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. 2026 Feb 23;31:101080. doi: 10.1016/j.reth.2026.101080

Genomic analysis of dental pulp mesenchymal stromal cells for therapeutic applications

Kunio Hirai a,b,1, Yuko Nitahara-Kasahara a,⁎⁎,1, Yuko Okamoto a, Noriko Sangu-Miyamoto c, Yuko Aizen a, Atsushi Watanabe d, Takashi Okada a,⁎
PMCID: PMC12950372  PMID: 41777871

Abstract

Introduction

Changes in cell characteristics and the accumulation of gene mutations during expansion culture present significant challenges for the clinical translation of stem cell transplantation technologies. Reduced cell quality may lead to diminished therapeutic efficacy and unexpected cellular dysfunction after transplantation. Mesenchymal stromal cells (MSCs), including dental pulp stromal cells (DPSCs), are promising candidates for clinical application because of their multipotent differentiation capacity, immunosuppressive properties, and proliferative potential. However, robust strategies to assess genomic stability during large-scale cell preparation remain insufficient.

Methods

DPSCs derived from healthy donors maintained safety profiles and exhibited delayed senescence even after more than 10 passages under large-scale expansion conditions. To evaluate genomic stability, DPSCs derived from healthy individuals and patients with type 1 diabetes mellitus were analyzed after successive passages. Copy number variations (CNVs) were assessed using CytoScan, and disease-related gene mutations were analyzed using the TruSight One Sequencing Panel.

Results

During long-term expansion, increases in chromosomal CNVs and de novo gene mutations were observed at earlier stages in cells showing reduced proliferative capacity, depending on donor characteristics. We identified mutation accumulation patterns and genes that were particularly susceptible to substitutions or insertions/deletions during expansion, indicating inter-donor variability in genomic alterations in DPSCs.

Conclusion

This work addresses a critical bottleneck in stem cell therapy by integrating genomic risk profiling into early-stage cell quality assessment, enabling safer and more reliable cell product development. Furthermore, our findings highlight the feasibility of applying medical genomic tools to preemptively identify putative high-risk cell populations during stem cell manufacturing, thereby providing a practical framework for improving the safety and consistency of cell-based transplantation therapies.

Keywords: Multipotent mesenchymal stromal cells, Dental pulp stromal cells, Cellular senescence, de novo mutation, Exome sequence

1. Introduction

Multipotent mesenchymal stromal cells (MSCs) have been extensively studied because of their self-renewal capacity and their ability to differentiate into multiple mesodermal lineages, including osteoblasts, chondrocytes, adipocytes, and myocytes [[1], [2], [3], [4]]. MSCs were originally identified in bone marrow but have since been isolated from a variety of tissues, such as adipose tissue, umbilical cord, amnion, and dental pulp, all of which share common surface marker expression and immunomodulatory properties [2,[5], [6], [7], [8], [9], [10], [11]].

Dental pulp stem cells (DPSCs), first identified by Gronthos et al., in 2000 [12], represent a unique population of MSCs derived from dental pulp tissue. DPSCs exhibit high proliferative capacity, multipotent differentiation potential, and immunosuppressive activity comparable to or exceeding that of bone marrow–derived MSCs (BM-MSCs) [6,[13], [14], [15], [16]]. Importantly, DPSCs can be obtained through minimally invasive procedures from discarded dental tissues, making them an attractive and ethically favorable cell source.

Owing to their immunoregulatory properties, MSCs have been clinically applied to inflammatory conditions, including acute graft-versus-host disease. DPSCs similarly modulate immune cell development and suppress T-cell responses through paracrine signaling, suggesting their potential utility in treating autoimmune and inflammatory diseases such as rheumatoid arthritis and Crohn's disease. In the dental field, DPSCs and stromal cells from human exfoliated deciduous teeth (SHEDs) have already been translated into clinical applications for pulp regeneration and reparative dentin formation.

Despite these promising properties, the clinical translation of MSC-based therapies remains challenged by donor-dependent variability, age-related decline in regenerative capacity, and difficulties in maintaining consistent cell quality during long-term in vitro expansion. Prolonged culture of MSCs has been associated with reduced proliferative capacity, loss of stemness, impaired immunomodulatory function, and replicative senescence, raising concerns regarding therapeutic efficacy and safety [4,[17], [18], [19], [20], [21]]. In particular, genomic instability and the accumulation of latent genetic mutations during ex vivo expansion represent critical but insufficiently characterized risks for cell-based therapies. Current criteria for defining MSC quality lack robust predictors of long-term safety and efficacy. Considering the long-term effects of transplantation, detecting the effects of minute latent genetic mutations in cells at an early stage is required. We focused on detecting genetic mutations that are directly related to the disease with higher sensitivity than standard protocols. To this end, we comprehensively analyzed the mutation accumulation patterns in disease-related genes using next-generation sequencing (NGS).

