Abstract
As people age, the risk of cardiovascular disease, diabetes, cancer and Alzheimer’s disease increases, making age itself the greatest risk factor many human diseases. Thus, understanding aging can have profound consequences for human health. One striking feature of the aging process is the accumulation of senescent cells with age. When cells become damaged, they can enter into a state of senescence, a permanent cell cycle exit associated with secretion of inflammatory cytokines. In mouse models of aging, the destruction of senescent cells with senolytic drugs delays age-associated decline and extends healthy lifespan. Yet, despite wealth of accumulated knowledge, we do not entirely understand the biology of senescent cells. Prior work has shown that senescence is associated with increased variation in gene expression, suggesting there may be distinct transcriptional signatures of senescence. Understanding the different transcriptional physiological states of senescent cells should allow us to better treat them with cell-type-specific senolytic drugs. Here, we performed a large scale single cell RNA-sequencing time series of experiments to understand the how the transcriptional heterogeneity develops among senescent cell types. Our approach allowed us to observe and classify the different transcriptional signatures of senescent cells as they emerged through time. We found that upon entering oxidative stress-induced senescence, fractions of cells were reproducibly adopting two distinct transcriptional states. One transcriptional state is associated with stress response and the other is associated with tissue remodeling. Our data suggest that combinations of senolytic drugs may more effectively eliminate senescent cells by targeting physiologically distinct sub-populations.
Competing Interest Statement
The authors have declared no competing interest.