PROJECT SUMMARY Although typical scRNA-seq or scATAC-seq data contain 5-25% of reads that map to the mitochondrial DNA (mtDNA) genome, such mtDNA mapped reads are often filtered out or ignored during downstream analysis. The mitochondria generate over 90% of the cellular energy and are central to health and disease. mtDNA mutations directly cause mitochondrial disease. In addition, random somatic mtDNA mutations accumulate with age and are associated with a broad range of aging-related diseases such as immune disorders, cardiovascular disease and neurodegeneration. However, little is understood about whether accumulation of specific mtDNA variants during aging occurs at the same rate across different cell types, organs, age, gender and race. Such insights would substantially improve our understanding of how mtDNA mutations contribute to various human diseases. Accordingly, significant gaps of knowledge in the single-cell biology field include the lack of robust mtDNA analysis tools and how to utilize the rich mtDNA information often neglected in the ever-increasing datasets to obtain new biological insights. We propose to address these knowledge gaps by: (1) develop robust mtDNA analysis workflows and tools integrated with the HuBMAP Portal; (2) systemically analyze mtDNA from single- cell datasets generated by HuBMAP and other resources; (3) determine whether and how prevalent human mtDNA variants impact mitochondrial and cellular function.

PROJECT NARRATIVE Optimal mitochondrial function is central to human health and disease. We propose to develop tools to study the rich mitochondrial DNA (mtDNA) information in HuBMAP and other single-cell datasets. We will obtain new biological insights into how cell/organ type, aging, gender, race, and other factors impact mtDNA integrity and how mtDNA variants affect cellular gene expression and function.