In addition, we also considered the influence of the donor's disease background on cell function. Chronic hyperglycemia in type 1 diabetes mellitus (T1DM) induces oxidative stress and metabolic memory in stem cells [22]. Although diabetic MSCs show functional decline, the effects of T1DM on the long-term genomic stability of DPSCs during in vitro expansion remain unclear. This question is particularly relevant because large-scale expansion is required for therapeutic applications. Therefore, in this study, we aimed to comprehensively evaluate chromosomal stability and mutation accumulation in DPSCs during long-term expansion by comparing cells derived from healthy donors and those with disease backgrounds, T1DM. Using NGS–based disease-related exome panels, we sought to detect low-frequency mutations and copy number variations (CNV) with enabling sensitive detection of low-frequency variants. This approach provides a framework for assessing cellular stability and safety, contributing to the standardization and quality control of DPSC-based therapeutic products.

2. Materials and methods

2.1. Cell culture

DPSCs derived from three healthy donors (#1–3) and one patient with type 1 diabetes mellitus (T1DM, #1) were cultured as independent cell lines in Dulbecco's modified Eagle's medium (Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific), 100 U/mL penicillin, and 100 μg/mL streptomycin (Sigma-Aldrich, St. Louis, MO, USA) at 37 °C in an atmosphere with 5% CO2. The cultured cells were harvested and frozen in liquid nitrogen until further use. DPSCs between passages 3, 10, 15, and 20 were used in all experiments.

2.2. Senescence

Senescence-associated β-galactosidase (β-gal) assay was performed to assess DPSC senescence [23]. DPSCs were plated at a density of 5000 cells/cm2 in 12-well plates (n = 3) and grown to 80% confluency. The cells (passages 13, 14, 17, and 18 from a healthy donor) were fixed with 0.5% glutaraldehyde and washed thoroughly with PBS. Plates were stained with β-gal solution (OZ Biosciences, CA, USA) for 24 h at 37 °C. Cells were visualized under an IX71 microscope (Olympus, Tokyo, Japan). The number of β-gal-positive cells (30–324 positive cells) was quantified using brightfield and fluorescence imaging (5–12 fields of view per sample).

2.3. Flow cytometry

Cell surface markers of DPSCs (passages 10 and 20 from a healthy donor) were analyzed using specific fluorescence-conjugated antibodies against CD73-CFS (R&D Systems, Minneapolis, MN, USA) and CD90-APC (R&D Systems). DPSCs were pretreated with FcR Blocking Reagent (Miltenyi Biotec, Bergisch Gladbach, Germany) following the manufacturer's instructions to prevent nonspecific antibody binding. Furthermore, 7-amino-actinomycin D (Immunostep, Salamanca, Spain) was used to exclude dead cells. For intracellular staining, DPSCs were fixed and permeabilized with 4% paraformaldehyde containing 0.1% polyoxyethylene (10) octylphenyl ether (FUJIFILM Wako Pure Chemical Corp., Osaka, Japan). Samples were analyzed using Guava easyCyte (Luminex, Austin, TX, USA).

2.4. Chromosomal microarray testing

Genomic DNA (gDNA) was extracted from cultured DPSCs from three healthy donors (#1–3, passages 5, 10, 15, and 20) and one patient with T1DM (#1, passages 10 and 20) using a genomic DNA miniprep kit (Qiagen, CA, USA). After the quality of extracted gDNA was confirmed by array analysis, gDNA was analyzed with the Affymetrix Copy Number Variation (CNV) microarray analysis (Santa Clara, CA, USA), CytoScan® 750 K Suite (Thermo Fisher Scientific) according to the manufacturer's instructions. The Chip includes 750 K target markers. The resulting files were analyzed using the Chromosome Analysis Suite Affymetrix® software. CNVs > 4.2 Mb were primarily analyzed because they were more reliably detected by the platform; most CNVs that were 200 kb in length were familial and benign, to ensure robust detection and reduce false positives with our platform, consistent with prior reports that stated CNVs at or above approximately 200 kb.

