Grant: CIHR Fellowship

The decline in skeletal muscle mass and strength in old age is a chronic disease defined as sarcopenia, and it is a major determinant of quality of life and independent loss. To date, it is unclear how molecular changes to the cells within the tissue contribute to muscle weakness, and whether some cell populations are particularly vulnerable or why. An important advance in the field of aging research has been the development of machine learning algorithms that can evaluate the aging signature of a biological sample. Recently, our group has developed such a tool that is compatible with single-cell gene expression data (transcriptomics). The goal of this current project is to adapt the framework and learn the signatures associated with age-induced muscle weakness, essentially using transcriptomics data as composite biomarkers. This will subsequently enable the analysis of human sarcopenia at the cellular level. The biomarkers developed here will yield a greater understanding of sarcopenia onset and will be of high therapeutic value when evaluating new or existing interventions for prolonging healthy muscle function in the elderly.