Abstract Cellular senescence is defined as an irreversible growth arrest observed when cells are exposed to a variety of stressors, including DNA damage, oxidative stress, or nutrient deprivation. Although senescence is a well-established driver of aging and age-related diseases, it is a highly heterogeneous process with significant variations across organisms, tissues, and cell types. The relatively low abundance of senescent cells in healthy aged tissues poses a major challenge to the longitudinal study of senescence in specific organs, including the human lung. To overcome this limitation, we developed a positive-unlabeled learning framework to generate a comprehensive list of senescence marker genes in human lungs (termed SenSet) using the largest publicly available single-cell lung dataset, the Human Lung Cell Atlas (HLCA). We validated SenSet in a highly complex ex vivo human 3D lung tissue culture model subjected to the senescence inducers bleomycin, doxorubicin, or irradiation, and established its sensitivity and accuracy in characterizing senescence. Using SenSet, we identified and validated cell-type-specific senescence signatures in distinct lung cell populations upon aging and environmental exposure. Our study provides a comprehensive analysis of senescent cells in the healthy aging lung, presenting fundamental implications for our understanding of major lung diseases, including cancer, fibrosis, chronic obstructive pulmonary disease, or asthma.
Hasanaj et al. (Fri,) studied this question.