
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39251776
71822
10.1038/s41598-024-71822-4
Article
Variant load of mitochondrial DNA in single human mesenchymal stem cells
Hipps Daniel daniel.hipps@nhs.net

12
Pyle Angela 2
Porter Anna L. R. 2
Dobson Philip F. 12
Tuppen Helen 2
Lawless Conor 2
Russell Oliver M. 2
Turnbull Doug M. 12
Deehan David J. 1
Hudson Gavin gavin.hudson@ncl.ac.uk

3
1 https://ror.org/05p40t847 grid.420004.2 0000 0004 0444 2244 The Newcastle Upon Tyne Hospitals NHS Foundation, Newcastle upon Tyne, UK
2 grid.1006.7 0000 0001 0462 7212 Wellcome Centre for Mitochondrial Research, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
3 grid.1006.7 0000 0001 0462 7212 Wellcome Centre for Mitochondrial Research, Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
9 9 2024
9 9 2024
2024
14 2098923 7 2024
30 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Heteroplasmic mitochondrial DNA (mtDNA) variants accumulate as humans age, particularly in the stem-cell compartments, and are an important contributor to age-related disease. Mitochondrial dysfunction has been observed in osteoporosis and somatic mtDNA pathogenic variants have been observed in animal models of osteoporosis. However, this has never been assessed in the relevant human tissue. Mesenchymal stem cells (MSCs) are the progenitors to many cells of the musculoskeletal system and are critical to skeletal tissues and bone vitality. Investigating mtDNA in MSCs could provide novel insights into the role of mitochondrial dysfunction in osteoporosis. To determine if this is possible, we investigated the landscape of somatic mtDNA variation in MSCs through a combination of fluorescence-activated cell sorting and single-cell next-generation sequencing. Our data show that somatic heteroplasmic variants are present in individual patient-derived MSCs, can reach high heteroplasmic fractions and have the potential to be pathogenic. The identification of somatic heteroplasmic variants in MSCs of patients highlights the potential for mitochondrial dysfunction to contribute to the pathogenesis of osteoporosis.

Keywords

Mesenchymal stem cells
Somatic mtDNA variation
Osteoporosis
Bone disease
Subject terms

Genomic analysis
PCR-based techniques
Next-generation sequencing
Mesenchymal stem cells
Bone
Osteoporosis
http://dx.doi.org/10.13039/100010269 Wellcome Trust 203105/Z/16/Z http://dx.doi.org/10.13039/501100000297 Royal College of Surgeons of England RES/0163/7579 Hipps Daniel Biotechnology and Biological Sciences Research Council and MRCG016354/1 issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Mesenchymal stem cells (MSCs) are the non-haematopoietic progenitor cells of the musculoskeletal system1. MSCs have the capacity to differentiate into mesodermal lineages such as osteocytes, adipocytes, and chondrocytes as well ectodermal and endodermal lineages and, are thus critical to the generation, maintenance and repair of skeletal tissues and bone1. However, the regenerative potential of MSCs decreases during ageing2,3. Thus, therapeutic strategies that either remove, rejuvenate, or replace senescent MSCs have been proposed to repair bone and cartilage injuries4 or to mitigate the degeneration that occurs during age-related diseases such as osteoarthritis and osteoporosis5,6. However, significant challenges remain. MSCs are relatively rare cells and are difficult to isolate and culture7. In addition, there is uncertainty regarding successful delivery to affected tissues8 and MSCs are often rapidly removed after transplantation which limits their therapeutic ability9,10.

Primary osteoporosis occurs when the destruction of bone by osteoclasts overtakes the formation of bone by osteoblasts11 and is associated with the senescence of MSCs12. The reduction in bone mass and microarchitecture deterioration results in a loss of strength leading to an increased risk of fragility fractures with advancing age13–15, with bone mass and mineral density peaking in the third decade before declining with advancing age16.

Decreased mitochondrial function is a hallmark of human ageing17 often accompanied by an increase in somatic mitochondrial DNA (mtDNA) variation18, and is a component of several age-related bone diseases including osteoporosis and osteoarthritis19,20. Animal models of increased mtDNA mutagenesis show evidence of osteoporosis21 accelerated bone loss and bone formation, with a corresponding reduction in osteoblasts and increased osteoclasts20. This age-related bone loss correlates with increased mitochondrial dysfunction in MSCs20. In humans, osteoblast populations and mitochondrial deficiency both increase with age16, and it has been suggested that mitochondria regulate the balance between osteoclast/osteoblast activity19. These observations are consistent with the animal models and suggest that the accumulation of somatic mtDNA variants may play a significant role in the pathoetiology of osteoporosis in humans.

However, investigating whether somatic mtDNA variants have a role in age-related bone disease is challenging. MtDNA mutations are known to accumulate in stem cell populations22, however, MSCs are relatively sparse and there are challenges relating to accurate and efficient isolation22 Additionally, mtDNA is polyploid23 and variants can be present on all mtDNAs (a state known as homoplasmy) or only in a fraction of mtDNAs (known as heteroplasmy)24. To manifest a phenotype, a heteroplasmic variant must reach a specific threshold, which can range between 60 and 90%25. In addition, somatic mtDNA variants are acquired, often initially in stem cell compartments and then persisting in differentiated cells26, and can increase in heteroplasmic fraction during ageing due to clonal expansion27. Thus, to be confident of the understanding of mtDNA variants in age-related diseases, we must investigate individual cells.

Understanding the mutational landscape of MSCs could explain the increase in respiratory deficient osteoblasts seen in previous studies and thus play a role in the pathogenesis of osteoporosis. Here we present a proof-of-concept study, using single-cell mtDNA sequencing to investigate the landscape of somatic mtDNA variation in MSCs, the precursors to human osteoblasts.

Materials and methods

Cohort

Bone marrow samples were taken at the time of elective hip arthroplasty and other routine orthopaedic surgeries from 13 individuals (5 males, mean age = 51.0 ± 26.4, and 8 Females, mean age = 78.8 ± 15.9) undergoing elective orthopaedic surgery for either trauma or osteoarthritis between 2017 and 2019 from the Newcastle upon Tyne Hospitals (STable 1). Ethical approval for the use of adult human samples was gained as an adjunct to the Newcastle Bone and Joint Biobank (REC reference 14/NE/1212, IRAS project ID166522 and Newcastle University reference 8741/2016). All patients gave informed consent to the use of tissue as part of the ethical approval requirements and experiments were performed in accordance with relevant guidelines and regulations.

MSC isolation and lysis

Bone marrow samples were split into two equal-volume aliquots, one for single cell isolation and a second to generate a ‘consensus sample’ for comparison. For one aliquot, mononuclear cells were concentrated and separated from the rest of the marrow (X ml) using a density gradient (a 1.077 g/ml Lymphoprep™ density gradient medium, StemCell Technologies)28. This removed contaminants from the sample such as red cells, fat and multinucleated cells which were not of interest. MSCs were isolated within the Lymphoprep™—marrow interphase. MSCs were concentrated within the mononuclear cells to facilitate Fluorescence-activated cell sorting (FACS, SFigure 1). Individual MSCs were sorted from the mononuclear cell pool by FACS using lineage-specific cell markers. Purified MSCs were defined as positive for cell surface markers, CD73, CD90, and CD105 while being negative for CD34 and CD45 (based on the MSC Phenotyping Cocktail Kit, anti-human, REAfinity™ and previous work29–31. Individual MSCs were FACS sorted into wells of a 96-well microtiter plate using electrostatic deflection (taking ~ 20 min per 96 cells). Each MSC was lysed in 15 µl single-cell lysis buffer, containing 0.5 M Tris–HCl, 0.5% Tween 20, 1% Proteinase K, pH 8.5. Lyses were centrifuged at 150G for 2 min at room temperature to ensure the cell was submerged. MSCs were lysed for 3 h at 55 °C.

