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Introduction Cellular senescence, defined by an irreversible cell cycle arrest in response to potentially oncogenic stimuli, has been described as a protective mechanism in tumourigenesis and a therapeutic target in cancer. The senescence-associated secretory phenotype (SASP) is a pro-inflammatory response by senescent cells involving the release of cytokines, chemokines, growth factors and proteases that, in a cancer progression context, may be beneficial by the elimination of senescent cells or deleterious when triggering angiogenesis, cell proliferation and epithelial-to-mesenchymal transition. Despite senescence’s importance in cancer and the suggested role of alternative splicing in its regulation, the transcriptional heterogeneity of senescent cells has, to our knowledge, been extensively characterised only at the gene expression level. Material and methods Next-generation sequencing of RNA (RNA-seq) allows alternative splicing quantification with unprecedented precision. The inclusion level of an exon is commonly quantified by its percent-spliced-in (PSI) value, i.e. the proportion of RNA-seq reads providing evidence supporting its inclusion. However, a PSI ratio does not incorporate information about the number of reads used in the quantification of the cognate alternative splicing event, directly proportional to the precision of its estimate. Beta distributions can be exploited in modelling inclusion levels, using reads supporting exon inclusion and exclusion as surrogates of the distribution’s shape parameters. We employed a computational pipeline, based on fitted beta distributions, to accurately quantify and compare alternative splicing across different types of senescent cells, relying both on public and in-house RNA-seq datasets. Results and discussions Our analyses reproducibly identified, at a transcriptome-wide level, the alternative splicing changes specifically related with replicative and different types of induced senescence in multiple types of cells. For instance, Ras-induced senescence appears to associate with alterations in the splicing of genes involved in the secretory pathway and intracellular trafficking. Conclusion Differential splicing analyses based on beta distribution modelling contribute to elucidate the specific alternative splicing signatures of different types of senescent cells, providing insights for targeting senescence in cancer therapies. |
