Date Published: July 11, 2017
Publisher: John Wiley and Sons Inc.
Author(s): Christopher D. Wiley, James M. Flynn, Christapher Morrissey, Ronald Lebofsky, Joe Shuga, Xiao Dong, Marc A. Unger, Jan Vijg, Simon Melov, Judith Campisi.
Senescent cells play important roles in both physiological and pathological processes, including cancer and aging. In all cases, however, senescent cells comprise only a small fraction of tissues. Senescent phenotypes have been studied largely in relatively homogeneous populations of cultured cells. In vivo, senescent cells are generally identified by a small number of markers, but whether and how these markers vary among individual cells is unknown. We therefore utilized a combination of single‐cell isolation and a nanofluidic PCR platform to determine the contributions of individual cells to the overall gene expression profile of senescent human fibroblast populations. Individual senescent cells were surprisingly heterogeneous in their gene expression signatures. This cell‐to‐cell variability resulted in a loss of correlation among the expression of several senescence‐associated genes. Many genes encoding senescence‐associated secretory phenotype (SASP) factors, a major contributor to the effects of senescent cells in vivo, showed marked variability with a subset of highly induced genes accounting for the increases observed at the population level. Inflammatory genes in clustered genomic loci showed a greater correlation with senescence compared to nonclustered loci, suggesting that these genes are coregulated by genomic location. Together, these data offer new insights into how genes are regulated in senescent cells and suggest that single markers are inadequate to identify senescent cells in vivo.
Cellular senescence is a process by which mitotically competent cells permanently arrest proliferation (growth) in response to a variety of physiological signals and pathological stresses (Munoz‐Espin & Serrano, 2014). Because the growth arrest prevents the propagation of stressed or damaged cells, the senescence response is an important tumor‐suppressive mechanism (Campisi, 2013). Further, because senescent cells accumulate with age, they can cause or contribute to several degenerative diseases of aging (Baker et al., 2016). These effects might stem from the fact that senescent cells cannot divide and therefore cannot create new cells to maintain tissue homeostasis. However, as senescent cells generally comprise a minority of cells within even very old tissues (Dimri et al., 1995; Herbig et al., 2006; Kreiling et al., 2011; Waaijer et al., 2012; Baker et al., 2016), it is more likely that senescent cells drive age‐related disease cells nonautonomously. Indeed, senescent cells secrete a myriad of inflammatory cytokines, chemokines, proteases, and growth factors that can have potent effects on tissue microenvironments (Coppe et al., 2008) and thus drive age‐related pathologies by mechanisms that extend beyond the loss of proliferative potential.
As senescent cells are relatively rare, even in tissues from aged animals (Dimri et al., 1995; Herbig et al., 2006; Kreiling et al., 2011; Waaijer et al., 2012; Baker et al., 2016), the ability to identify and study individual senescent cells is a unique opportunity to better understand the range of senescent phenotypes that contribute to their physiological and pathological effects (Munoz‐Espin & Serrano, 2014). In this study, we highlight the ability to identify senescent cells by their gene expression profile, while also demonstrating that the gene expression profiles of senescent cells are highly heterogeneous. Despite this heterogeneity, genes that reside in closely linked loci appear to be coregulated during senescence.
American Federation for Aging Research, (Grant / Award Number: ‘Fellowship’) National Institute on Aging, (Grant / Award Number: ‘AG009909‘,’AG017242‘,’T32‐AG000266‘) National Institute of Arthritis and Musculoskeletal and Skin Diseases, (Grant / Award Number: ‘AR063919‘)
RL, JS, and MU were employed by the Fluidigm Corporation (South San Francisco) at the time C1 analyses were performed, and C1 analyses were performed at Fluidigm.
CW and JF designed the experiments, manually isolated single cells, and analyzed the data under the guidance of JC and SM. CW selected the genes that were analyzed, prepared senescent and quiescent cells, and performed several analyses. JF ran all qPCRs, normalized the data, and performed several analyses. CM performed LDA. XD conducted PCA with guidance from JV. RL and JS performed C1‐based isolation of cells and mRNA under the guidance of MU. CW and JC wrote the article with input from SM and JV.