In total, 146 MSCs were extracted across the 13 patient bone marrow samples (> 10 cells per sample, STable 2). The remaining aliquot (estimated as ~ 1 million cells) was also lysed using the same conditions and used as a per sample reference for mtDNA sequencing.

Mitochondrial DNA amplification and deep sequencing

As in previous work32, and to ensure efficient and accurate amplification from low-concentration single-cell extracted DNA (i.e., fg/ml), mtDNA was enriched using five overlapping long-range PCR amplicons (rCRS, or GenBank ID NC_012920.1, positions as—Set 1: m.323-343 and m.3574-3556; Set 2: m.3017-3036 and m.6944-6924; Set 3: m.6358-6377 and m.10147-10128; Set 4: m.9607-9627 and m.13859-13839 and Set 5 m.13365-13383 and m.771-752). As in previous work32, primer efficiency and specificity were assessed as successful after zero amplification of DNA from Rho0 cell lines, to avoid the unintended amplification of nuclear pseudogenes (nuclear-mitochondrial segments or NUMTs33). Rho0 cell lines were provided by the Mitochondrial Research Group, Institute of Genetic Medicine, Newcastle University.

Amplified products were assessed by gel electrophoresis, against DNA +ve and DNA −ve controls, and quantified using a Qubit 2.0 fluorimeter (Life Technologies, Paisley, UK), purified using Agencourt AMPure XP beads (Beckman-Coulter, USA) and pooled in equimolar concentrations and re-quantified. Pooled amplicons were tagmented, amplified, cleaned, normalized, and pooled into a single Illumina library using the Nextera XT DNA sample preparation kit (Illumina, CA, USA). Multiplex pools were sequenced using MiSeq Reagent Kit v3.0 (Illumina, CA, USA) in paired-end, 250 bp reads. Post-sequencing data, limited to reads with QV ≥ 30, were exported for analysis. In total, 140 cells were successfully amplified, sequenced, and moved forward for bioinformatic analysis (STable 2).

Bioinformatic analysis

Post-sequencing FASTQ files from 140 samples were analysed using an in-house bioinformatic pipeline32,34,35. Briefly, sequence reads were quality checked using FastQC (v. 011.8), mapped against genome version GRCh38 (including NC_012920.1), using BWA (invoking –mem, v. 0.7.17)36 sorted and indexed using Samtools (v.1.12)37. All duplicated reads from the resulting bam files were marked using Picard (v.2.2.4). mtDNA variants (mtSNVs) were called using bcftools (v1.10.2) and Mutserve (v.2.0.0) accordingly38,39. mtSNVs with base quality (--baseQ) over 30 and minimum heteroplasmy level (--level) 0.02 were called. Low-quality variants, present in low-complexity regions40 were not included in comparative analysis (e.g., 66–71 bp, 300–316 bp, 513–525 bp, 3106–3107 bp, 12,418–21,425 bp, 16,181–16,194 bp). The remaining variants were annotated via ENSEMBL VEP v10741. Heteroplasmic fraction (HF) is defined as the proportion of mtDNA variant allele depth relative to reference allele depth. Homoplasmic variation was defined as HF > 0.98 (98%). Heteroplasmic variation was defined as HF > 0.02/< 0.98 (2% and < 98%). Heteroplasmic counts are based on the number of heteroplasmies per cell. To ensure high-quality comparisons, we invoked strict post-bioinformatic quality control (QC). We removed samples if mtDNA (rCRS or GenBank ID NC_012920.1) coverage was < 99% at a minimal depth of 1500×.

After filtering for low coverage, 99 cells remained (ensuring at least > 3 cells per sample) and were taken forward for analyses (STable 3). After variant calling, each single cell was compared to the consensus sample to confirm homology through homoplasmic variant sharing (e.g., mtDNA haplogroups42, STable 4) and to assess potential contamination. Haplogroups were determined using Haplogrep 3 (v3.2.1, confidence > 0.943, and all samples were of European ancestry (STable 4).

Statistical analysis

Somatic variants were defined as heteroplasmies (> 2% and < 98%) present in 1 cell and absent in the consensus (STable 4) and each of the other cells. All analyses were carried out in R (v3.4) using data-appropriate tests (detailed in text). Statistical significance was set at < 0.05. Multiple significance correction can be too conservative for a discovery study, particularly when testing a priori hypotheses with variables that are not all independent44. Thus, unless specified in the text, we report unadjusted P values for reasons well documented in the literature44.

Results

MSC identification and isolation

Through a combination of density gradient and flow cytometry cell sorting (FACS), we were able to successfully isolate 146 single-MSCs across the 13 patient bone marrow samples (STable 2). All single-MSCs (100%) and the 13 consensus samples were accurately enriched for mtDNA and prepared for next-generation sequencing (NGS). After NGS, we obtained high-quality sequence data (covering > 99% mtDNA at > 1500 at > 1500×) for 99 MSCs and each consensus sequence (STable 2), with 47 MSCs removed due to low coverage (STables 2 and 3).

mtDNA variation

Prior to characterising the single MSCs, we analysed the consensus sequence data. Twelve of the 13 patient samples harboured heteroplasmic variation (ranging between 1 and 7 heteroplasmic variants with between ~ 0.02–0.94 heteroplasmy fractions, HF, STable 4). None were previously reported as deleterious (Mitomap.org). Consensus sample homoplasmic variation was used to confirm ancestry and single-MSCs/consensus similarity (all > 99% similarity).

Next, we investigated somatic heteroplasmic variation in each of the isolated single-MSCs (n = 99), using each corresponding consensus sequence data as a sample-specific reference. In total, we identified 7,849 somatic mtDNA variants with a HF > 0.00/< 0.98. The majority of these variants had a HF < 0.02 (5,372 or 68.4% of total somatic variants, Fig. 1). For further analysis, and similar to previous work32,45,46, we set a conservative HF threshold of 0.02, selecting 2,477 variants (or 31.6%) for further analysis (Fig. 1 and SDataset 1).Fig. 1 Histogram of somatic heteroplasmic variation in 13 patient samples. Frequency histogram of heteroplasmy fraction (HF) of all somatic heteroplasmic variants (HF > 0.00/< 1.00, n = 7849 variants) identified in 99 cells from 13 samples. The red line indicates a HF of 0.02. The vast majority of somatic heteroplasmic variation (n = 5,372 or 68.4%) had a HF < 0.02. 2,477 variants (31.6%) were taken forward for further analysis (SDataset 1).

