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.

细胞衰老（Cellular senescence）是衰老的标志性特征，其表型呈现情境依赖性，且跨越多个生物长度尺度。尽管其机制层面的重要性已得到广泛认可，但在细胞群体中识别并表征衰老状态仍颇具挑战。我们以原代皮肤成纤维细胞（primary dermal fibroblasts）为研究对象，结合单细胞成像技术、机器学习方法、多种诱导衰老模型及多类蛋白质生物标志物，对功能性衰老亚型进行了系统定义。单细胞形态学分析共鉴定出11个独立的形态聚类簇。其中，我们确认C7、C10、C11这3个聚类为确凿的衰老亚型，其中C10在衰老队列中展现出最强的年龄相关性。此外，我们发现供体的衰老负荷与亚型组成可作为其对多柔比星（doxorubicin）诱导衰老易感性的预测指标。功能分析结果显示，不同衰老亚型对衰老靶向疗法的响应存在亚型依赖性：C7对达沙替尼（Dasatinib）联合槲皮素（Quercetin）的治疗响应最为显著。我们开发的单细胞分析框架SenSCOUT可实现衰老亚型的高效识别与分类，有望应用于下一代衰老靶向疗法筛选研究，或可基于衰老亚型的存在情况解释异质性衰老表型的成因。