Abstract

Mitochondria generate over 90% of cellular energy and are central to managing health and disease. Typical scRNA-seq or scATAC-seq data contain 5-25% of reads that map to the mitochondrial DNA (mtDNA) genome, but such mtDNA mapped reads are often filtered out or ignored during downstream analysis. 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. 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. Moreover, significant gaps of knowledge in single-cell biology lack robust mtDNA analysis tools and methods for collecting the rich mtDNA information that is often neglected in the ever-increasing datasets. We propose to address these knowledge gaps by: (1) developing robust mtDNA analysis workflows and tools integrated into the HuBMAP Portal; (2) systematically analyzing mtDNA from single-cell datasets generated by HuBMAP and other resources; (3) determining whether and to what extent human mtDNA variants impact mitochondrial and cellular function. 

Public health relevance statement

Optimal mitochondrial function is central to managing 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 type, organ type, aging, gender, race, and other factors impact mtDNA integrity and how mtDNA variants affect cellular gene expression and function.