Although there was some variability between single-MSCs within the same bone marrow sample, likely a result of cell-specific clonal expansion18,26,47, the distribution of heteroplasmic variant counts were highly similar between patient samples (Dunnett’s p > 0.05, Fig. 2 and STable 5). Additionally, although some specific cells harboured comparatively high-fraction variants (61 variants with HF > 0.5 across 12 out of 13 samples), the overall distribution of heteroplasmic fractions within single-MSCs was highly similar between patients (ANOVA p = 0.07 and Dunnett’s Test with Patient 1 as the reference category p > 0.05 for all groups) and was typically below 10% (Fig. 2 and SDataset 1). Further analysis of somatic heteroplasmic variants showed that 33.95% were T > C substitutions, 29.75% were A > G, 20.67% were C > T and 9.12% were G > A. The remaining possible polymorphisms (A > C, A > T, C > A, C > G, G > T, G > C, T > A and T > G) represented 6.5% of the variants detected (STable 6).Fig. 2 Somatic heteroplasmic count and heteroplasmic fraction distributions are similar across all 13 patient samples. Boxplots and strip charts showing the count of somatic heteroplasmies per cell per sample (upper) and the heteroplasmic fraction (lower, HF > 0.02/< 0.98) of each somatic heteroplasmic variant detected in each single-MSC grouped by sample. We observed no significant difference in heteroplasmy counts or heteroplasmy fraction distribution between samples (for each, ANOVA p > 0.05 and Dunnett’s p > 0.05 with patient 1 as reference). Boxes denoted 25th and 75th percentile, whiskers indicate 95th percentile and the horizontal line indicates the median.

Previous work has suggested that somatic mtDNA variation often occurs in regional hotspots32,48–51, particularly within the mtDNA control region (m.16024-57652). Thus, we next investigated whether single-MSCs showed a similar trend. Similar to others49,51, we observed a significant overrepresentation of somatic heteroplasmic variants in the D-Loop (m.16024-576) when compared to other loci (Fig. 3, Dunnets p, all comparisons < 0.05). However, we found no significant difference in overall HF between loci, (Fig. 3, ANOVA p = 0.115). Although, rRNA HFs were significantly higher when compared to D-Loop as a reference category (Dunnett’s p, D-Loop v rRNA = 0.038), tRNA and protein-coding variant HF distributions were not significantly different (Dunnett’s p, D-Loop v tRNA = 0.203 and D-Loop v protein coding = 0.082). This is in line with previous work53 and suggests that heteroplasmies in the rRNA genes (MT-RNR1 and MT-RNR2) are more tolerated as they must reach a higher HF to exert a biochemical defect54. Overall, these data suggest that although there is a regional accumulation of mtDNA variation, selection appears uniform across loci.Fig. 3 Somatic heteroplasmic count and heteroplasmic fractions stratified by locus. Strip charts showing count of heteroplasmies (HF > 0.02/< 0.98) per cell per sample after normalisation by locus length (total region bp lengths: D-loop = 1124, rRNA = 2511, tRNA = 1486 and Protein Coding = 11,382) (left) and the heteroplasmic fraction (HF > 0.02/< 0.98) of somatic heteroplasmies (right) grouped by locus type. As expected, there is a higher frequency of heteroplasmic variants in the D-Loop compared to rRNA, tRNA and protein-coding regions (ANOVA p = 8.2 × 10–5, Dunnett’s p, D-Loop v rRNA = 0.002, D-Loop v tRNA = 0.011, and D-Loop v protein coding = 0.001). There was no significant difference in overall HF between loci (ANOVA p = 0.115), However, rRNA HFs appear higher when compared to D-Loop as a reference (Dunnett’s p, D-Loop v rRNA = 0.038). tRNA and protein HFs distributions were not significantly different (Dunnett’s p, D-Loop v tRNA = 0.203 and D-Loop v protein coding = 0.082).

Finally, to explore the potential functional consequence of the identified somatic heteroplasmic variants in MSCs, we stratified protein-coding variants (Fig. 3) into variants predicted as non-synonomous and synonymous (Fig. 4). Although we found no significant difference in the HFs achieved by non-synonymous versus synonymous variants (Mann Whitney U, p > 0.05, Fig. 4), we did identify a significant increase in non-synonymous variant count (Mann Whitney U, p < 0.001, Fig. 4). Similar to previous work55,56, we calculated a MutPred score57 and APOGEE 258 scores of each of the coding region somatic heteroplasmic variants. APOGEE 2 appeared more conservative (Fig. 4), however, both tools showed a significant skew towards pathogenicity (MutPred score > 0.5057, Kurtosis = 3.17, Shapiro-Wilks Normality Test p < 0.001 and APOGEE 2 score > 0.3858, Kurtosis = 1.57, Shapiro-Wilks Normality Test p < 0.001) with the majority of somatic variants identified in MSCs from each sample predicted to be pathogenic (MutPred57 pathogenic = 949 variants or 82% and APOGEE 258 = pathogenic 593 or 51%, Fig. 4). Taken together, these data suggest that, similar to previous work59,60, somatic non-synonymous variation is relatively frequent in single MSCs from aged individuals and has the potential to be pathogenic, accepting that HFs are similar between non-synonymous and synonymous variants51.Fig. 4 Non Synonomous and synonomous somatic heteroplasmic count and heteroplasmic fractions stratified by locus. Upper Left: Strip chart showing the heteroplasmy (HF > 0.02/< 0.98) counts per cell per sample after normalisation by locus length (total region bp lengths: D-loop = 1124, rRNA = 2511, tRNA = 1486 and Protein Coding = 11,382). There was a significant increase in non-synonymous variant counts across all 13 individuals (Mann Whitney U, p < 0.001). Upper Right: Strip chart showing the distribution of protein-coding variant heteroplasmy fractions (HF > 0.02/< 0.98) after stratification into non-synonymous and synonymous variants. We observed no significant difference in HF between non-synonymous and synonymous variants (Mann Whitney U, p > 0.05). Lower left: Histogram of MutPred57 and APOGEE 258 scores showing a significant skew towards pathogenicity (MutPred score > 0.5057, Kurtosis = 3.17, Shapiro-Wilks Normality Test p < 0.001 and APOGEE 2 score > 0.3858, Kurtosis = 1.57, Shapiro-Wilks Normality Test p < 0.001). Dotted red lines indicate MutPred and APOGEE thresholds (0.5 and 0.38 respectively). Lower right: Boxplots of MSC somatic heteroplasmic MutPred and APOGEE 2 scores indicating that the majority of samples harbour cells with potentially pathogenic variants (MutPred57 pathogenic = 949 variants or 82% and APOGEE 258 = pathogenic 593 or 51%).

Discussion

Here we present a proof-of-principle study of the landscape of somatic mtDNA heteroplasmic variation in MSCs isolated from patient bone marrow (BM). Somatic heteroplasmic variation appears pervasive in MSCs, with multiple variants present in individual cells, often at high HF and with the potential to be deleterious. Understanding the mtDNA variant landscape of MSCs, the progenitors of cells of the musculoskeletal system1, has the potential to inform our understanding of bone-related conditions that exhibit age-related mitochondrial dysfunction.

Despite their critical role in the generation, maintenance and repair of skeletal tissues and bone, MSCs are relatively sparse, accounting for ~ 0.01% of the cellular population of bone marrow61. Thus, the accurate isolation of in-tact, uncontaminated, MSCs from BM is challenging62. Although MSCs can be isolated and cultured62 which would facilitate investigations of MSC mtDNA variation, previous work has shown that heteroplasmic fractions (HF) can dramatically change due to genetic drift and clonal expansion during culture23,63,64.

