chromosomal segments can affect the expression of genes related to differentiation via interactions with epigenetic variations, resulting in a reduced potential for iPSCs to differentiate into specific cell types [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Chromosomal abnormalities may also lead to increased heterogeneity, making the differentiation outcomes of iPSCs unpredictable. Moreover, genomic instability is closely associated with cellular transformation into cancer. Numerous studies have indicated that iPSCs with genomic instability may exhibit uncontrolled proliferation after transplantation, increasing the risk of tumor formation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In particular, chromosomal amplifications of oncogenes such as \u003cem\u003eBCL2L1\u003c/em\u003e, \u003cem\u003ec-MYC\u003c/em\u003e, and mutation or loss of \u003cem\u003eTP53\u003c/em\u003e, are associated with the transformation of transplanted iPSCs into tumor cells [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, the genetic alterations present in iPSCs are inherited by their derived cell products throughout the differentiation process, which may introduce latent disease risks associated with the clinical use of these products.\u003c/p\u003e\u003cp\u003eTo ensure the safety of hiPSC-derived products, it is crucial not only to conduct genetic variation testing on differentiated end products and intermediates but also, more importantly, to select iPSC clones with normal chromosomes and genomes at the source for downstream cell banking. Additionally, determining the maximum number of permissible passages during continuous subculturing is essential. This necessitates the establishment of accurate and sensitive detection methods that can truly reflect genetic variations. Karyotyping by G banding analysis is a standard approach for identifying chromosomal anomalies in hiPSCs, yet it is limited to detecting alterations exceeding 5 Mb in size and lacks the precision to pinpoint the exact sites of variation. Minimum mosaicism detected is 12% by routine analysis of 50 metaphase spreads [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Whole-genome sequencing (WGS) and Whole Exome Sequencing (WES) are capable of detecting SNVs, Indels, and CNVs. However, limited by the depth of sequencing, WGS has poor detection capabilities for low-frequency variants. Furthermore, the short read length of WGS and WES technologies also impede the accurate identification of SVs. Optical Genome Mapping (OGM) creates large-sized marked DNA fragments which can be assembled into whole genomes map quickly and efficiently [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This technology can detect chromosomal structural variants with higher resolution than karyotyping (\u0026ge;\u0026thinsp;500 bp), FISH and chromosomal microarray, and with higher accuracy than next generation sequencing techniques [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, it is an emerging technology that has shown significant promise in the diagnosis of hematological tumors and genetic disorders [\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we conducted a comprehensive assessment of the variations at the chromosomal, sub-chromosomal, and genomic sequence levels across three distinct passages of hiPSC lines using OGM, WES, RNA sequencing (RNA-seq), and karyotypic analysis. We observed that with the increase in cell passages, all three hiPSC lines exhibited a diversity of genetic variations. Utilizing OGM, WES, RNA-seq and karyotypic analysis, we detected these genetic variations across various genomic scales, with further elucidating the expression alterations of the mutated genes, as well as stemness-related genes. The synergy of these methodologies provides an integrated approach to genetic variation profiling. This study has established a strategy for assessing the genetic stability of hiPSCs, offering technical solutions for the selection of healthy hiPSC clones and the evaluation of clone passage stability, thereby ensuring the safety of cell therapy products derived from hiPSCs in clinical applications.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ehuman iPSCs culture\u003c/h2\u003e\u003cp\u003eHuman iPSC lines, iPSC#1, iPSC#2, and iPSC#3 were obtained from National Stem Cell Translational Resource Center of China. These cell lines were generated by reprogramming human umbilical cord blood mononuclear cells (UCBMCs) using the CTS\u0026trade; CytoTune\u0026trade;-iPS 2.1 reprogramming kit (Thermo Fisher Scientific Inc.). The UCBMCs were isolated from different adult healthy donors. When the three iPSC lines were cultured from P0 to P8, clones were mechanically picked using a syringe needle and grown in 12-well plates coated with Laminin (LN-521, BioLamina), and then expanded in E8 complete medium (Gibco). From passage 9 onward, the iPSC lines were continuously cultured in StemFit\u0026trade; Basic03 medium (Ajinomoto Co., Inc., Japan) and passaged using ReLeSR\u0026trade; (STEMCELL Technologies). The iPSCs were cryopreserved at passages P8, P18, P28, P38, and P48, using CryoStor CS10 (STEMCELL Technologies) as the cryopreservation medium. Before tests, cells were thawed in a water bath and then passaged once after they reached confluence in the culture flasks for subsequent experiments.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eKaryotyping\u003c/h3\u003e\n\u003cp\u003eBefore cell harvesting, colchicine (Invitrogen) was added directly to the plate of cells, achieving a final concentration of 100 ng/ml for 40 min. Subsequently, the cells were trypsinized, treated with a hypotonic solution for 20 min, and then fixed. Metaphases were spread on microscope slides and stained using the standard G-banding technique. For chromosomal number analysis, 500 metaphases were counted. Then 50 metaphases were photographed and classified in accordance with the International System for Human Cytogenetic Nomenclature.\u003c/p\u003e\n\u003ch3\u003eBionano optical genome mapping\u003c/h3\u003e\n\u003cp\u003eBionano optical genome mapping was conducted following the manufacturer's instructions. In summary, ultra-high molecular weight (UHMW) genomic DNA was extracted from 1.5 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e cells using the Prep SP-G2 Blood \u0026amp; Cell Culture DNA Isolation Kit (Bionano Genomics). DNA quantification was performed using the Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific). A total of 750 ng of UHMW DNA was labeled using the Bionano Prep DLS-G2 Labeling Kit (Bionano Genomics). Subsequently, the labeled UHMW DNA was quantified using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). The labeled DNA, at a concentration of 4 to 15 ng/mL, was loaded onto the Saphyr Chip G2.3 (Bionano Genomics) and run on a Bionano Saphyr Gen2 instrument. A total of 2,000 GB of data were collected per sample. DNA molecules with an average mapping rate greater than 70% and a minimum length of 150 kbp were selected for subsequent analysis. The Rare Variant Analysis pipeline was applied using Bionano Solve version 3.6.1, with hg19_DLE1_0kb_0labels.cmap as the reference. Aneuploidy, CNV, and SV calling were filtered using the recommended parameters by Bionano Solve. SVs not identified in the control database samples with the same enzyme were considered true SVs.