Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts

Cellular senescence, a hallmark of aging, reveals context-dependent phenotypes across multiple biological length scales. Despite its mechanistic importance, identifying and characterizing senescence across cell populations is challenging. Using primary dermal fibroblasts, we combined single-cell imaging, machine learning, several induced senescence conditions, and multiple protein biomarkers to define functional senescence subtypes. Single-cell morphology analysis revealed eleven distinct morphology clusters. Among these, we identified three as bona-fide senescence subtypes (C7, C10, C11), with C10 exhibiting the strongest age-dependence within an aging cohort. Additionally, we observed that a donor’s senescence burden and subtype-composition were indicative of susceptibility to doxorubicin-induced senescence. Functional analysis revealed subtype-dependent responses to senotherapies, with C7 being most responsive to Dasatinib + Quercetin. Our single-cell analysis framework, SenSCOUT, enables robust identification and classification of senescence subtypes, offering applications in next-generation senotherapy screens, with potential toward explaining heterogeneous senescence phenotypes based on the presence of senescence subtypes.