To overcome these challenges, we devised a FACs-based strategy that utilised both positive and negative selecting cell surface makers to isolate individual and intact BM-derived MSCs from all patient samples (n = 146 across 13 patient samples) for downstream next-generation-sequencing (NGS) of mtDNA. The success of single-cell sequencing and the accuracy of heteroplasmy detection is dependent on sample, template quantity and quality33. BM-derived MSCs are estimated to have ~ 800 copies of mtDNA65, which is on par with or higher than previous single-cell mtDNA sequencing experiments66–68. In line with this, we were able to successfully isolate and enrich mtDNA from all isolated MSCs. However, despite successful amplification of mtDNA in all cells, 47 MSCs fell below the quality control threshold, typically due to significant portions of low mtDNA coverage. In this instance, we preferred to offset the reduction in sample number, rather than reduce the confidence in heteroplasmy detection. For cells passing QC, we were able to identify somatic heteroplasmic variants. The count per cell and heteroplasmic fractions (HF) were comparable between samples. Overall, and similar to others69, the highest number of variants (after normalisation for sequence length) were observed in the D-Loop, followed by rRNA, protein-coding and finally tRNA regions. MSCs did appear to harbour an overrepresentation of non-synonymous somatic heteroplasmies and whilst the HFs were typically low (< 0.25), the vast majority were predicted to be pathogenic and thus have the potential to be functionally relevant.

This work provides insight into the origin of mtDNA variants in blood, bone, and skeletal tissues. MSCs respond to multiple different stimuli and differentiate into multiple cell lineages, such as bone, cartilage, adipose, muscle, tendon and stroma, or self-renew70. Tracking clonal expansion from one cell to daughter cells from in vivo samples is therefore very challenging. Thus, we do not know whether variants detected in MSCs play a role in respiratory deficiency seen in human osteoblasts, but it would be logical to conclude this could be the case. Taken together, our data suggest that MSCs can be accurately isolated and used to study mtDNA. Additionally, our data suggests that potentially pathogenic somatic heteroplasmic variation is relatively common in MSCs, albeit it at relatively low levels.

This is of particular interest given observations of respiratory chain deficiency in human osteoblasts during advancing age71. This mirrors deficiencies seen in in other disorders that are attributed to an age-related accumulation of somatic mtDNA variants22,23,72 and whilst investigations in dental tissue-derived MSCs identified some specific mtDNA pathogenic variants in MSCs65, work in BM-derived MSCs has been limited, restricted by sample availability. Our work shows that somatic variation does occur in early adulthood (i.e. patients 1 and 2 were aged 22 and 25 respectively at sampling) and similar to others31, suggests that studying clonal expansion of mtDNA variation in bone diseases could unlock some of the hidden pathology of these diseases. It is worth noting that investigating the dynamic relationship between somatic heteroplasmic variation and mitochondrial function is in itself challenging73 and would be compounded by the variation often observed between samples across age ranges74 and would require large sample numbers. For example, > 200 colon samples were required to establish the relevance of somatic heteroplasmic variants to the respiratory chain deficiency observed during human ageing74.

Conclusions

Several lines of evidence18,21,75,76 suggest that an age-related accumulation of somatic mtDNA variants contributes to disease20,34,71,74, including bone disease. In this proof-of-concept study, we provide evidence that somatic heteroplasmic variants are common in patient-derived MSCs, can reach high heteroplasmic fractions and have the potential to be pathogenic, overcoming the significant challenges of isolating and investigating patient-derived MSCs. This work suggests that a larger, cross-sectional study of mtDNA variation in bone disease is warranted.

Supplementary Information

Supplementary Information 1.

Supplementary Information 2.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71822-4.

Acknowledgements

The authors would like to express their deepest gratitude to the patients who donated tissue.

Author contributions

Conceptualization: D.H.; D.M.T. and G.H. Methodology: D.H.; A.P; O.M.R; and G.H. Data Acquisition and Analysis: D.H.; A.P.; A.L.R.P; P.D.; H.T.; C.L. and G.H. Manuscript Preparation: D.H.; D.M.T. and G.H. Funding acquisition: D.H.; D.J.D and D.M.T

Funding

This work was supported by The Wellcome Centre for Mitochondrial Research (203105/Z/16/Z), Newcastle University Centre for Ageing and Vitality (supported by the Biotechnology and Biological Sciences Research Council and MRC. G016354/1), the Malhotra Group, Newcastle NIHR Biomedical Research Centre in Age- and Age-Related Diseases award to the Newcastle upon Tyne Hospitals NHS Foundation Trust. The Royal College of Surgeons of England provided a one-year Fellowship to DH support this work (RES/0163/7579).

Data availability

Data Availability The datasets generated and/or analysed during the current study are available in the European Nucleotide Repository (Accession: PRJEB79104).

