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

By Dominik Saul, Robyn Laura Kosinsky, Elizabeth J Atkinson, Madison L Doolittle, Xu Zhang, Nathan LeBrasseur, Robert Pignolo, Paul Robbins, Laura Niedernhofer, Yuji Ikeno, Diana Jurk, Joao Passos, Latonya Hickson, Ailing Xue, David Monroe, Tamara Tchkonia, Jams L. Kirkland, Joshua N Farr, Sundeep Khosla

Posted 11 Dec 2021
bioRxiv DOI: 10.1101/2021.12.10.472095

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.

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