2.5. Targeted exome sequencing using the MiSeq next-generation sequencer

Targeted sequencing was performed using the TruSight One panel and MiSeq Sequencer (Illumina, San Diego, CA, USA). The Illumina TruSight One panel comprises 125,396 probes to capture 11,946,514-bp targeted exon regions and 4813 genes with known associated clinical phenotypes. The sequence library was constructed using 50 ng of genomic DNA, and 151-bp paired-end reads were sequenced using a MiSeq next-generation sequencer (Illumina). Optimization and validation experiments were manually performed following the manufacturer's instructions. The experiments were performed using gDNA from one healthy donor (passages 10, clone #1–2; passages 20, clone #1–3) and one patient with T1DM (passages 10 and 20, clone #1–2, respectively) by loading three samples (3-plex) per MiSeq run per flow cell. After quantitation using Qubit (Thermo Fisher Scientific, Waltham, MA, USA), gDNA was subjected to Nextera tagmentation, which converts the input gDNA into adapter-tagged libraries. Sequence output was processed using the MiSeq Reporter pipeline (Illumina, San Diego, USA). Variant calling and annotation were performed using Illumina Variant Studio (v3.0, Illumina, San Diego, CA, USA). VCF files generated from sequencing data were imported and filtered based on Pass filter, read depth (>20), and de novo mutation. Functional annotation was performed using dbSNP, ClinVar, and COSMIC databases, and variants were classified according to ACMG/AMP guidelines.

2.6. Statistical analysis

Genetic data were compared using one-way analysis of variance (ANOVA; senescence analysis in Fig. 1C, and de novo mutation analysis in Fig. 4B) or two-way ANOVA (de novo mutation analysis in Fig. 3, Fig. 4A) followed by Tukey's multiple comparisons test. Data normality was assessed using the Shapiro–Wilk test (P > 0.05 indicated no significant deviation). Homogeneity of variance was evaluated based on model assumptions and visual inspection of residuals. When normality assumptions were not met, nonparametric analyses were additionally performed where appropriate. Statistical significance was set at P values < 0.05.

Fig. 1.

Fig. 1

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Morphology of cultured dental pulp stromal cells (DPSCs).

(A) Bright field images of cultured DPSCs obtained from a healthy donor (3, 10, and 20 passages) and a patient with type 1 diabetes mellitus (T1DM) at passage 10 ( × 20); Scale bars, 50 μm. (B) Senescence-associated β-galactosidase (β-gal) assay using cultured healthy DPSCs (13, 14, 17, and 18 passages). Scale bars, 200 or 50 μm. (C) Quantified number of β-gal positive cells (%) was calculated from the images. one-way ANOVA.

Fig. 4.

Fig. 4

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Common mutation patterns in all cultured DPSCs.

(A) Frequency of single nucleotide substitutions, T > G, T > C, T > A, C > T, C > G, and C > A, on each chromosome among the common 3704 mutations. Statistical differences compared with T > G (∗∗∗∗p < 0.0001), T > C (####p < 0.0001), T > A (‡‡‡‡p < 0.0001), C > T (††††p < 0.0001), two-way ANOVA (Tukey's post hoc test). (B) Mutation abundance and pattern, T > G, T > C, T > A, C > T, C > G, and C > A in each DPSC of healthy individuals (three clones) and patients with T1DM (two clones) at passage 20 from 10. Statistical differences compared with Normal P20-1 (∗p < 0.05), and Normal P20-2 (#p < 0.05), one-way ANOVA (Tukey's post hoc test). (C) Deletion and insertion mutation (1–3 bp, > 3bp) number in each DPSCs of healthy (three clones) and patients with T1DM (two clones) from passages 10 to 20. N.D., not detected.

Fig. 3.

Fig. 3

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Distribution map of de novo mutation numbers in cultured DPSCs.

The analysis data from the TruSight One panel using the Variant Studio software are graphically presented. The total number of mutations on each chromosome was counted in cultured DPSCs from healthy subjects (white bars) and patients with T1DM (gray bars) at passages 10 (A) and 20 (B). Common de novo mutations in all clones are indicated by black bars on the right side of the graph (A). Statistical differences compared with Normal clone 1 (∗p < 0.05 and ∗∗∗∗p < 0.0001), Normal clone 2 (####p < 0.0001), and Normal clone 3 (††P < 0.01), one-way ANOVA (Tukey's post hoc test).