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Summer R Fine A Mesenchymal progenitor cell research: Limitations and recommendations Proc. Am. Thorac. Soc. 2008 5 707 710 10.1513/pats.200801-007AW 18684722
Summer, R. & Fine, A. Mesenchymal progenitor cell research: Limitations and recommendations. Proc. Am. Thorac. Soc. 5, 707–710. 10.1513/pats.200801-007AW (2008).18684722 10.1513/pats.200801-007AW
2. Babenko VA Age-related changes in bone-marrow mesenchymal stem cells Cells 2021 10 1273 10.3390/cells10061273 34063923
Babenko, V. A. et al. Age-related changes in bone-marrow mesenchymal stem cells. Cells 10, 1273. 10.3390/cells10061273 (2021).34063923 10.3390/cells10061273
3. Fraile M Eiro N Costa LA Martín A Vizoso FJ Aging and mesenchymal stem cells: Basic concepts, challenges and strategies Biology 2022 11 1678 10.3390/biology11111678 36421393
Fraile, M., Eiro, N., Costa, L. A., Martín, A. & Vizoso, F. J. Aging and mesenchymal stem cells: Basic concepts, challenges and strategies. Biology 11, 1678. 10.3390/biology11111678 (2022).36421393 10.3390/biology11111678
4. Wyles CC Houdek MT Behfar A Sierra RJ Mesenchymal stem cell therapy for osteoarthritis: Current perspectives Stem Cells Cloning Adv. Appl. 2015 8 117 124 10.2147/SCCAA.S68073
Wyles, C. C., Houdek, M. T., Behfar, A. & Sierra, R. J. Mesenchymal stem cell therapy for osteoarthritis: Current perspectives. Stem Cells Cloning Adv. Appl. 8, 117–124. 10.2147/SCCAA.S68073 (2015).10.2147/SCCAA.S68073
5. Aghebati-Maleki L Prospect of mesenchymal stem cells in therapy of osteoporosis: A review J. Cell. Physiol. 2019 234 8570 8578 10.1002/jcp.27833 30488448
Aghebati-Maleki, L. et al. Prospect of mesenchymal stem cells in therapy of osteoporosis: A review. J. Cell. Physiol. 234, 8570–8578 (2019).30488448 10.1002/jcp.27833
6. Freitag J Mesenchymal stem cell therapy in the treatment of osteoarthritis: reparative pathways, safety and efficacy: A review BMC Musculoskelet. Disord. 2016 17 230 10.1186/s12891-016-1085-9 27229856
Freitag, J. et al. Mesenchymal stem cell therapy in the treatment of osteoarthritis: reparative pathways, safety and efficacy: A review. BMC Musculoskelet. Disord. 17, 230. 10.1186/s12891-016-1085-9 (2016).27229856 10.1186/s12891-016-1085-9
7. Ramakrishnan A Torok-Storb B Pillai MM Primary marrow-derived stromal cells: Isolation and manipulation Methods Mol. Biol. (Clifton N.J.). 2013 1035 75 101 10.1007/978-1-62703-508-8_8
Ramakrishnan, A., Torok-Storb, B. & Pillai, M. M. Primary marrow-derived stromal cells: Isolation and manipulation. Methods Mol. Biol. (Clifton N.J.). 1035, 75–101. 10.1007/978-1-62703-508-8_8 (2013).10.1007/978-1-62703-508-8_8
8. Lee S Choi E Cha M-J Hwang K-C Cell adhesion and long-term survival of transplanted mesenchymal stem cells: A prerequisite for cell therapy Oxid. Med. Cell. Longev. 2015 2015 632902 10.1155/2015/632902 25722795
Lee, S., Choi, E., Cha, M.-J. & Hwang, K.-C. Cell adhesion and long-term survival of transplanted mesenchymal stem cells: A prerequisite for cell therapy. Oxid. Med. Cell. Longev. 2015, 632902. 10.1155/2015/632902 (2015).25722795 10.1155/2015/632902
9. Michel J-B Anoikis in the cardiovascular system: Known and unknown extracellular mediators Arterioscler. Thromb. Vasc. Biol. 2003 23 2146 2154 10.1161/01.ATV.0000099882.52647.E4 14551156
Michel, J.-B. Anoikis in the cardiovascular system: Known and unknown extracellular mediators. Arterioscler. Thromb. Vasc. Biol. 23, 2146–2154 (2003).14551156 10.1161/01.ATV.0000099882.52647.E4
10. Taddei M Giannoni E Fiaschi T Chiarugi P Anoikis: An emerging hallmark in health and diseases J. Pathol. 2012 226 380 393 10.1002/path.3000 21953325
Taddei, M., Giannoni, E., Fiaschi, T. & Chiarugi, P. Anoikis: An emerging hallmark in health and diseases. J. Pathol. 226, 380–393 (2012).21953325 10.1002/path.3000
11. Jiang Y Zhang P Zhang X Lv L Zhou Y Advances in mesenchymal stem cell transplantation for the treatment of osteoporosis Cell Prolif. 2021 54 e12956 10.1111/cpr.12956 33210341
Jiang, Y., Zhang, P., Zhang, X., Lv, L. & Zhou, Y. Advances in mesenchymal stem cell transplantation for the treatment of osteoporosis. Cell Prolif. 54, e12956. 10.1111/cpr.12956 (2021).33210341 10.1111/cpr.12956
12. Föger-Samwald U Kerschan-Schindl K Butylina M Pietschmann P Age related osteoporosis: Targeting cellular senescence Int. J. Mol. Sci. 2022 23 2701 10.3390/ijms23052701 35269841
Föger-Samwald, U., Kerschan-Schindl, K., Butylina, M. & Pietschmann, P. Age related osteoporosis: Targeting cellular senescence. Int. J. Mol. Sci. 23, 2701. 10.3390/ijms23052701 (2022).35269841 10.3390/ijms23052701
13. Riggs BL Wahner HW Dunn WL Mazess RB Offord KP Melton LJ 3rd Differential changes in bone mineral density of the appendicular and axial skeleton with aging: Relationship to spinal osteoporosis J. Clin. Investig. 1981 67 328 10.1172/JCI110039 7462421
Riggs, B. L. et al. Differential changes in bone mineral density of the appendicular and axial skeleton with aging: Relationship to spinal osteoporosis. J. Clin. Investig. 67, 328 (1981).7462421 10.1172/JCI110039
14. Riggs BL Changes in bone mineral density of the proximal femur and spine with aging: Differences between the postmenopausal and senile osteoporosis syndromes J. Clin. Investig. 1982 70 716 10.1172/JCI110667 7119111
Riggs, B. L. et al. Changes in bone mineral density of the proximal femur and spine with aging: Differences between the postmenopausal and senile osteoporosis syndromes. J. Clin. Investig. 70, 716 (1982).7119111 10.1172/JCI110667
15. Law MR Wald NJ Meade TW Strategies for prevention of osteoporosis and hip fracture BMJ: Br. Med. J. 1991 303 453 10.1136/bmj.303.6800.453 1912840
Law, M. R., Wald, N. J. & Meade, T. W. Strategies for prevention of osteoporosis and hip fracture. BMJ: Br. Med. J. 303, 453 (1991).1912840 10.1136/bmj.303.6800.453
16. Hendrickx G Boudin E Van Hul W A look behind the scenes: The risk and pathogenesis of primary osteoporosis Nat. Rev. Rheumatol. 2015 11 462 474 10.1038/nrrheum.2015.48 25900210
Hendrickx, G., Boudin, E. & Van Hul, W. A look behind the scenes: The risk and pathogenesis of primary osteoporosis. Nat. Rev. Rheumatol. 11, 462–474 (2015).25900210 10.1038/nrrheum.2015.48
17. Terzioglu, M. & Larsson, N. G. Mitochondrial dysfunction in mammalian ageing. In Novartis Foundation Symposium, Vol. 287. 197–208; discussion 208–113 (2007).
18. Linnane A Ozawa T Marzuki S Tanaka M Mitochondrial DNA mutations as an important contributor to ageing and degenerative diseases The Lancet 1989 333 642 645 10.1016/S0140-6736(89)92145-4
Linnane, A., Ozawa, T., Marzuki, S. & Tanaka, M. Mitochondrial DNA mutations as an important contributor to ageing and degenerative diseases. The Lancet 333, 642–645 (1989).10.1016/S0140-6736(89)92145-4
19. Yan C Mitochondrial quality control and its role in osteoporosis Front. Endocrinol. 2023 14 1077058 10.3389/fendo.2023.1077058