\u003c/p\u003e\n\u003ch3\u003eRNA sequencing and gene expression analysis\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from 2 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e cells using TRIzol\u0026trade; reagent (ThermoFisher Scientific). After digestion with DNase I, the RNA concentration and integrity were detected using Agilent 2100 Bioanalyzer. Then, according to the instructions of VAHTS Universal V6 RNA-seq Library Prep Kit for Illumina\u0026reg; (NR604-01/02), cDNA strand synthesis and sequencing library construction were carried out. Sequencing was performed using the Illumina HiSeq X Ten sequencer and HiSeq X Ten Reagent Kit v2.5, with a sequencing strategy of PE150. 150bp paired-end sequencing reads were obtained. The raw data obtained from sequencing were filtered using the Fastp software to obtain clean reads, which were then aligned to the reference sequence hg19 by Hisat2 software [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Gene expression quantification analysis was conducted based on the alignment results by Stringtie software [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and differential expression analysis was performed according to the TPM values of genes in different sample groups.\u003c/p\u003e\u003cp\u003eHeatmaps of gene expression in specific CNV regions were generated using genes with TPM values greater than 5 in cells across different passages. This was accomplished using the R package \u0026ldquo;pheatmap\u0026rdquo; with the parameter scale = \u0026ldquo;row\u0026rdquo;. The oncogenes classified as Tie1 listed in the COSMIC database (v96) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] in these regions were selected for further heatmap analysis.\u003c/p\u003e\n\u003ch3\u003eWhole-exome sequencing (WES)\u003c/h3\u003e\n\u003cp\u003eTotal DNA was extracted from 2 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e cells using the TIANamp Genomic DNA Kit (Tiangen). DNA concentration was measured using the Qubit\u0026reg; 3.0 Fluorometer (Thermo Fisher Scientific). A total of 200 ng of DNA was used as the template. Library preparation was performed using the VAHTS Universal Plus DNA Library Prep Kit for Illumina V2 (Vazyme). Hybridization capture was conducted using the SureSelect XT target enrichment system (Agilent SureSelect XT Reagent Kit). Library quantification was performed using the Qubit 3.0 Fluorometer, and library size was assessed using the Agilent 2100 Bioanalyzer. The library was sequenced on NovaSeq X Plus sequencing platform (Illumina) using the Paired-end 150 bp (PE150) sequencing strategy.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of germline variants and somatic mutations\u003c/h2\u003e\u003cp\u003eThe quality control, adapter trimming, and quality filtering of WES fastq files were performed using fastp (v0.22.0). The clean reads were then aligned to the GRCh38 genome using bwa mem (v0.7.17). The resulting bam files were analyzed using bamdst (v1.0.9), followed by sorting, duplication marking, and Base Quality Score Recalibration (BQSR) using the GATK toolkit (v4.3) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Germline short variants were identified using GATK\u0026rsquo;s HaplotypeCaller (v4.3). Variant Quality Score Recalibration (VQSR) was performed using GATK VariantRecalibrator, based on the following databases: hapmap_3.3.hg38, 1000G_omni2.5.hg38, 1000G_phase1.snps.high_confidence.hg38, and dbsnp_138.hg38 for single nucleotide variants (SNVs) calling; Mills_and_1000G_gold_standard.indels.hg38, dbsnp_138.hg38, and Axiom_Exome_Plus.genotypes.all_populations.poly.hg38 for indels calling. Somatic mutations were identified and filtered using GATK's Mutect2 with the parameters \"min-allele-fraction\u0026thinsp;=\u0026thinsp;0.02\", \"normal-p-value-threshold\u0026thinsp;=\u0026thinsp;0.001\", and \"unique-alt-read-count\u0026thinsp;=\u0026thinsp;3\". The corresponding human PBMCs used for the generation of induced pluripotent stem cells (iPSCs) were used as the normal sample for Mutect2. The germline and somatic variants were annotated using ANNOVAR (v Date: 2020-06-08) with the databases esp6500siv2_all, ALL.sites.2015_08, EAS.sites.2015_08, and gnomad_exome [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Variants with read counts greater than 7 and allele frequencies less than 1% in the above databases were filtered out using the R package tidyverse (v2.0.0).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLong-Range PCR and Sanger sequencing\u003c/h3\u003e\n\u003cp\u003eTo validate the breakpoints between the translocation of chr17 with chrX, and the translocation of chr7 with chr8, long-range PCR were conducted with the primers as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimer sequence using in long-range PCR\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTranslocation position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStart position (hg19)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimer sequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSize of amplicon\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChr17:ChrX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echr17:76726676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGTCAGAAATTGAGCTTCCATGATTCCTCTGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e~\u0026thinsp;10 kb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echrX:11053007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGTTTTCAAGGTGACTGACTCGATGTAGGGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChr7:Chr8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echr7:104258051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTCAGATATTTCCAGATGCCTGGGTCACAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e~\u0026thinsp;13.3 kb\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echr8:113279925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAATAAACGCTCTGTCCTCTCTTTTGGTGG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePCR amplification products of long-range PCR were conducted next-generation sequencing. Genomic breakpoints were visualized using the IGV software. Based on the IGV results, Genomic breakpoints were further validated with Sanger sequencing with the primers as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimer sequence using in Sanger sequencing\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTranslocation position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStart position (hg19)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrimer sequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSize of amplicon\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChr17:ChrX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echr17:76726682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAATTGAGCTTCCATGATTCCTCTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1885bp\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echrX:11043693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGTGGTATTCACCAGCAATGTGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChr7:Chr8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echr7:104265894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCAAGCCCAGCAGCAGTAATAAG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1493bp\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003echr8:113275103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTGTCTCAGTTGCTTAGGACCCTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eeSNP