Impact of measuring heteroplasmy of a pathogenic mitochondrial DNA variant at the single-cell level in individuals with mitochondrial disease

Pathogenic mitochondrial DNA heteroplasmy has mainly been assessed with bulk sequencing in individuals with mitochondrial disease. However, the distribution of heteroplasmy at the single-cell level in skin fibroblasts obtained from individuals, together with detailed clinical and biochemical information, remains to be investigated. We used the mitochondrial DNA single-cell assay for the transposase-accessible chromatin sequencing method. Skin fibroblasts were obtained from six individuals with mitochondrial disease and pathogenic m.3243A>G variants of differing severity. Different distributions of heteroplasmy at the single-cell level were identified in skin fibroblasts from all six individuals. Four individuals with different outcomes showed similar averaged heteroplasmy rates with normal mitochondrial respiratory chain enzyme activity, while the distribution of single-cell heteroplasmy patterns differed among the individuals. This study showed different heteroplasmy distribution patterns at the single-cell level in individuals with the m.3243A>G variant, who had a similar averaged heteroplasmy rates with normal mitochondrial respiratory chain enzyme activity. Whether such different heteroplasmy distribution patterns explain the different clinical outcomes should be assessed further in future studies. Measuring heteroplasmy of pathogenic mitochondrial DNA variants at the single-cell level could be important in individuals with mitochondrial disease.

Automated summary

This study investigated the distribution of pathogenic mitochondrial DNA heteroplasmy at the single-cell level in skin fibroblasts from individuals with the m.3243A>G variant. Different distribution patterns were found among individuals with similar averaged heteroplasmy rates and normal mitochondrial respiratory chain enzyme activity. Further studies are needed to determine if these different distribution patterns explain the different clinical outcomes.