Yan, C. et al. Mitochondrial quality control and its role in osteoporosis. Front. Endocrinol. 14, 1077058. 10.3389/fendo.2023.1077058 (2023).10.3389/fendo.2023.1077058
20. Dobson PF Mitochondrial dysfunction impairs osteogenesis, increases osteoclast activity, and accelerates age related bone loss Sci. Rep. 2020 10 1 14 10.1038/s41598-020-68566-2 31913322
Dobson, P. F. et al. Mitochondrial dysfunction impairs osteogenesis, increases osteoclast activity, and accelerates age related bone loss. Sci. Rep. 10, 1–14 (2020).31913322 10.1038/s41598-020-68566-2
21. Trifunovic A Premature ageing in mice expressing defective mitochondrial DNA polymerase Nature 2004 429 417 423 10.1038/nature02517 15164064
Trifunovic, A. et al. Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nature 429, 417–423 (2004).15164064 10.1038/nature02517
22. Baines HL Turnbull DM Greaves LC Human stem cell aging: do mitochondrial DNA mutations have a causal role? Aging Cell 2014 13 201 205 10.1111/acel.12199 24382254
Baines, H. L., Turnbull, D. M. & Greaves, L. C. Human stem cell aging: do mitochondrial DNA mutations have a causal role?. Aging Cell 13, 201–205 (2014).24382254 10.1111/acel.12199
23. Lawless C Greaves L Reeve AK Turnbull DM Vincent AE The rise and rise of mitochondrial DNA mutations Open Biol. 2020 10 200061 10.1098/rsob.200061 32428418
Lawless, C., Greaves, L., Reeve, A. K., Turnbull, D. M. & Vincent, A. E. The rise and rise of mitochondrial DNA mutations. Open Biol. 10, 200061 (2020).32428418 10.1098/rsob.200061
24. Taylor RW Mitochondrial DNA mutations in human colonic crypt stem cells J. Clin. Investig. 2003 112 1351 1360 10.1172/JCI19435 14597761
Taylor, R. W. et al. Mitochondrial DNA mutations in human colonic crypt stem cells. J. Clin. Investig. 112, 1351–1360 (2003).14597761 10.1172/JCI19435
25. Tuppen HAL Blakely EL Turnbull DM Taylor RW Mitochondrial DNA mutations and human disease J Biochim. Biophys. Acta (BBA)-Bioenerg. 2010 1797 113 128 10.1016/j.bbabio.2009.09.005
Tuppen, H. A. L., Blakely, E. L., Turnbull, D. M. & Taylor, R. W. Mitochondrial DNA mutations and human disease. J Biochim. Biophys. Acta (BBA)-Bioenerg. 1797, 113–128 (2010).10.1016/j.bbabio.2009.09.005
26. Greaves LC Clonal expansion of early to mid-life mitochondrial DNA point mutations drives mitochondrial dysfunction during human ageing PLoS Genet. 2014 10 e1004620 10.1371/journal.pgen.1004620 25232829
Greaves, L. C. et al. Clonal expansion of early to mid-life mitochondrial DNA point mutations drives mitochondrial dysfunction during human ageing. PLoS Genet. 10, e1004620 (2014).25232829 10.1371/journal.pgen.1004620
27. Fayet G Ageing muscle: Clonal expansions of mitochondrial DNA point mutations and deletions cause focal impairment of mitochondrial function Neuromuscul. Disord. 2002 12 484 493 10.1016/S0960-8966(01)00332-7 12031622
Fayet, G. et al. Ageing muscle: Clonal expansions of mitochondrial DNA point mutations and deletions cause focal impairment of mitochondrial function. Neuromuscul. Disord. 12, 484–493 (2002).12031622 10.1016/S0960-8966(01)00332-7
28. Chan CD Co-localisation of intra-nuclear membrane type-1 matrix metalloproteinase and hypoxia inducible factor-2α in osteosarcoma and prostate carcinoma cells Oncol. Lett. 2021 21 1 1 33240407
Chan, C. D. et al. Co-localisation of intra-nuclear membrane type-1 matrix metalloproteinase and hypoxia inducible factor-2α in osteosarcoma and prostate carcinoma cells. Oncol. Lett. 21, 1–1 (2021).33240407
29. Baghaei K Isolation, differentiation, and characterization of mesenchymal stem cells from human bone marrow Gastroenterol. Hepatol. Bed Bench 2017 10 208 29118937
Baghaei, K. et al. Isolation, differentiation, and characterization of mesenchymal stem cells from human bone marrow. Gastroenterol. Hepatol. Bed Bench 10, 208 (2017).29118937
30. Miltenyi Biotec. (http://www.miltenyibiotec.com/en/products-and-services/macs-flow-cytometry/reagents/kits-and-assays/msc-phenotyping-kit-human.aspx, 2011).
31. Dominici M Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for cellular therapy position statement Cytotherapy 2006 8 315 317 10.1080/14653240600855905 16923606
Dominici, M. et al. Minimal criteria for defining multipotent mesenchymal stromal cells. The International Society for cellular therapy position statement. Cytotherapy 8, 315–317 (2006).16923606 10.1080/14653240600855905
32. Coxhead J Kurzawa-Akanbi M Hussain R Pyle A Chinnery P Hudson G Somatic mtDNA variation is an important component of Parkinson's disease Neurobiol. Aging 2016 38 217 e211 217.e216
Coxhead, J. et al. Somatic mtDNA variation is an important component of Parkinson’s disease. Neurobiol. Aging 38(217), e211-217.e216 (2016).
33. Santibanez-Koref M Griffin H Turnbull DM Chinnery PF Herbert M Hudson G Assessing mitochondrial heteroplasmy using next generation sequencing: A note of caution Mitochondrion 2019 46 302 306 10.1016/j.mito.2018.08.003 30098421
Santibanez-Koref, M. et al. Assessing mitochondrial heteroplasmy using next generation sequencing: A note of caution. Mitochondrion 46, 302–306 (2019).30098421 10.1016/j.mito.2018.08.003
34. Lowes H Kurzawa-Akanbi M Pyle A Hudson G Post-mortem ventricular cerebrospinal fluid cell-free-mtDNA in neurodegenerative disease Sci. Rep. 2020 10 15253 10.1038/s41598-020-72190-5 32943697
Lowes, H., Kurzawa-Akanbi, M., Pyle, A. & Hudson, G. Post-mortem ventricular cerebrospinal fluid cell-free-mtDNA in neurodegenerative disease. Sci. Rep. 10, 15253 (2020).32943697 10.1038/s41598-020-72190-5
35. Bury, A. G., Robertson, F. M., Pyle, A., Payne, B. A. & Hudson, G. in Mitochondrial Medicine: Volume 3: Manipulating Mitochondria and Disease-Specific Approaches 433–447 (Springer, 2021).
36. Li H Durbin R Fast and accurate short read alignment with Burrows--Wheeler transform Bioinformatics 2009 25 1754 1760 10.1093/bioinformatics/btp324 19451168
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows--Wheeler transform. Bioinformatics 25, 1754–1760 (2009).19451168 10.1093/bioinformatics/btp324
37. Li H The sequence alignment/map format and SAMtools Bioinformatics 2009 25 2078 2079 10.1093/bioinformatics/btp352 19505943
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079. 10.1093/bioinformatics/btp352 (2009).19505943 10.1093/bioinformatics/btp352
38. Li H A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data Bioinformatics 2011 27 2987 2993 10.1093/bioinformatics/btr509 21903627
Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993. 10.1093/bioinformatics/btr509 (2011).21903627 10.1093/bioinformatics/btr509
39. Weissensteiner H mtDNA-Server: Next-generation sequencing data analysis of human mitochondrial DNA in the cloud Nucleic Acids Res. 2016 44 W64 69 10.1093/nar/gkw247 27084948
Weissensteiner, H. et al. mtDNA-Server: Next-generation sequencing data analysis of human mitochondrial DNA in the cloud. Nucleic Acids Res. 44, W64-69. 10.1093/nar/gkw247 (2016).27084948 10.1093/nar/gkw247