karyotyping\u003c/h3\u003e\n\u003cp\u003eeSNP karyotyping was performed as previously described, with minor modifications. The raw RNA-seq reads were aligned to the human reference genome (hg19) using TopHat2 (v2.1) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Reads containing Ns in their CIGAR string were split to process spanning splicing events in the RNA-seq data. Subsequently, SNPs were called using GATK HaplotypeCaller (v4.3). SNPs with a minor allele frequency of less than 0.2 in the total allele depth, or with a read depth below 20, were discarded to reduce errors and noise. For visualization, the moving medians of the major-to-minor allele ratios were plotted against the moving medians of the chromosomal positions, using a window size of 100\u0026ndash;150 SNPs. The p-value was calculated using a one-tailed t-test, comparing the major/minor values of SNPs within the window to those of the total SNP pool, with correction for multiple testing using the False Discovery Rate (FDR) method.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eChromosome abnormalities were identified by karyotyping in three different hiPSCs lines\u003c/h2\u003e\u003cp\u003eThree human iPSC lines reprogrammed by Sendai viral were used in the study. The three iPSC lines were continually cultured \u003cem\u003ein vitro\u003c/em\u003e for 50 passages and evaluated the pluripotent abilities and genome stability every 10 passages. All the 3 iPSC lines keep the characteristics of expression of pluripotent-related markers and the potential of differentiation ability (Fig. S1). Although the cells of passage (P) 10 displayed norm karyotype, however, several karyotyping abnormalities occurred in all the 3 iPSC lines during \u003cem\u003ein vitro\u003c/em\u003e amplification (Fig.\u0026nbsp;1). A chromosome duplication between q13 and q31 in chromosome (chr) 5 appeared in 5.9% (3 of 51) metaphase chromosomes of iPSC#1 cells at P50. The iPSC#2 showed 3 changes of karyotype at P40 and P50, ie, chromosome addition at p22.1 of chrX in cells, translocation between q13 of chr9 and q13 of chr22, and duplication of chr17. All three abnormalities stared to appear from P40. In iPSC#3, trisomy of chromosome 12, a common aneuploidy in iPSC lines and ESC lines, was observed from P20 in 28.0% (14 of 50) metaphase chromosomes. Upon extended culture to P30, the frequency of trisomy of chromosome 12 increased to 92% (46 of 50 mitotic cells exhibited the abnormality). The data suggest that trisomy of chromosome 12 confers a significant proliferative advantage, as evidenced by a more than threefold increase in the proportion of aneuploid cells following 10 passages. At passages 40 and 50, the prevalence of trisomy of chr12 had reached 100%. Another chromosome abnormality in iPSC#3 was chr20 duplication (q11.2 - q13.3). Despite the absence of this variant in P30 cells, the incidence of the variant had risen to 75% by P40, with 39 of 52 mitotic cells exhibiting the variant. By P50, the variant was present in all mitotic cells (60 of 60), indicating a complete fixation of this variant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDetection of aneuploidy and gene copy number variations in iPSCs by OGM\u003c/h2\u003e\u003cp\u003eTo delve deeper into chromosomal variations at a submicroscopic level, we employed OGM technique and the Rare Variant Analysis pipeline to examine three iPSC line samples across passages from P10 to P50. Our findings revealed that trisomy 12 in iPSC#3 was detectable starting from passage P30 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). While the OGM\u0026rsquo;s sensitivity for detecting aneuploidies is somewhat lower compared to karyotyping, suggesting a more conservative algorithm in OGM for identifying aneuploidies, the fractional copy number of chromosome 12, ranging from 3.14 to 3.03, suggests a high level of accuracy in aneuploidy detection.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChromosome aneuploidy of iPSC#3 identified by OGM\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecells\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003echr\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003etypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003efractChrLen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003efractCN\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003egain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9874603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.135326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003egain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9942027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.142477\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003egain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9846582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.029844\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe OGM method was able to identify CNVs that were determined by karyotyping as either chromosome duplication or gain, including CNVs on chr17q of iPSC#2, and chr12p and chr20q of iPSC#3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Additionally, the OGM method detected CNVs that were not identified by karyotyping, such as those on chromosomes 2 and 20 of iPSC#1 from P30 to P50, with sizes of 2.2 Mb and 3.4 Mb respectively, which exceeded the detection threshold of karyotyping (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The duplication of chromosome 17 in iPSC#2, which was also detected by karyotyping, was confirmed by OGM, with the breakpoints ranging from 40.56 Mb to 74.17 Mb (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). In iPSC#3, the duplication and inversion of a fragment on chromosome 20 occurred at 30.9 Mb and 48.4 Mb, and the OGM method was able to pinpoint the three breakpoints, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eChromosome SV analysis calling by OGM\u003c/h2\u003e\u003cp\u003eChromosome structural variations (SVs), including insertions, deletions, and duplications, are identified by the Rare Variant Analysis pipeline of the OGM method. In iPSC#1 and iPSC#3, the number of SVs remains relatively stable across different passages, with approximately 20 to 30 SVs observed. However, in iPSC#2, the number of SVs increases with cell passage, reaching 78 SVs by passage 50 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The average size of SVs varies among the iPSC lines. The sizes of insertions and deletions are consistent across the three iPSC lines, averaging about 690 kb. In contrast, the size of duplications varies significantly. For iPSC#1 cells, the average size of duplications ranges from 33 kb to 1.88 Mb (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eAn inter-chromosomes translocation involving chromosomes 17 and X was detected in iPSC#2 from individuals P40 and P50 using the OGM technique, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. This rearrangement was not completely defined by conventional karyotyping, which only indicated an additional copy of the X chromosome (add(X)) in the same iPSCs (Fig.