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Updated: Dec 29, 2025
Measuring Single-Cell Mitochondrial DNA Copy Number and Heteroplasmy Using Digital Droplet Polymerase Chain Reaction
Published on: July 12, 2022
Evaluation of mitochondrial DNA copy number estimation techniques
Ryan J Longchamps1, Christina A Castellani1, Stephanie Y Yang1
1Department of Genetic Medicine, McKusick-Nathans Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.
Abstract:
Mitochondrial DNA copy number (mtDNA-CN), a measure of the number of mitochondrial genomes per cell, is a minimally invasive proxy measure for mitochondrial function and has been associated with several aging-related diseases. Although quantitative real-time PCR (qPCR) is the current gold standard method for measuring mtDNA-CN, mtDNA-CN can also be measured from genotyping microarray probe intensities and DNA sequencing read counts. To conduct a comprehensive examination on the performance of these methods, we use known mtDNA-CN correlates (age, sex, white blood cell count, Duffy locus genotype, incident cardiovascular disease) to evaluate mtDNA-CN calculated from qPCR, two microarray platforms, as well as whole genome (WGS) and whole exome sequence (WES) data across 1,085 participants from the Atherosclerosis Risk in Communities (ARIC) study and 3,489 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). We observe mtDNA-CN derived from WGS data is significantly more associated with known correlates compared to all other methods (p < 0.001). Additionally, mtDNA-CN measured from WGS is on average more significantly associated with traits by 5.6 orders of magnitude and has effect size estimates 5.8 times more extreme than the current gold standard of qPCR. We further investigated the role of DNA extraction method on mtDNA-CN estimate reproducibility and found mtDNA-CN estimated from cell lysate is significantly less variable than traditional phenol-chloroform-isoamyl alcohol (p = 5.44x10-4) and silica-based column selection (p = 2.82x10-7). In conclusion, we recommend the field moves towards more accurate methods for mtDNA-CN, as well as re-analyze trait associations as more WGS data becomes available from larger initiatives such as TOPMed.
Insights
Whole genome sequencing (WGS) offers a more accurate measurement of mitochondrial DNA copy number (mtDNA-CN) than qPCR. This advanced method improves the analysis of aging-related diseases and mitochondrial function.
Area of Science:
- Genomics
- Molecular Biology
- Biostatistics
Background:
- Mitochondrial DNA copy number (mtDNA-CN) serves as a key indicator of mitochondrial function and is linked to aging and related diseases.
- Quantitative real-time PCR (qPCR) is the established standard for mtDNA-CN measurement, but alternative methods using microarrays and sequencing exist.
Purpose of the Study:
- To comprehensively evaluate and compare the performance of different methods for measuring mtDNA-CN.
- To assess the association of mtDNA-CN estimates with known biological correlates across various measurement techniques.
Main Methods:
- Utilized data from 1,085 ARIC and 3,489 MESA study participants.
- Calculated mtDNA-CN from qPCR, two microarray platforms, whole exome sequencing (WES), and whole genome sequencing (WGS).
- Evaluated methods based on associations with age, sex, white blood cell count, Duffy genotype, and cardiovascular disease.
Main Results:
- mtDNA-CN derived from WGS showed significantly stronger associations with known correlates compared to qPCR, microarrays, and WES (p < 0.001).
- WGS-derived mtDNA-CN demonstrated greater statistical significance (5.6 orders of magnitude) and more extreme effect sizes (5.8 times) than qPCR.
- DNA extraction from cell lysate yielded more reproducible mtDNA-CN estimates than phenol-chloroform or silica-based methods.
Conclusions:
- Whole genome sequencing is recommended as a superior method for measuring mtDNA-CN due to its enhanced accuracy and association strength.
- The field should transition to more accurate mtDNA-CN measurement techniques and re-evaluate trait associations using WGS data from large-scale initiatives.
