Systems biology provides a structure to elucidate complex biological networks from multi-omic measurements. However, a limitation of systems biology is the ability to create testable hypotheses for further experimentation. Here we present two system-based methods, a coessentiality network and single-cell RNA sequencing, that are aimed to uncover metabolic functionality for uncharacterized genes and mitochondrial DNA mutations.Coessentiality mapping has been useful to systematically cluster genes into biological pathways and identify gene functions (Pan et al., 2018; Wainberg et al., 2019; Wang et al., 2017). Here, using the debiased sparse partial correlation (DSPC) method (Basu et al., 2017), we construct a functional coessentiality map for cellular metabolic processes across human cancer cell lines. This analysis reveals 35 modules associated with known metabolic pathways and further assigns metabolic functions to unknown genes. In particular, we identify C12orf49 as an essential regulator of cholesterol and fatty acid metabolism in mammalian cells. Mechanistically, C12orf49 localizes to the Golgi, binds membrane-bound transcription factor peptidase, site 1 (MBTPS1, site 1 protease) and is necessary for the cleavage of its substrates, including sterol regulatory element binding protein (SREBP) transcription factors. The electron transport chain (ETC) activity in mammalian cells is necessary for survival and proliferation. The ETC is composed of ~100 subunits mostly encoded in the nuclear genome, but 13 essential subunits are in the mitochondrial genome (mtDNA). Accumulation of mutations in the mtDNA can lead to severe genetic defects and cell death. Interestingly, we and other groups have found the occurrence of loss-of-function (LOF) mtDNA mutations across a variety of cancer types at high heteroplasmy. Heteroplasmy is defined as the proportion of mtDNA copies with a specific mutation over the total number of mtDNA copies. Furthermore, there is an enrichment for these LOF mutations suggesting that they are positively selected. Despite their prevalence, it is unclear whether these mutations have functional roles in cancer progression or are simply passenger mutations as the study of mtDNA mutations is stymied by the lack of methods to genetically modify the mtDNA. Here, we have identified a novel method that combines single-cell RNA sequencing (scRNAseq) and fluorescence-activated cell sorting (FACS) in order to create a low and high heteroplasmic mixed population of cells. The high heteroplasmic populations have severe ETC dysfunction and are morphologically, transcriptomically, and metabolomically distinct from the low heteroplasmic cells. These differences result in the high heteroplasmy cells having elevated metastatic potential compared to the low heteroplasmy cells suggesting a role for LOF mtDNA mutations in cancer progression. Altogether, our findings reveal that a combination between single-cell RNAseq and FACS can produce distinct populations that correlate with LOF heteroplasmic mutations.