40. Goto H Dynamics of mitochondrial heteroplasmy in three families investigated via a repeatable re-sequencing study Genome Biol. 2011 12 1 16 10.1186/gb-2011-12-6-r59
Goto, H. et al. Dynamics of mitochondrial heteroplasmy in three families investigated via a repeatable re-sequencing study. Genome Biol. 12, 1–16 (2011).10.1186/gb-2011-12-6-r59
41. Zerbino DR Ensembl 2018 Nucleic Acids Res. 2018 46 D754 D761 10.1093/nar/gkx1098 29155950
Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761. 10.1093/nar/gkx1098 (2018).29155950 10.1093/nar/gkx1098
42. Torroni A Classification of European mtDNAs from an analysis of three European populations Genetics 1996 144 1835 1850 10.1093/genetics/144.4.1835 8978068
Torroni, A. et al. Classification of European mtDNAs from an analysis of three European populations. Genetics 144, 1835–1850 (1996).8978068 10.1093/genetics/144.4.1835
43. Schönherr S Weissensteiner H Kronenberg F Forer L Haplogrep 3-an interactive haplogroup classification and analysis platform Nucleic Acids Res. 2023 51 W263 w268 10.1093/nar/gkad284 37070190
Schönherr, S., Weissensteiner, H., Kronenberg, F. & Forer, L. Haplogrep 3-an interactive haplogroup classification and analysis platform. Nucleic Acids Res. 51, W263-w268. 10.1093/nar/gkad284 (2023).37070190 10.1093/nar/gkad284
44. Perneger TV What's wrong with Bonferroni adjustments Bmj 1998 316 1236 1238 10.1136/bmj.316.7139.1236 9553006
Perneger, T. V. What’s wrong with Bonferroni adjustments. Bmj 316, 1236–1238 (1998).9553006 10.1136/bmj.316.7139.1236
45. McElhoe JA Holland MM Characterization of background noise in MiSeq MPS data when sequencing human mitochondrial DNA from various sample sources and library preparation methods Mitochondrion 2020 52 40 55 10.1016/j.mito.2020.02.005 32068127
McElhoe, J. A. & Holland, M. M. Characterization of background noise in MiSeq MPS data when sequencing human mitochondrial DNA from various sample sources and library preparation methods. Mitochondrion 52, 40–55 (2020).32068127 10.1016/j.mito.2020.02.005
46. Calabrese C Heteroplasmic mitochondrial DNA variants in cardiovascular diseases PLoS Genet. 2022 18 e1010068 10.1371/journal.pgen.1010068 35363781
Calabrese, C. et al. Heteroplasmic mitochondrial DNA variants in cardiovascular diseases. PLoS Genet. 18, e1010068 (2022).35363781 10.1371/journal.pgen.1010068
47. Khrapko K The timing of mitochondrial DNA mutations in aging Nat. Genet. 2011 43 726 727 10.1038/ng.895 21792237
Khrapko, K. The timing of mitochondrial DNA mutations in aging. Nat. Genet. 43, 726–727 (2011).21792237 10.1038/ng.895
48. Galtier N Enard D Radondy Y Bazin E Belkhir K Mutation hot spots in mammalian mitochondrial DNA Genome Res. 2006 16 215 222 10.1101/gr.4305906 16354751
Galtier, N., Enard, D., Radondy, Y., Bazin, E. & Belkhir, K. Mutation hot spots in mammalian mitochondrial DNA. Genome Res. 16, 215–222 (2006).16354751 10.1101/gr.4305906
49. Stoneking M Hypervariable sites in the mtDNA control region are mutational hotspots Am. J. Hum. Genet. 2000 67 1029 1032 10.1086/303092 10968778
Stoneking, M. Hypervariable sites in the mtDNA control region are mutational hotspots. Am. J. Hum. Genet. 67, 1029–1032 (2000).10968778 10.1086/303092
50. Smith AL Age-associated mitochondrial DNA mutations cause metabolic remodeling that contributes to accelerated intestinal tumorigenesis Nat. Cancer 2020 1 976 989 10.1038/s43018-020-00112-5 33073241
Smith, A. L. et al. Age-associated mitochondrial DNA mutations cause metabolic remodeling that contributes to accelerated intestinal tumorigenesis. Nat. Cancer 1, 976–989 (2020).33073241 10.1038/s43018-020-00112-5
51. Wei W Germline selection shapes human mitochondrial DNA diversity Science 2019 364 eaau6520 10.1126/science.aau6520 31123110
Wei, W. et al. Germline selection shapes human mitochondrial DNA diversity. Science 364, eaau6520 (2019).31123110 10.1126/science.aau6520
52. Xu J Single nucleotide polymorphisms in the D-loop region of mitochondrial DNA is associated with the kidney survival time in chronic kidney disease patients Renal Fail. 2015 37 108 112 10.3109/0886022x.2014.976132
Xu, J. et al. Single nucleotide polymorphisms in the D-loop region of mitochondrial DNA is associated with the kidney survival time in chronic kidney disease patients. Renal Fail. 37, 108–112. 10.3109/0886022x.2014.976132 (2015).10.3109/0886022x.2014.976132
53. Li M Transmission of human mtDNA heteroplasmy in the Genome of the Netherlands families: Support for a variable-size bottleneck Genome Res. 2016 26 417 426 10.1101/gr.203216.115 26916109
Li, M. et al. Transmission of human mtDNA heteroplasmy in the Genome of the Netherlands families: Support for a variable-size bottleneck. Genome Res. 26, 417–426 (2016).26916109 10.1101/gr.203216.115
54. Smith PM The role of the mitochondrial ribosome in human disease: searching for mutations in 12S mitochondrial rRNA with high disruptive potential Hum. Mol. Genet. 2014 23 949 967 10.1093/hmg/ddt490 24092330
Smith, P. M. et al. The role of the mitochondrial ribosome in human disease: searching for mutations in 12S mitochondrial rRNA with high disruptive potential. Hum. Mol. Genet. 23, 949–967 (2014).24092330 10.1093/hmg/ddt490
55. Venter M Malan L Van Dyk E Elson JL Van der Westhuizen FH Using MutPred derived mtDNA load scores to evaluate mtDNA variation in hypertension and diabetes in a two-population cohort: The SABPA study J. Genet. Genomics 2017 44 139 149 10.1016/j.jgg.2016.12.003 28298255
Venter, M., Malan, L., Van Dyk, E., Elson, J. L. & Van der Westhuizen, F. H. Using MutPred derived mtDNA load scores to evaluate mtDNA variation in hypertension and diabetes in a two-population cohort: The SABPA study. J. Genet. Genomics 44, 139–149 (2017).28298255 10.1016/j.jgg.2016.12.003
56. Cox SN Mitochondrial and nuclear DNA variants in amyotrophic lateral sclerosis: Enrichment in the mitochondrial control region and Sirtuin pathway genes in spinal cord tissue Biomolecules 2024 14 411 10.3390/biom14040411 38672428
Cox, S. N. et al. Mitochondrial and nuclear DNA variants in amyotrophic lateral sclerosis: Enrichment in the mitochondrial control region and Sirtuin pathway genes in spinal cord tissue. Biomolecules 14, 411. 10.3390/biom14040411 (2024).38672428 10.3390/biom14040411
57. Li B Automated inference of molecular mechanisms of disease from amino acid substitutions Bioinformatics 2009 25 2744 2750 10.1093/bioinformatics/btp528 19734154
Li, B. et al. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 25, 2744–2750 (2009).19734154 10.1093/bioinformatics/btp528
58. Bianco SD APOGEE 2: Multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants Nat. Commun. 2023 14 5058 10.1038/s41467-023-40797-7 37598215
Bianco, S. D. et al. APOGEE 2: Multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants. Nat. Commun. 14, 5058. 10.1038/s41467-023-40797-7 (2023).37598215 10.1038/s41467-023-40797-7
