Implications of time-series gene expression profiles of replicative senescence
Although senescence has long been implicated in aging-associated pathologies, it is not clearly understood how senescent cells are linked to these diseases. To address this knowledge gap, we profiled cellular senescence phenotypes and mRNA expression patterns during replicative senescence in human diploid fibroblasts. We identified a sequential order of gain-of-senescence phenotypes: low levels of reactive oxygen species, cell mass/size increases with delayed cell growth, high levels of reactive oxygen species with increases in senescence-associated β-galactosidase activity (SA-β-gal), and high levels of SA-β-gal activity. Gene expression profiling revealed four distinct modules in which genes were prominently expressed at certain stages of senescence, allowing us to divide the process into four stages: early, middle, advanced, and very advanced. Interestingly, the gene expression modules governing each stage supported the development of the associated senescence phenotypes. Senescence-associated secretory phenotype-related genes also displayed a stage-specific expression pattern with three unique features during senescence: differential expression of interleukin isoforms, differential expression of interleukins and their receptors, and differential expression of matrix metalloproteinases and their inhibitory proteins. We validated these phenomena at the protein level using human diploid fibroblasts and aging Sprague-Dawley rat skin tissues. Finally, disease-association analysis of the modular genes also revealed stage-specific patterns. Taken together, our results reflect a detailed process of cellular senescence and provide diverse genome-wide information of cellular backgrounds for senescence.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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