\u0026nbsp;1). The OGM data revealed that a segment of chromosome 17, spanning from 41.4 Mb to 76.7 Mb, had moved to the short arm of chromosome X at the 11.1 Mb position, occurring both in an inverted orientation (variant allele frequency (VAF) of 0.33) and in a direct sequence (VAF of 0.06) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). To confirm the inter-chromosomal translocation, long-range PCR and Sanger sequencing were employed. The analysis confirmed the fusion of DNA from chromosomes 17 and X, with exact breakpoints at 76,727,298 bp and 11,042,448 bp, respectively, and an intervening 12 bp insertion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eAdditionally, the OGM technique identified a translocation between chromosome 7 at 104.3 Mb and chromosome 8 at 113.2 Mb in iPSC#2 from individual P50, with a high confidence level of 0.96 and a VAF of 0.01 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and D). This translocation was not observed through karyotyping, possibly due to its low frequency. To verify the authenticity of this genetic variant, long-range PCR and Sanger sequencing were again utilized. The results confirmed the presence of a fusion between chromosomes 7 and 8 at the specific sites of 104,266,943 bp and 113,274,683 bp, with a 6 bp DNA insertion at the breakpoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eGene expression changes correlated with Aneuploidy and CNV\u003c/h2\u003e\u003cp\u003eTo further validate chromosomal and submicroscopic anomalies and to assess their genetic impacts, gene expression profiles were examined through RNA-seq of the aforementioned iPSC lines. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA illustrates that the relative expression levels of genes in cells from various passages, compared to P10, exhibited significant alterations at chromosomal regions where CNVs were present. Notably, in chromosome 12 of iPSC#3, gene expression levels were approximately 1.5 times higher in P40 and P50 than in P10, suggesting a correlation with the trisomy 12 chromosome count in iPSC#3. The heatmap of gene expression across CNV regions, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, revealed a progressive increase in expression levels for all five CNVs with increased gene copy numbers as cell passages advanced. Furthermore, the list of altered genes included numerous oncogenes, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, such as \u003cem\u003eBCL2L1\u003c/em\u003e on chromosome 20, and \u003cem\u003eKRAS\u003c/em\u003e, \u003cem\u003eKMT2D\u003c/em\u003e, \u003cem\u003eMDM2\u003c/em\u003e on chromosome 12, as well as \u003cem\u003eRNF43\u003c/em\u003e on chromosome 17, which are potential drivers of chromosomal variations and may be linked to cellular transformation. We also conducted eSNP Karyotyping analysis using RNA-seq data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), which reaffirmed the presence of chromosomal abnormalities, including trisomy 12 and duplication of chromosome 17. The absence of CNV detection for chromosomes 20 and 2 in the eSNP Karyotyping analysis suggests that its sensitivity is not superior to that of the OGM method. Additionally, serial RNA-seq profiling revealed nuanced transcriptional drift with passages: \u003cem\u003eNANOG\u003c/em\u003e expression rose progressively after P30, whereas \u003cem\u003ePOU5F1\u003c/em\u003e exhibited transient fluctuation at P40; nonetheless, overall levels of \u003cem\u003ePOU5F1\u003c/em\u003e and other core pluripotency factors (\u003cem\u003eSOX2, LIN28A\u003c/em\u003e) remained within a narrow range, indicating preservation of the undifferentiated state.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of SNVs and indels by WES and evaluation the risks\u003c/h2\u003e\u003cp\u003eGermline variants refer to genetic alterations carried by the donor who provided the original umbilical cord blood cells for reprogramming. These variants are inherited by all spring iPSC lines and iPSC-derived products. Specific germline mutations related to genetic diseases and cancers can increase risks after the clinical application of iPSC -derived products. Germline short variants, including SNVs and Indels, were identified using the GATK HaplotypeCaller tool based on WES data of donor cells and iPSCs with different passages. The three iPSC lines carried 312, 302, and 296 SNVs and 60, 57, and 57 Indels, respectively. Most SNVs were heterozygous (82.4%, 82.1%, and 79.1%), whereas only about half of the Indels were heterozygous (55.0%, 57.9%, and 45.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). For SNVs, GC\u0026thinsp;\u0026gt;\u0026thinsp;AT and GC\u0026thinsp;\u0026gt;\u0026thinsp;TA transitions were relatively higher in all three iPSC lines, followed by AT\u0026thinsp;\u0026gt;\u0026thinsp;GC transitions, which were correlated with previous reports [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Most SNVs were nonsynonymous. For Indels, nonframeshift deletions, nonframeshift insertions, and frameshift deletions were the dominant variant types (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Pathogenic variants associated with genetic disorders in the OMIM database accounted for approximately one-quarter of all identified mutations in any of the three iPSC lines. There were 16 tumor-related mutations for iPSC#1, 23 for iPSC#2, and 21 for iPSC#3. Some mutations were at high risk for developing aggressive tumors, such as \u003cem\u003eCDH1\u003c/em\u003e and \u003cem\u003eRELN\u003c/em\u003e. The types of mutations annotated in the ClinVar database were similar among the three iPSC lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Taken together, these data indicate that germline variants occurred randomly in the three donors. Some variants might be related to tumors or diseases. More attention should be given to evaluating the risk of these mutations based on the cells derived from iPSCs and clinical conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSomatic mutations in iPSCs are not present in the germline and can arise during the reprogramming process or subsequent culturing of iPSCs. In this study, we identified somatic mutations in iPSCs using the GATK Mutect2 tool, comparing these cells to their parental umbilical cord blood cells, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA. Across all three iPSC lines, the number of somatic mutations ranged from 3 to 22. Notably, iPSC#2 exhibited a higher number of somatic mutations compared to the other lines. Specifically, the mean number of somatic mutations was 14 in iPSC#2, 6.8 in iPSC#1, and 6.8 in iPSC#3. When comparing iPSCs to their parental cells, the distribution of new somatic mutations across passages revealed distinct patterns. In iPSC#1, new somatic mutations were predominantly observed at passage 10 (11 mutations). In contrast, iPSC#2 and iPSC#3 exhibited somatic mutations at every 10 passages. Notably, in iPSC#2, more than 5 new somatic mutations occurred at each 10-passage interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Among these mutations, \u003cem\u003eBCOR\u003c/em\u003e was mutated in both iPSC#1 and iPSC#2, harboring frameshift