Yearning for machine learning: applications for the classification and characterisation of senescence.

Senescence is a widely appreciated tumour suppressive mechanism, which acts as a barrier to cancer development by arresting cell cycle progression in response to harmful stimuli. However, senescent cell accumulation becomes deleterious in aging and contributes to a wide range of age-related pathologies. Furthermore, senescence has beneficial roles and is associated with a growing list of normal physiological processes including wound healing and embryonic development. Therefore, the biological role of senescent cells has become increasingly nuanced and complex. The emergence of sophisticated, next-generation profiling technologies, such as single-cell RNA sequencing, has accelerated our understanding of the heterogeneity of senescence, with distinct final cell states emerging within models as well as between cell types and tissues. In order to explore data sets of increasing size and complexity, the senescence field has begun to employ machine learning (ML) methodologies to probe these intricacies. Most notably, ML has been used to aid the classification of cells as senescent, as well as to characterise the final senescence phenotypes. Here, we provide a background to the principles of ML tasks, as well as some of the most commonly used methodologies from both traditional and deep ML. We focus on the application of these within the context of senescence research, by addressing the utility of ML for the analysis of data from different laboratory technologies (microscopy, transcriptomics, proteomics, methylomics), as well as the potential within senolytic drug discovery. Together, we aim to highlight both the progress and potential for the application of ML within senescence research.

Current Understanding of the Role of Senescent Melanocytes in Skin Ageing.

Melanocytes reside within the basal epidermis of human skin, and function to protect the skin from ultraviolet light through the production of melanin. Prolonged exposure of the skin to UV light can induce irreparable DNA damage and drive cells into senescence, a sustained cell cycle arrest that prevents the propagation of this damage. Senescent cells can also be detrimental and contribute to skin ageing phenotypes through their senescence-associated secretory phenotype. Senescent cells can act in both an autocrine and paracrine manner to produce widespread tissue inflammation and skin ageing. Recently, melanocytes have been identified as the main senescent cell population within the epidermis and have been linked to a variety of skin ageing phenotypes, such as epidermal thinning and the presence of wrinkles. However, the literature surrounding melanocyte senescence is limited and tends to focus on the role of senescence in the prevention of melanoma. Therefore, this review aims to explore the current understanding of the contribution of senescent melanocytes to human skin ageing.

Early growth response 2 (EGR2) is a novel regulator of the senescence programme.

Senescence, a state of stable growth arrest, plays an important role in ageing and age-related diseases in vivo. Although the INK4/ARF locus is known to be essential for senescence programmes, the key regulators driving p16 and ARF transcription remain largely underexplored. Using siRNA screening for modulators of the p16/pRB and ARF/p53/p21 pathways in deeply senescent human mammary epithelial cells (DS HMECs) and fibroblasts (DS HMFs), we identified EGR2 as a novel regulator of senescence. EGR2 expression is up-regulated during senescence, and its ablation by siRNA in DS HMECs and HMFs transiently reverses the senescent phenotype. We demonstrate that EGR2 activates the ARF and p16 promoters and directly binds to both the ARF and p16 promoters. Loss of EGR2 down-regulates p16 levels and increases the pool of p16- p21- 'reversed' cells in the population. Moreover, EGR2 overexpression is sufficient to induce senescence. Our data suggest that EGR2 is a direct transcriptional activator of the p16/pRB and ARF/p53/p21 pathways in senescence and a novel marker of senescence.