Previous studies have revealed the existence of mitochondrial DNA (mtDNA) mutations in single cells and cell-free biofluid. However, currently there are few cost-effective approaches to assessing mtDNA mutations in these biological samples, and the functional implications of these mutations have not been well understood. Chapter 2 investigates single-cell mtDNA mutations in aging. We developed a cost-effective mtDNA targeted-sequencing protocol called scSTAMP. We applied our method to assess single-cell mtDNA mutations in B lymphocytes and monocytes from a 76-year-old female. Our results indicated that over 50% of cells carried at least one mtDNA mutation with variant allele frequencies (VAFs) over 20%. Surprisingly, over 60% of the mutations were in protein-coding genes, of which over 70% were nonsynonymous, and more than 50% of the nonsynonymous mutations were predicted to be highly pathogenic. Interestingly, about 80% of the observed mutations were singletons in the respective cell populations. Our results revealed that mtDNA mutations with functional significance might be prevalent at advanced age, calling for further investigation on age-related mtDNA mutation dynamics at the single-cell level. Chapter 3 investigates circulating cell-free mtDNA (ccf-mtDNA) mutations in sepsis. We developed a novel approach for targeted enrichment of ccf-mtDNA. Our results suggested that the captured DNA molecules were mostly from mtDNA, with less than 5% of interference originating from nuclear-mitochondrial sequences (NUMTs). We also observed different patterns of mtDNA heteroplasmic variants across cell-free plasma, urine, and peripheral blood mononuclear cells (PBMCs) samples from the same individual. Overall, our approach enables noninvasive detection of mtDNA heteroplasmy in various types of cell-free samples, which could benefit larger-scale detection of mitochondrial functional degradation and disease diagnosis. Chapter 4 investigates the association between nuclear epigenome and mtDNA quality and quantity using single-cell ATAC-seq (scATAC-seq). By leveraging bivariate regression, we identified a novel chromatin region that was missed by univariate models. In addition, by leveraging random forests to implicitly model interactions between the regions, we identified another chromatin region that was missed by linear models. Our results revealed the potential of scATAC-seq in massive screening of associations between the two genomes, and suggested the importance of multivariate models and variable interactions in association studies.