deletion, stop-gain, and short-insertion variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). As a key regulator of pluripotency and mesodermal/ectodermal differentiation, \u003cem\u003eBCOR\u003c/em\u003e is among the most frequently mutated genes in hiPSC lines; its disruption is linked to widespread transcriptional alterations and impaired lineage commitment [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eEvaluation strategies of the genetic stability of iPSCs for clinical applications\u003c/h2\u003e\u003cp\u003eThe above research demonstrates that genetic alterations in iPSCs can manifest at multiple levels, including chromosomal abnormalities, submicroscopic structural variations, and gene sequence variations (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, aberrations such as trisomy 12 and extensive CNVs affecting chromosomes 17 and 20 may confer a survival or growth advantage to these cells during \u003cem\u003ein vitro\u003c/em\u003e culture and passaging. Consequently, these genetic changes significantly increase the tumorigenicity of differentiated cell products derived from these iPSC lines.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of genomic aberrations detected in iPSC Lines\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethods\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIdentified aberrations\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKaryotyping\u003c/p\u003e\u003cp\u003e(G-band analysis)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAneuploidy: Trisomy 12\u003c/p\u003e\u003cp\u003eSV: dup(17)(q21q25), t(9;22)(q13;q13), add(X)(p22.1), dup(20)(q11.2q13.3), dup(5)(q13q31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptical genome mapping\u003c/p\u003e\u003cp\u003e(OGM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAneuploidy: Trisomy 12\u003c/p\u003e\u003cp\u003eSV: fus(17;17)(q21.2;q25.1), t(7;8)(q22.2;q23.3), fus(20;20)(q11.21;q13.13), dup(2)(p23.3p23.2), dup(20)(q11.21q11.22)\u003c/p\u003e\u003cp\u003eCNV: 17q21.31q24.3(44780236_67659743)x2\u0026thinsp;~\u0026thinsp;3, 20q11.2120q13.13 (31460244_51029691)x2\u0026thinsp;~\u0026thinsp;3, 2p23.3p23.2(27058745_29202355)x3\u0026thinsp;~\u0026thinsp;4, 20q11.21q11.22(29651348_33301277)x2\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhole-exome sequencing\u003c/p\u003e\u003cp\u003e(WES)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003egermline mutations: such as \u003cem\u003eCDH1, RELN\u003c/em\u003e\u003c/p\u003e\u003cp\u003esomatic mutations: such as \u003cem\u003eBCOR, SMAD1, ELP2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBulk RNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eincrease in expression levels: such as \u003cem\u003eBCL2L1, KRAS, KMT2D, MDM2, RNF43\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo meet the requirements of clinical translation, we suggested an integrated framework for assessing the genomic stability of iPSCs, systematically evaluating chromosomal, structural, coding, and transcriptomic integrity (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). G-banded karyotyping (\u0026ge;\u0026thinsp;50 metaphase spreads) screens for whole-chromosome aneuploidy, polyploidy, and large structural variants (5\u0026ndash;10 Mb resolution). IPSCs intended for clinical use should exhibit no recurrent chromosomal abnormalities; any sporadic alterations must remain within the allowable limits defined by the Pharmacopoeia. OGM at 300\u0026ndash;600\u0026times; effective coverage resolves sub-chromosomal variants. This method detects aneuploidies with VAF\u0026thinsp;\u0026gt;\u0026thinsp;10%, as well as structural variants (insertions\u0026thinsp;\u0026ge;\u0026thinsp;5 kb, deletions\u0026thinsp;\u0026ge;\u0026thinsp;7 kb, duplications\u0026thinsp;\u0026ge;\u0026thinsp;150 kb, inversions\u0026thinsp;\u0026ge;\u0026thinsp;70 kb, translocations\u0026thinsp;\u0026ge;\u0026thinsp;70 kb) and hPSC-specific recurrent CNVs\u0026thinsp;\u0026ge;\u0026thinsp;500 kb. All identified variants should undergo risk assessment to determine clinical acceptability. Recurrent lesions encompassing high-risk driver oncogenes must not be present. WES at 300\u0026ndash;500\u0026times; depth interrogates coding regions at single-nucleotide resolution. Pathogenic SNVs and Indels must be systematically analyzed and risk-assessed; high-risk mutations\u0026mdash;such as \u003cem\u003eTP53\u003c/em\u003e mutations\u0026mdash;should be absent. Bulk RNA-seq (\u0026gt;\u0026thinsp;6 Gb clean reads per sample) quantifies the transcriptional impact of genomic alterations, bridging a critical gap in current iPSC quality-control paradigms and providing further evidence of genomic integrity and clinical suitability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMethods for evaluating iPSC genetic stability in clinical applications\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethods\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecommended technical specifications\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVariant calling performance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRecommended acceptable standards\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKaryotyping\u003c/p\u003e\u003cp\u003e(G-band analysis)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eexamining at least 50 metaphase spreads\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePolyploidy and aneuploidy;\u003c/p\u003e\u003cp\u003eSVs (such as insertion, deletion, duplication, inversion, and translocation et al.), with resolution of ~\u0026thinsp;5\u0026ndash;10 Mb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo recurrent chromosomal abnormalities were observed.\u003c/p\u003e\u003cp\u003eAll non-recurrent chromosomal abnormalities fell within the limits specified by the Pharmacopoeia.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptical genome mapping\u003c/p\u003e\u003cp\u003e(OGM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u0026ndash;600\u0026times; depth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAneuploidy\u0026thinsp;\u0026gt;\u0026thinsp;10% VAF;\u003c/p\u003e\u003cp\u003eInsertion between 5\u0026ndash;50 kbp, deletion\u0026thinsp;\u0026gt;\u0026thinsp;7 kbp, duplication\u0026thinsp;\u0026gt;\u0026thinsp;150 kbp, and inversion\u0026thinsp;\u0026gt;\u0026thinsp;70 kbp,\u003c/p\u003e\u003cp\u003etranslocation\u0026thinsp;\u0026gt;\u0026thinsp;70 kbp;\u003c/p\u003e\u003cp\u003eCNVs, especially those recurrent CNV of hPSCs, \u0026gt;500 kbp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo recurrent genomic abnormalities encompassing high-risk driver oncogenes were detected.\u003c/p\u003e\u003cp\u003eTumor- or disease-associated variants underwent systematic risk analysis and assessment.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhole-exome sequencing\u003c/p\u003e\u003cp\u003e(WES)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300\u0026ndash;500\u0026times; depth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSNVs and Indels, 1 bp resolution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTumor- and disease-associated variants were systematically evaluated for pathogenic risk; no high-risk driver mutations\u0026mdash;such as TP53 alterations\u0026mdash;were detected.