59. Zhang R Wang Y Ye K Picard M Gu Z Independent impacts of aging on mitochondrial DNA quantity and quality in humans BMC genomics 2017 18 1 14 10.1186/s12864-017-4287-0 28049423
Zhang, R., Wang, Y., Ye, K., Picard, M. & Gu, Z. Independent impacts of aging on mitochondrial DNA quantity and quality in humans. BMC genomics 18, 1–14 (2017).28049423 10.1186/s12864-017-4287-0
60. Kang E Age-related accumulation of somatic mitochondrial DNA mutations in adult-derived human iPSCs Cell Stem Cell 2016 18 625 636 10.1016/j.stem.2016.02.005 27151456
Kang, E. et al. Age-related accumulation of somatic mitochondrial DNA mutations in adult-derived human iPSCs. Cell Stem Cell 18, 625–636. 10.1016/j.stem.2016.02.005 (2016).27151456 10.1016/j.stem.2016.02.005
61. Lin GL Hankenson KD Integration of BMP, Wnt, and notch signaling pathways in osteoblast differentiation J. Cell. Biochem. 2011 112 3491 3501 10.1002/jcb.23287 21793042
Lin, G. L. & Hankenson, K. D. Integration of BMP, Wnt, and notch signaling pathways in osteoblast differentiation. J. Cell. Biochem. 112, 3491–3501 (2011).21793042 10.1002/jcb.23287
62. Baustian C Hanley S Ceredig R Isolation, selection and culture methods to enhance clonogenicity of mouse bone marrow derived mesenchymal stromal cell precursors Stem Cell Res. Therapy 2015 6 151 10.1186/s13287-015-0139-5
Baustian, C., Hanley, S. & Ceredig, R. Isolation, selection and culture methods to enhance clonogenicity of mouse bone marrow derived mesenchymal stromal cell precursors. Stem Cell Res. Therapy 6, 151. 10.1186/s13287-015-0139-5 (2015).10.1186/s13287-015-0139-5
63. Prigione A Human induced pluripotent stem cells harbor homoplasmic and heteroplasmic mitochondrial DNA mutations while maintaining human embryonic stem cell-like metabolic reprogramming Stem cells 2011 29 1338 1348 10.1002/stem.683 21732474
Prigione, A. et al. Human induced pluripotent stem cells harbor homoplasmic and heteroplasmic mitochondrial DNA mutations while maintaining human embryonic stem cell-like metabolic reprogramming. Stem cells 29, 1338–1348 (2011).21732474 10.1002/stem.683
64. Wei W Gaffney DJ Chinnery PF Cell reprogramming shapes the mitochondrial DNA landscape Nat. Commun. 2021 12 5241 10.1038/s41467-021-25482-x 34475388
Wei, W., Gaffney, D. J. & Chinnery, P. F. Cell reprogramming shapes the mitochondrial DNA landscape. Nat. Commun. 12, 5241 (2021).34475388 10.1038/s41467-021-25482-x
65. Park J Mitochondrial genome mutations in mesenchymal stem cells derived from human dental induced pluripotent stem cells BMB Rep. 2019 52 689 694 10.5483/BMBRep.2019.52.12.045 31234953
Park, J. et al. Mitochondrial genome mutations in mesenchymal stem cells derived from human dental induced pluripotent stem cells. BMB Rep. 52, 689–694. 10.5483/BMBRep.2019.52.12.045 (2019).31234953 10.5483/BMBRep.2019.52.12.045
66. Miller TE Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations Nat. Biotechnol. 2022 40 1030 1034 10.1038/s41587-022-01210-8 35210612
Miller, T. E. et al. Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations. Nat. Biotechnol. 40, 1030–1034. 10.1038/s41587-022-01210-8 (2022).35210612 10.1038/s41587-022-01210-8
67. Payne BA Cree L Chinnery PF Single-cell analysis of mitochondrial DNA Methods Mol. Biol. (Clifton, N. J.) 2015 1264 67 76 10.1007/978-1-4939-2257-4_7
Payne, B. A., Cree, L. & Chinnery, P. F. Single-cell analysis of mitochondrial DNA. Methods Mol. Biol. (Clifton, N. J.) 1264, 67–76. 10.1007/978-1-4939-2257-4_7 (2015).10.1007/978-1-4939-2257-4_7
68. Bi C Single-cell individual full-length mtDNA sequencing by iMiGseq uncovers unexpected heteroplasmy shifts in mtDNA editing Nucleic Acids Res. 2023 51 e48 10.1093/nar/gkad208 36999592
Bi, C. et al. Single-cell individual full-length mtDNA sequencing by iMiGseq uncovers unexpected heteroplasmy shifts in mtDNA editing. Nucleic Acids Res. 51, e48. 10.1093/nar/gkad208 (2023).36999592 10.1093/nar/gkad208
69. Nie Y Murley A Golder Z Rowe JB Allinson K Chinnery PF Heteroplasmic mitochondrial DNA mutations in frontotemporal lobar degeneration Acta Neuropathol. 2022 143 687 695 10.1007/s00401-022-02423-6 35488929
Nie, Y. et al. Heteroplasmic mitochondrial DNA mutations in frontotemporal lobar degeneration. Acta Neuropathol. 143, 687–695. 10.1007/s00401-022-02423-6 (2022).35488929 10.1007/s00401-022-02423-6
70. Mareschi K Expansion of mesenchymal stem cells isolated from pediatric and adult donor bone marrow J. Cell. Biochem. 2006 97 744 754 10.1002/jcb.20681 16229018
Mareschi, K. et al. Expansion of mesenchymal stem cells isolated from pediatric and adult donor bone marrow. J. Cell. Biochem. 97, 744–754 (2006).16229018 10.1002/jcb.20681
71. Hipps D Detecting respiratory chain defects in osteoblasts from osteoarthritic patients using imaging mass cytometry Bone 2022 158 116371 10.1016/j.bone.2022.116371 35192969
Hipps, D. et al. Detecting respiratory chain defects in osteoblasts from osteoarthritic patients using imaging mass cytometry. Bone 158, 116371 (2022).35192969 10.1016/j.bone.2022.116371
72. Greaves LC Comparison of mitochondrial mutation spectra in ageing human colonic epithelium and disease: Absence of evidence for purifying selection in somatic mitochondrial DNA point mutations PLoS Genet. 2012 8 e1003082 10.1371/journal.pgen.1003082 23166522
Greaves, L. C. et al. Comparison of mitochondrial mutation spectra in ageing human colonic epithelium and disease: Absence of evidence for purifying selection in somatic mitochondrial DNA point mutations. PLoS Genet. 8, e1003082 (2012).23166522 10.1371/journal.pgen.1003082
73. Wallace DC Chalkia D Mitochondrial DNA genetics and the heteroplasmy conundrum in evolution and disease Cold Spring Harbor Perspect. Biol. 2013 5 a021220 10.1101/cshperspect.a021220
Wallace, D. C. & Chalkia, D. Mitochondrial DNA genetics and the heteroplasmy conundrum in evolution and disease. Cold Spring Harbor Perspect. Biol. 5, a021220 (2013).10.1101/cshperspect.a021220
74. Greaves LC Defects in multiple complexes of the respiratory chain are present in ageing human colonic crypts Exp. Gerontol. 2010 45 573 579 10.1016/j.exger.2010.01.013 20096767
Greaves, L. C. et al. Defects in multiple complexes of the respiratory chain are present in ageing human colonic crypts. Exp. Gerontol. 45, 573–579 (2010).20096767 10.1016/j.exger.2010.01.013
75. Trifunovic A Larsson NG Mitochondrial dysfunction as a cause of ageing J. Intern. Med. 2008 263 167 178 10.1111/j.1365-2796.2007.01905.x 18226094
Trifunovic, A. & Larsson, N. G. Mitochondrial dysfunction as a cause of ageing. J. Intern. Med. 263, 167–178. 10.1111/j.1365-2796.2007.01905.x (2008).18226094 10.1111/j.1365-2796.2007.01905.x
76. Taylor RW Turnbull DM Mitochondrial DNA mutations in human disease Nat. Rev. Genet. 2005 6 389 402 10.1038/nrg1606 15861210
Taylor, R. W. & Turnbull, D. M. Mitochondrial DNA mutations in human disease. Nat. Rev. Genet. 6, 389–402 (2005).15861210 10.1038/nrg1606
