A New Gene Set Identifies Senescent Cells and Predicts Senescence-Associated Pathways Across Tissues

Abstract

Although cellular senescence is increasingly recognized as driving multiple age-related co-morbidities through the senescence-associated secretory phenotype (SASP), in vivo senescent cell identification, particularly in bulk or single cell RNA-sequencing (scRNA-seq) data remains challenging. Here, we generated a novel gene set (SenMayo) and first validated its enrichment in bone biopsies from two aged human cohorts. SenMayo also identified senescent cells in aged murine brain tissue, demonstrating applicability across tissues and species. For direct validation, we demonstrated significant reductions in SenMayo in bone following genetic clearance of senescent cells in mice, with similar findings in adipose tissue from humans in a pilot study of pharmacological senescent cell clearance. In direct comparisons, SenMayo outperformed all six existing senescence/SASP gene sets in identifying senescent cells across tissues and in demonstrating responses to senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from publicly available human and murine bone marrow/bone scRNA-seq data and identified monocytic and osteolineage cells, respectively, as showing the highest levels of senescence/SASP genes. Using pseudotime and cellular communication patterns, we found senescent hematopoietic and mesenchymal cells communicated with other cells through common pathways, including the Macrophage Migration Inhibitory Factor (MIF) pathway, which has been implicated not only in inflammation but also in immune evasion, an important property of senescent cells. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Moreover, using this senescence panel, we were able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways associated with these cells, which may be particularly useful for evolving efforts to map senescent cells (e.g., SenNet). In addition, SenMayo represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.

mTOR dysregulation induces IL6 and paracrine AT2 cell senescence impeding lung repair in lymphangioleiomyomatosis

“…As senescence is a complex cellular response, which cannot be robustly represented by a single marker we examined the SenMayo senescence gene signature, a validated panel of 125 genes responsive to senescence in a range settings 26 . The SenMayo gene panel was significantly enhanced in LAM derived AT2 cells when compared with normal lung AT2 cells (Figure 3C and supplementary table 2).…”

“…Consistent with this idea, RNA sequencing showed increased activity in cytokine response pathways, consistent with SASP signalling and senescence in addition to injury as shown by increased apoptosis in lung parenchyma. scRNAseq data from LAM lungs showed that the SenMayo gene panel 26 , a set of 125 genes which identify senescent cells in multiple settings was increased in LAM when compared with control AT2 cells. Utilising scRNAseq from a patient treated with rapamycin, we observed that AT2 cell related senescent gene expression could be reduced by mTOR inhibitor therapy, reinforcing the role of mTOR dysregulation in LAM and AT2 cell senescence.…”

Sexually dimorphic activation of innate antitumour immunity prevents adrenocortical carcinoma development

“…Phagocytosis gene sets were curated from an extensive search of the literature, including papers by Park et al 74 , Lecoultre et al 75 and Janda et al 76 and extracted from the MGI gene ontology database. Senescence gene sets were extracted from papers by Eggert et al 32 , Kuilman et al 77 , özcan et al 78 , Acosta et al 79 , Fridman et al 80 , Coppé et al 81,82 , Buhl et al 83 and Saul et al 84 . LM22 and ImmuCC gene sets were derived from gene expression signatures published in Newman et al 85 and Chen et al 86 .…”

Molecular Mechanisms of Vascular Health: Insights From Vascular Aging and Calcification

“…177 Importantly, senescent cells that accrue in aging tissues actively contribute to the inflammatory phenotypes. [180][181][182][183] A gene set containing numerous direct BMP/Wnt modulators (eg, Bmp2, Wnt2 [wingless2], Wnt16, Dkk1 [Dickkopf WNT signaling pathway inhibitor 1], etc) and targets of noncanonical Wnt action was shown to demarcate senescent cells in multiple tissues. 181 However, the conflicting literature on the role of Wnt agonists in promoting or preventing cell senescence suggests that canonical-noncanonical signaling bias and duration of signal exposure deserve additional investigation.…”