## 3 Results
Adaptive Sampling Detects Broader Variant Diversity While WGS Reveals Higher-Frequency Hotspots, Together Providing Complementary Perspectives on Mitochondrial Heteroplasmy  
Analyzing mitochondrial DNA heteroplasmy in organ-on-chip (OoC) models and pharmacogenomic studies is crucial for validating their physiological relevance, as these subtle genomic variations can significantly impact cellular energy metabolism, oxidative stress responses, and mitochondrial network dynamics--factors that ultimately determine how accurately these microfluidic systems recapitulate the complex bioenergetic profiles and molecular adaptations observed in native tissue environments. All 15 heteroplasmy sites are listed with their mitochondrial genome positions and sequencing coverage depth. For each site, a reference and an alternative allele are presented with their respective frequencies, along with the heteroplasmy levels (percentage of the alternative allele). Most sites detected using AS show moderate heteroplasmy levels between 5 and 12%. Three adjacent positions (307-309) in the D-loop region show notably higher heteroplasmy levels (11.1-29.6%). Position 308 has the highest heteroplasmy level at 29.6%. Coverage ranges from 20\(\times\) to 56\(\times\) across the heteroplasmy sites (Table 3). these heteroplasmy sites varied considerably, ranging from 35\(\times\) at position 624 to 129\(\times\) at position 13406 (Table 4). Overall, considering the relatively high level of noise in the R9.4.1 flow cells and SQK-LSK109 chemistry, we could still suggest that the detection of identical heteroplasmy sites and similar variant frequencies between whole genome sequencing and adaptive sampling approaches suggests consistent mitochondrial heteroplasmy patterns in this MSC cell line. Nonetheless, the non-uniform coverage possibly contributes also to these frequency differences. The heteroplasmy analysis identifies several key positions in the mtDNA where alternative alleles coexist at significant frequencies. Clustering in AS data identified two distinct mitochondrial populations (Clusters 0 and 1), with Cluster 0 containing the majority of reads. PCA visualization demonstrated clear separation between these clusters based on heteroplasmic variant patterns. AS data (Figure 6) appears to display more heteroplasmic sites, with variants distributed more evenly throughout the mitochondrial genome. The variant frequencies in the AS data generally fall within a lower range (mostly 5-25%), with more heteroplasmic sited identified compared to WGS data. The highest heteroplasmy levels in WGS data (Figure 7) occur near the near the D-Loop control region (~positions 150 and 350), with alternative allele frequencies reaching 40% and 30%, respectively, way higher than the frequences detected using AS. Additional heteroplasmy sites are detected around positions 2000, 13000, and 14000, but at lower frequencies (8-10%). These heteroplasmy patterns suggest active mutational processes or selection pressures at specific mtDNA loci within the cell culture. K-means clustering identified two distinct mitochondrial subpopulations, with Cluster 0 (16 reads) and Cluster 1 (13 reads) showing differential variant profiles across eight key heteroplasmic sites. Principal component analysis confirmed clear separation between these subpopulations, with PC1 explaining 42.6% of the variance. The circular representation illustrated the distribution of heteroplasmic variants across the mitochondrial genome's functional regions, with notable clustering of variants in particular genes. Overall, WGS analysis identifies fewer heteroplasmic sites compared to the alignment sequencing (AS) analysis.