Testing the utility of miBioAge as a personalized aging biomarker

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PROJECT SUMMARY Aging is highly individual phenomenon proceeding at different speed in chronologically identical people. These differences kindle the notion of biological age for which candidate biomarkers include serum analytes and frailty indices and more recently DNA methylation. Longitudinal profiling in humans revealed that organs and tissues age with different speeds resulting in highly individual ageotype. Further, measurements at the single cell resolution significantly improve the insights into human aging process. These and other studies underscore the need for predictive biomarkers of aging at the molecular level preferably with single cell resolution to unravel the complexity of organismal aging and to provide tissue-specific quantitative signatures of functional age. To be informative such molecular signatures must be anchored in the functional readouts of aging such as metabolic, physical, cognitive, and immune functions preferably at the level of individual organisms. The Terskikh laboratory has developed a novel technique rooted in the analysis of epigenome topography at the single cell level to quantitate changes in chromatin landscape. We capture patterns of nuclear staining of epigenetic marks (e.g. acetylated and methylated histones) and employ automated microscopy and machine learning to determine multiparametric signature of cellular state. Application of this technique to aging, termed microscopic imaging of Biological Age (miBioAge), revealed robust separation of young and old freshly isolated mouse and human tissues, and correlated with chronological age without linear regression. A recently funded clinical trial (U01 AG07694) will determine whether the mTOR inhibitor everolimus safely promotes healthspan in humans. We propose to take advantage of a unique set of human samples originating from U01 AG076941 clinical trial, to associate (using hyperbolic embedding and machine learning) individual miBioAge signatures in PBMC and skeletal muscles with multiple functional readouts.