3. Results

3.1. Cell morphology and senescence during long-term expansion

In the experimental culture (passage 3), no remarkable morphological changes in DPSCs derived from healthy donors were visually detected. At passages 10 and 20, small cell populations with flat and elongated morphologies were observed (Fig. 1A). Cell growth was slightly slowed after the sixth passage, showing a gradual delay in proliferation (Supplementary Fig. 1). β-gal staining was performed on the cultured DPSCs to confirm the population of senescent cells. β-gal positive cells were observed at 40 % after passage 10 of healthy DPSCs and increased gradually with each passage until passage 18 (Fig. 1B and C). The expression of cell surface antigens CD73 and CD90, which are common markers of MSCs, did not change remarkably in passages 10–20 (CD73, 81.3–76.5% at passage 10–20; CD90, 81.4–99.1% at passage 10–20), suggesting that marked morphological changes in cultured DPSCs until passage 20 were not detectable, whereas internal senescence of these cells progressed slowly, supported by lower growth speed and increased β-gal staining.

In DPSCs derived from a patient with T1DM, a flat and elongated morphology was observed at passages 10 and 20, exhibiting the characteristic morphology of senescent cells (Fig. 1A). These results suggest that the cells are more susceptible to passage than those from healthy individuals.

3.2. CNVs in the in vitro culture

Next, we focused on the internal changes, particularly genetic mutations, during cell expansion. We assessed the accumulation of de novo mutations during culture and the possibility of triggering cellular senescence.

Genomic DNA isolated from cultured DPSCs accumulated CNVs involving whole chromosomes. We detected an increased copy number of chromosomes after continuous passaging (Fig. 2). No outstanding CNVs were detected in any healthy donor-derived DPSCs at passages 5, 10, and 15 (Fig. 2A). The major CNVs in healthy #1 cells were remarkably increased in copy number, particularly concentrated on chromosome 10 at passage 20. CNVs in DPSCs from healthy #2 did not show significant changes in passages 10 and 20. In healthy #3, partial signal changes were confirmed on chromosome 9 at passage 20. In contrast, DPSCs derived from patients with T1DM showed localized CNVs scattered across the whole chromosomes at passage 20 (Fig. 2B). The Karyo view, presenting a diagram of all CNVs, demonstrates remarkable CNVs on chromosome 10 in healthy #1 but not in healthy #2 or the patient (Fig. 2C).

Fig. 2.

Fig. 2

Fig. 2

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Genomic copy number variants (CNVs) detected by the microarray approach.

The results of the microarray using the CytoScan 750 K Array were visualized in Whole Gene View using the Agilent Genomic Workbench (Agilent Technologies). Two individual samples derived from DPSCs were compared: low passage (5–10) and high passage (15–20) for each donor. (A) Healthy clones #1, 2, and 3 and (B) patients with T1DM clone #1. Vertical axes indicate the signal log2 ratio for the microarray and z-scores for XHMM (bottom). The log ratio represents the total signal intensity, indicating the total copy number on a logarithmic scale. The bottom panel shows a detailed view of the CNV region on chromosome 10 of the healthy clone #1 DPSCs. The horizontal axes indicate the physical positions of chromosome 10. The arrows indicate the loss and gain of CNVs. (C) In the Karyo view, a diagram of all CNVs (red bar for loss; blue bar for gain) in the whole genome of the cells tested is shown.

3.3. De novo mutations during DPSC expansion

Genetic mutations may be closely associated with reduced proliferative capacity, cellular characteristics, and senescence during expansion. Disease-associated genetic mutations are strongly associated with quality degradation. TruSight One is a 4813-gene sequencing panel covering a wide range of known disease-associated genes, which may facilitate the detection of abnormal cells. Unlike the CNVs described in Fig. 2A, de novo mutations were not concentrated on chromosome (chr) 10 in the healthy cell #1 clone. NGS analysis of cultured DPSCs was performed to assess disease-associated mutations during the DPSC expansion process. DPSCs from patients with T1DM showed many de novo mutations in the whole chromosomes, similar to healthy clones (Supplementary Fig. 2). From passages 10 to 20, the number of mutations did not increase significantly but only slightly. Rather than disease-specific differences in the total number of mutations, differences between clones were observed in normal clones. These mutation sites were plotted using nuclear chromosomes, and the four clones from healthy individuals and patients showed similar distribution patterns (Fig. 3A). A total of 3,704 variants were detected across clones from healthy individuals and patients (P10 and P20), and most mutations were haplotype mutations. We found mutations in all clones, with a slight difference in chromosomes 8 and 19 in the normal and patient clones, respectively; however, there was a common tendency for a high number of accumulations in chr1, 2, 6, and 11, similar to common mutations. After passage 10, de novo mutations accumulated in high numbers on chr1, 2, 6, and 12 in all clones P20 from healthy individuals and patients (Fig. 3B). After 10 passages, the number of newly occurring mutations at 20 passages was low, and all clones showed similar patterns of mutation accumulation, indicating differences between clones rather than between normal and patient samples (Supplementary Table 1). The number of variants and genes increased at P20 in healthy DPSCs but did not change in patient DPSCs compared with the P10 and P20 clones in healthy and patient cells.