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBulk RNA-seq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAt least 6 Gb clean data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGene expression level FoldChange\u0026thinsp;\u0026gt;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTumor- or disease-associated gene expression underwent systematic risk analysis and assessment.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHiPSCs, while holding immense potential for regenerative medicine, are increasingly recognized to acquire genetic aberrations during their derivation and expansion. Similar to embryonic stem cells, these pluripotent cells demonstrate heightened susceptibility to chromosomal instability and nucleotide-level variations. This vulnerability stems from two critical phases: (1) the epigenetic remodeling process during somatic cell reprogramming, which may compromise DNA repair mechanisms, and (2) the selective pressure exerted during prolonged in vitro culture (typically 20\u0026ndash;30 passages) required for cell banking and differentiation protocols. Notably, transient suppression of tumor suppressor pathways \u0026ndash; particularly p53 inactivation during reprogramming \u0026ndash; has been implicated in permitting the survival of genetically abnormal clones [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOf particular concern are subpopulations harboring mutations that confer proliferative advantages, which may progressively dominate the culture through serial passaging. Such clonal dynamics not only threaten genomic integrity but also raise substantial safety concerns for therapeutic applications. Current evidence suggests that undetected genetic variations in iPSC-derived products could lead to teratoma formation or functional abnormalities in differentiated cells. These risks collectively underscore the urgent need for comprehensive genomic surveillance strategies capable of detecting variants across multiple scales \u0026ndash; from chromosomal rearrangements to single-nucleotide alterations.\u003c/p\u003e\u003cp\u003eAs the gold standard for chromosomal analysis, karyotyping retains unique advantages in clinical-grade iPSC characterization, particularly in detecting whole-cell genomic abnormalities that elude molecular-based techniques. Crucially, it identifies polyploidization events (e.g., tetraploidy) that OGM and WES/WGS cannot discern, as these methods analyze extracted DNA without preserving cellular genomic boundaries. While demonstrating lower sensitivity than OGM for small structural variants (\u0026lt;\u0026thinsp;5 Mb), evidenced by its failure to detect a 2.1 Mb duplication on chr2 and a 3.6 Mb duplication on chr20 in iPSC#1, karyotyping showed 100% concordance with OGM in identifying large-scale translocations, inversions, and aneuploidies (as observed in iPSC#2 and iPSC#3). Notably, karyotyping resolved genomic alterations in repetitive regions where OGM\u0026rsquo;s label density approach falters, such as pericentromeric translocation at t(9;22)(q13;q13).These regions, comprising\u0026thinsp;~\u0026thinsp;8% of the genome, remain challenging for current molecular mapping technologies.\u003c/p\u003e\u003cp\u003eWhile the FDA guidelines recommend karyotyping of at least 20 cells in highly expanded primary cells[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], this study shows that karyotyping over 50 cells can identify more low - frequency abnormalities. In iPSC#2 cells at P40, a translocation involving chr9 and 22 was identified in 2 out of 50 metaphase cells, while a duplication of chr17 was detected in 3 out of 50 metaphase cells. Additionally, a duplication of chr5 in iPSC#1 cells was observed in 3 out of 51 metaphase cells (Fig.\u0026nbsp;1). Examination of fewer than 50 metaphase spreads, such as 20 or 30, would likely fail to detect the presence of these abnormalities.\u003c/p\u003e\u003cp\u003eOGM overcomes critical resolution limitations of conventional cytogenetics by enabling genome-wide SV detection at 500 bp resolution, effectively bridging the gap between karyotyping (\u0026gt;\u0026thinsp;5 Mb) and sequencing-based methods. In our study, OGM identified 2 translocations, 5 CNVs, and SVs smaller than 4.6 Mb that escaped karyotypic detection, including a clinically significant 3.6 Mb duplication at 20q11.21 (spanning \u003cem\u003eBCL2L1\u003c/em\u003e and \u003cem\u003eASXL1\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) associated with apoptosis resistance in hematopoietic differentiation assays. Its high-throughput capacity (6 samples/run) and rapid turnaround time (3 days vs. 14 days for karyotyping) proved particularly advantageous for analyzing post-differentiation cells lacking metaphase populations. Additionally, OGM method has remarkable but imperfect breakpoint precision. For example, translocations between chr17 (76,727,058 bp) and chrX (11,048,769 bp) were mapped within a 6.3 kb window of long-range PCR-validated breakpoints (chr17:76,727,298; chrX:11,042,448).\u003c/p\u003e\u003cp\u003eHowever, OGM has persistent limitations requiring methodological awareness. It failed to detect some balanced translocations confirmed by karyotyping especially in repeat-rich region. It cannot detect ploidy as described above. Therefore, OGM complements rather than replaces karyotyping.\u003c/p\u003e\u003cp\u003eWhile structural variants dominate discussions of iPSC genomic instability, WES reveals a parallel landscape of coding sequence alterations with profound biological consequences. Our analysis identified 1084 high-confidence exonic germline variants (mean 361\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7 per line), including 167 tumor-related, monogenic disorders related or pathogenic mutations (mean 55.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 per line). Crucially, these coding variants resided in genomic regions without detectable SVs by OGM or karyotyping, underscoring WES\u0026rsquo;s unique role in capturing small-scale mutagenesis events.\u003c/p\u003e\u003cp\u003eWGS provides a nominally comprehensive approach for detecting sequence variations, CNVs, and SVs. However, its utility in hiPSC genomic surveillance remains constrained by inherent technical and interpretative limitations. Compared to WES, the lower sequencing depth of typical WGS workflows (30\u0026ndash;50\u0026times; vs. 300\u0026ndash;500\u0026times; for WES) significantly reduces sensitivity for low-frequency sequence variants (e.g., subclonal mutations with \u0026lt;\u0026thinsp;10% allele frequency), a critical drawback given the mosaic nature of iPSC cultures. While WGS captures abundant non-coding variants, over 98% of these alterations currently lack well-established frameworks for clinical interpretation, particularly in the context of pluripotency or differentiation risks. Furthermore, WGS underperforms in SV detection compared to OGM, and it cannot reliably detect balanced translocations or ploidy changes resolvable by karyotyping. These limitations, compounded by higher computational costs and storage demands, render WGS suboptimal as a standalone solution for hiPSC genomic stability assessment.