Monocytes are biological sensors of aging and frailty in humans

Abstract

Among older adults, frailty is clinically identifiable and characterized by increased vulnerability to adverse health outcomes. Although various tools exist to identify frailty, diagnosis often depends on late-stage clinical symptoms, which typically limit proactive intervention options. We considered whether aging and frailty information may be encoded in the single-cell behaviors and dynamic responses of primary human monocytes. Combining high-content imaging, single-cell behavior profiling, and machine learning, we demonstrated unique age- and frailty-dependent monocyte behaviors at baseline and following exposure to inflammatory stressors. Using these single-cell behaviors, we developed a deep learning neural network model called scTRAIT. scTRAIT accurately predicts the frailty status of older donors, including the capability to track and forecast longitudinal changes in frailty status. Collectively, these findings demonstrate that aging and frailty information are robustly encoded within single-cell behaviors, establishing monocytes as significant biological sensors of aging and frailty in humans. TeaserSingle-cell monocyte responses enable robust prediction of aging and frailty in humans