When the mutation pattern was analyzed for the 3704 mutations common to all clones, most were single nucleotide substitutions, T > C and C > T, in all 23 chromosomes (Fig. 4A). Focusing on the P20 clone, many substitutions were found, including T > C and C > T, with no difference between healthy and diseased clones; however, variations between clones were found (Fig. 4B). Deletion and insertion mutations that occurred after passage 10 were counted (Fig. 4C). Deletion mutants (1–3 bp) appeared most frequently in all clones, whereas deletions or insertions > 3 bp only occurred in patient clones. Gene and variant patterns are described in Supplementary Table 2. We found common variant genes in all cultured DPSCs, including ALDH4A1 (chr1), FCGR3A (chr1), FRG1 (chr4), CYP21A1P (chr6), HLA-DRB5 (chr6), PSPH (chr7), KMT2C (chr7), ATN1 (chr12), and KDM6B (chr17) in healthy cells, NBPF1 (chr1), GFI1 (chr1), GFPT1 (chr1), FRG1(chr.4), PTCH1 (chr9), and CES1 (chr16), in patient cells.

These results demonstrate the frequency and tendency of de novo mutations in the culture expansion of DPSCs.

4. Discussion

In this study, we evaluated cellular senescence, proliferation capacity, chromosomal CNVs, and de novo mutations in DPSCs during long-term expansion culture to assess their stability and suitability for use in cell therapy. Addressing the critical concern raised in the Introduction regarding the safety and quality control of MSC-based therapies, our findings provide new insights into the temporal dynamics of genetic alterations during ex vivo expansion. To our knowledge, this is one of the first studies to longitudinally evaluate both CNVs and de novo mutations in DPSCs derived from healthy and diseased donors during extended culture.

We demonstrated that DPSCs maintained surface marker expression up to at least 10 passages, as reflected by a low proportion of senescent cells and preserved proliferative activity. However, genomic stability appeared to be influenced by donor background and disease status (Fig. 1, Fig. 2). These findings directly relate to the known donor-dependent variability described in MSC-based therapies and reinforce the need for individualized quality assessment.

Cellular senescence is regulated by multiple interconnected mechanisms, including DNA damage, oxidative stress, autophagy dysfunction, and epigenetic alterations. Sustained DNA damage activates the DNA damage response (DDR) [24], leading to sustained activation of the p53/p21 and p16 pathways and ultimately triggering senescence and functional decline [25]. Although DPSCs have been reported to exhibit slower senescence and higher neurogenic potential compared with other MSC populations, our data indicate that extended culture beyond 10 passages is associated with increasing genomic alterations, even in the absence of overt morphological changes. Comparative studies have shown that DPSCs display slower senescence and higher neurogenic potential compared with other MSC types, suggesting unique aging mechanisms. During senescence, MSCs release components of the senescence-associated secretory phenotype (SASP), including proteases, growth factors, cytokines, and chemokines, which may promote inflammation and tumorigenesis [[26], [27], [28]]. These findings highlight the importance of genetic monitoring during early culture stages to identify and eliminate abnormal clones with chromosomal imbalances before they expand.

Notably, CNVs and de novo mutations accumulated in parallel with declining proliferative potential (Fig. 2). These alterations were detected using CytoScan arrays and a targeted NGS–based exome panel, enabling higher sensitivity for low-frequency variants compared with conventional cytogenetic methods. Importantly, mutation-bearing cells may persist during culture in culture, underscoring the limitations of morphology-based quality control. While certain mutation patterns were recurrent across donors and disease backgrounds, their functional consequences remain to be elucidated. These alterations may represent either driver mutations influencing cellular phenotype or passenger mutations without measurable biological impact.