\u003c/p\u003e\u003cp\u003eRecurrent genetic abnormalities in iPSCs typically manifest as whole-chromosome or segmental aneuploidies. These CNVs are readily detectable in RNA-seq data as concordant increases in the transcript abundance of the affected genes. In the present study, we observed a strong positive correlation between gene-level CNV gains and their corresponding mRNA expression, indicating that RNA-seq can serve as an orthogonal validation of CNVs initially identified by OGM. Importantly, RNA-seq simultaneously reports the expression levels of recurrent oncogenes\u0026mdash;such as \u003cem\u003eBCL2L1\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e\u0026mdash;thereby providing a direct read-out of malignant transformation risk.\u003c/p\u003e\u003cp\u003eFurthermore, comparative RNA-seq across serial passages enables longitudinal assessment of genetic stability at the transcriptional level. Expression of core pluripotency regulators (\u003cem\u003ePOU5F1\u003c/em\u003e, \u003cem\u003eSOX2\u003c/em\u003e and \u003cem\u003eLIN28A\u003c/em\u003e) remained relatively stable over the passages examined except \u003cem\u003eNANOG\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), whereas lineage-priming genes associated with the three germ layers\u0026mdash;including \u003cem\u003eNES\u003c/em\u003e and \u003cem\u003ePAX6\u003c/em\u003e (ectoderm), \u003cem\u003eSOX17\u003c/em\u003e and \u003cem\u003eFOXA2\u003c/em\u003e (endoderm), and \u003cem\u003eNODAL\u003c/em\u003e and \u003cem\u003eBMP2\u003c/em\u003e (mesoderm)\u0026mdash;exhibited consistent basal expression without aberrant up- or down-regulation (data not shown). Collectively, these data demonstrate that transcriptomic profiling is a sensitive and quantitative tool for monitoring both oncogenic potential and developmental competence in iPSC cultures intended for clinical application.\u003c/p\u003e\u003cp\u003eThe multimodal framework combining karyotyping, OGM, and WES achieves unparalleled resolution across genomic scales through complementary detection capabilities. Karyotyping provides a whole-genome cytogenetic overview to identify chromosomal aneuploidies (e.g., trisomy 12) and balanced rearrangements, while OGM bridges the resolution gap by detecting SVs below the 5 Mb karyotyping threshold (e.g. Chr20 CNV gain). WES complements these approaches by pinpointing coding-region mutations undetectable by chromosomal analyses, such as \u003cem\u003eBCOR\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e mutations. Crucially, the technologies\u0026rsquo; limitations are reciprocally addressed: OGM\u0026rsquo;s inability to resolve polyploidy is counterbalanced by karyotyping, while RNA-seq compensates for WES\u0026rsquo;s non-coding blindness by capturing regulatory consequences of intergenic SVs. Furthermore, cross-validation reinforces result reliability\u0026mdash;in the 3 hiPSC lines, concordant detection of fus(17;17)(q21;q25), fus(20;20)(q11;q13) duplications by both OGM and karyotyping confirmed their technical robustness. This tiered approach achieved 50% concordance for SVs and CNVs, demonstrating that no single technology suffices for comprehensive hiPSC genomic surveillance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur integrative approach combining karyotyping, optical genome mapping, WES, and transcriptomic profiling establishes a robust framework for interrogating the genomic stability of iPSCs across multiple biological scales. By systematically addressing the limitations of conventional single-technology workflows, this multimodal strategy not only enhances detection sensitivity for clinically relevant variants but also pioneers functional annotation of genetic alterations through multi-omics correlation. While challenges persist in resolving epigenetic drift, mitochondrial heteroplasmy, and low frequency subclonal mosaicism, the methodological foundation laid here provides a critical roadmap for advancing iPSC quality control. These efforts collectively underscore that ensuring genomic integrity is not merely a technical prerequisite but an ethical imperative for safe and effective regenerative medicine.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCNVs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecopy number variations\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ehiPSCs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehuman induced pluripotent stem cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIndels\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einsertions and deletions\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eOGM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoptical genome mapping\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRNA-seq\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRNA sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSNVs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esingle-nucleotide variations\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSVs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003estructural variants\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eUHMW\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eultra-high molecular weight\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eVAF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003evariant allele frequency\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eWES\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ewhole-exome sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eWGS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ewhole-genome sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Key Research and Development Program (No: 2021YFA1101601) and State Key Laboratory of Drug Regulatory Science Project (2023SKLDRS0122).\u003c/p\u003e\n\u003cp\u003eAuthor information\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Collection and Research Center, National Institutes for Food and Drug Control, Beijing 102629, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKehua Zhang, Tao Na, Chuncui Jia, Xianghe Yuan, Meichen Guo, Xu Yang, Min Li, Shufang Meng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eState Key Laboratory of Drug Regulatory Science, Beijing 102629, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKehua Zhang, Tao Na, Meichen Guo, Xu Yang, Min Li, Xianghe Yuan, Shufang Meng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBeijing Key Laboratory of Quality Control and Non-clinical Research and Evaluation for Cellular and Gene Therapy Medicinal Products, Beijing 102629, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKehua Zhang, Tao Na, Meichen Guo, Xu Yang, Min Li, Xianghe Yuan, Shufang Meng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Laboratory of the Ministry of Health for Research on Quality and Standardization of Biotech Products\u003c/strong\u003e\u003cstrong\u003e， Beijing 102629, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKehua Zhang, Tao Na, Meichen Guo, Xu Yang, Min Li, Xianghe Yuan, Shufang Meng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNational Stem Cell Translational Resource Center, Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWenwen Jia, Zhihui Bai, Jizhen Lu, Zhongmin Liu\u003c/p\u003e\n\u003cp\u003eContributions\u003c/p\u003e\n\u003cp\u003eKehua Zhang collected and analyzed the OGM, WES, and RNA-seq data. Tao Na assisted with data interpretation and provided critical insights. Chuncui Jia and Meichen Guo performed cell expansion and banking. Xu Yang prepared ultra-high-molecular-weight DNA and executed the OGM experiments. Min Li carried out WES library preparation and bioinformatic analysis. Xianghe Yuan conducted karyotyping assessments. Wenwen Jia, Zhihui Bai, and Jizhen Lu reprogrammed somatic cells and established the iPSC lines. Zhongmin Liu offered valuable guidance throughout the project. Kehua Zhang drafted the manuscript. Shufang Meng conceived the study and oversaw all experimental work. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Shufang Meng.\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003ePreparation and banking of human iPSC lines were approved by Human Cell Clinical Research Ethics Committee of Shanghai East Hospital, Tongji University. The approved project title is \u0026ldquo;Preparation and Banking of Clinical-grade Human HLA High-matched iPS Cells\u0026rdquo; (Approval number: [2018] Tilinshen No. (002). Date of approval: August 22, 2018.).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eArtificial intelligence (AI)\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have not used AI-generated work in this manuscript.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAdditional information\u003c/p\u003e\n\u003cp\u003eThe raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics \u0026amp; Bioinformatics 2021) in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA012852 \u0026amp; HRA012814) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eTakahashi K, Yamanaka S. 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Somatic mutation landscapes at single-molecule resolution. Nature. 2021;593(7859):405-10. Epub 2021/04/30.\u003c/li\u003e\n \u003cli\u003eRouhani FJ, Zou X, Danecek P, Badja C, Amarante TD, Koh G, et al. Substantial somatic genomic variation and selection for BCOR mutations in human induced pluripotent stem cells. Nat Genet. 2022;54(9):1406-16. Epub 2022/08/12.\u003c/li\u003e\n \u003cli\u003eQuaid K, Xing X, Chen YH, Miao Y, Neilson A, Selvamani V, et al. iPSCs and iPSC-derived cells as a model of human genetic and epigenetic variation. Nat Commun. 2025;16(1):1750. Epub 2025/02/19.\u003c/li\u003e\n \u003cli\u003eAdministration FaD. Safety Testing of Human Allogeneic Cells Expanded for Use in Cell-Based Medical Products. 2024.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"stem-cell-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scrt","sideBox":"Learn more about [Stem Cell Research \u0026 Therapy](http://stemcellres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/scrt/default.aspx","title":"Stem Cell Research \u0026 Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Human induced pluripotent stem cells, genome stability, quality control, optical genome mapping, cell therapy","lastPublishedDoi":"10.21203/rs.3.rs-7522863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7522863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHuman induced pluripotent stem cells (hiPSCs) acquire genomic instability during reprogramming and culture, which poses significant risks for clinical applications. Current detection methods, such as karyotyping analysis, often fail to identify critical submicroscopic variations. This highlights an urgent need for comprehensive genomic surveillance strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThree human iPSC lines were continually cultured \u003cem\u003ein vitro\u003c/em\u003e for 50 passages, with genome stability evaluated every 10 passages. The evaluation methods included karyotyping to detect chromosomal abnormalities, optical genome mapping (OGM) to identify copy number variations (CNVs) and structural variants (SVs), whole-exome sequencing (WES) to detect coding mutations, and RNA sequencing (RNA-seq) to detect the changes of gene expression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe detected accumulating chromosomal abnormalities (e.g., trisomy 12), SVs, CNVs, and sequence mutations in three hiPSC lines during extended culture. OGM effectively identified SVs and CNVs below karyotyping resolution, particularly recurrent genome abnormalities such as gains on chr17q, chr12p and chr20q. WES revealed coding mutations, including germline short variants and newly acquired somatic mutations, some of which were associated with tumors or diseases, such as \u003cem\u003eCDH1\u003c/em\u003e, \u003cem\u003eBCOR\u003c/em\u003e. Transcriptional changes correlated with genomic alterations, including dysregulation of oncogenes such as \u003cem\u003eBCL2L1\u003c/em\u003e, \u003cem\u003eKRAS\u003c/em\u003e and \u003cem\u003eMDM2\u003c/em\u003e. Results demonstrate that each method had unique detection capabilities and limitations, and only integrative approaches can comprehensively identify genomic abnormalities.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study established a comprehensive strategy for evaluating the genetic stability of hiPSCs by integrating karyotyping, OGM, WES, and RNA-seq.\u0026nbsp;This comprehensive strategy can be applied to scenarios such as hiPSC clone screening, establishment of cell bank passages, and quality control of hiPSC-derived products. It provides a reliable genetic stability evaluation protocol to support the safe clinical application of hiPSC-related products.\u003c/p\u003e","manuscriptTitle":"Comprehensive Assessment of the Genomic Stability of Human Induced Pluripotent Stem Cells for Clinical Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 05:22:09","doi":"10.21203/rs.3.rs-7522863/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-22T16:40:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T15:10:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278538012537510008731805504802904369020","date":"2025-09-30T20:37:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T18:01:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T09:34:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T11:16:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Stem Cell Research \u0026 Therapy","date":"2025-09-16T01:46:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"stem-cell-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scrt","sideBox":"Learn more about [Stem Cell Research \u0026 Therapy](http://stemcellres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/scrt/default.aspx","title":"Stem Cell Research \u0026 Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e0b8479-2c6b-4a71-9f45-7d6224240c65","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:24:46+00:00","versionOfRecord":{"articleIdentity":"rs-7522863","link":"https://doi.org/10.1186/s13287-026-04975-w","journal":{"identity":"stem-cell-research-and-therapy","isVorOnly":false,"title":"Stem Cell Research \u0026 Therapy"},"publishedOn":"2026-03-25 16:13:16","publishedOnDateReadable":"March 25th, 2026"},"versionCreatedAt":"2025-10-14 05:22:09","video":"","vorDoi":"10.1186/s13287-026-04975-w","vorDoiUrl":"https://doi.org/10.1186/s13287-026-04975-w","workflowStages":[]},"version":"v1","identity":"rs-7522863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7522863","identity":"rs-7522863","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}