Interestingly, DPSCs derived from donors with T1DM exhibited earlier signs of senescence and more widespread chromosomal alterations across the genome (Fig. 2), although it is uncertain whether these patterns are specific to the disease state. Our study also identified various mutation profiles: Neuromuscular disease–related genes: FRG1, ATN1, GFPT1, Immune-related genes: HLA-DRB5, FCGR3A, KDM6B, Cancer-associated genes: GFI1, PTCH1, Epigenetic regulators: KMT2C, KDM6B, ATN1, Metabolic and developmental genes: PSPH, ALDH4A1, CES1. The identification of mutations in cancer-associated, immune-related, and epigenetic regulatory genes raises potential safety concerns for clinical applications; however, the pathogenic significance of these variants cannot be inferred solely from sequencing data and warrants functional investigation.

Current minimal criteria to define human MSCs (hMSCs) are insufficient to predict therapeutic success. Therefore, additional parameters such as viability, purity, potency, proliferation rate, genomic stability, and tumorigenicity should be evaluated [29]. Cytogenetic analyses including array-CGH, SNP arrays, and conventional karyotyping (e.g., G-banding, FISH) are essential to detect chromosomal aberrations during culture [2,[30], [31], [32]], but NGS provides complementary sensitivity for detecting subclonal mutations. Consistent with ISCT recommendations, comprehensive chromosomal analysis should be integrated into standardized manufacturing workflows [2,32]. Even minimal genomic changes during expansion may significantly impact cell phenotype and function [33,34].

Although this study identified associations between senescence and mutation accumulation, it did not establish direct causality. The limited sample size and donor heterogeneity represent important constraints. Larger, longitudinal studies incorporating functional assays will be required to determine whether specific genetic alterations directly drive replicative decline or alter therapeutic efficacy.

DPSCs are a promising tool for cell-based therapy due to their multipotency, immunomodulatory effects, and relative genomic stability during early passages. However, long-term culture can result in donor- and disease-specific mutation accumulation. Genomic surveillance using targeted sequencing and CNV analysis may facilitate the early detection of unstable clones and improve the safety and standardization of stem cell-based products.

5. Conclusions

In recent years, the pattern of genetic mutations that occur during the cell culture process, particularly in cancer-related genes, as an important part of the necessary risk assessment, has gained traction. While tumor risk is the most important point of evaluation, the potential for risk of cell transplantation to affect immune response and metabolism should also be considered as part of the assessment. In this study, we identified genes susceptible to mutations that are associated with risk during the culture. However, this study has a few limitations, primarily the small sample size, which reduces the statistical power and limits the definitive interpretation. Therefore, our study should be regarded as a pilot/exploratory investigation that provides preliminary observations and a potential basis for future studies with larger cohorts. A quality assessment of these genes is required when monitoring the culture process of MSCs/DPSCs.

Author contributions

Y. N-K. and T. O. conceived and planned the experiments. K. H., Y. N-K., and N. S-M performed the experiments and contributed to sample preparation. K. H., Y. N-K., Y. O., and Y. A. analyzed the data. A. W. provided suggestions regarding the experiments. Y. N-K. K.H. Wrote the manuscript. T.O. Supervised the project.

Funding

This work was supported by the funding bodies of the Carrier Support Center of Nippon Medical School, the Japan Agency for Medical Research and Development (AMED) under grant numbers JP24bm1523001, JP24bm1523007, and KAKENHI Grant-in-Aid for Scientific Research (A) 24H00646, (B) 25K02459, and (C) 22K06921. The funding bodies of the Carrier Support Center of Nippon Medical School were responsible for the sample collection and experimentation. AMED and KAKENHI funding was partially involved in the costs of employment, manuscript writing, and English editing.

Declaration of competing interest

DPSCs were provided by the Cell Technology Corporation.

Acknowledgments

The authors would like to acknowledge Cell Technology Corporation for providing support materials. We are also grateful to Yoshitaka Miyagawa, Yoshiyuki Yamazaki, and Mashito Sakai (Nippon Medical School) for their technical advice and support, and to Aki Nakamura-Takahashi, Chiaki Masuda, Yuki Oda, Tomomi Fukatsu, and Maya Kawamura (Nippon Medical School) for their technical assistance. We also thank Yukihiko Hirai for his contributions to the manuscript revision and Guillermo Posadas-Herrera for English editing.

Footnotes

Peer review under responsibility of the Japanese Society for Regenerative Medicine.

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.reth.2026.101080.

Contributor Information

Yuko Nitahara-Kasahara, Email: y-kasahara@ims.u-tokyo.ac.jp.

Takashi Okada, Email: t-okada@ims.u-tokyo.ac.jp.

Appendix ASupplementary data

The following is the supplementary data to this article:

Multimedia component 1
mmc1.docx (55.2KB, docx)

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