Death-seq and ye shall find: A novel screening strategy for dying cells.

Killing senescent cells to improve health-span holds great promise. However, screening for senescence-regulating genes and molecules is challenging because these cells do not proliferate. In this issue, Colville and Liu et al. develop Death-seq, a positive selection screening tool that overcomes this hurdle to offer broad genetic and pharmacological utility.

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.

Senescence-associated morphological profiles (SAMPs): an image-based phenotypic profiling method for evaluating the inter and intra model heterogeneity of senescence.

Senescence occurs in response to a number of damaging stimuli to limit oncogenic transformation and cancer development. As no single, universal senescence marker has been discovered, the confident classification of senescence induction requires the parallel assessment of a series of hallmarks. Therefore, there is a growing need for "first-pass" tools of senescence identification to streamline experimental workflows and complement conventional markers. Here, we utilise a high content, multidimensional phenotypic profiling-based approach, to assess the morphological profiles of senescent cells induced via a range of stimuli. In the context of senescence, we refer to these as senescence-associated morphological profiles (SAMPs), as they facilitate distinction between senescent and proliferating cells. The complexity of the profiles generated also allows exploration of the heterogeneity both between models of senescence and within an individual senescence model, providing a level of insight at the single cell level. Furthermore, we also demonstrate that these models are applicable to the assessment of senescence in vivo, which remains a key challenge for the field. Therefore, we believe SAMPs has the potential to serve as a useful addition in the repertoire of senescence researchers, either as a first-pass tool or as part of the established senescence hallmarks.

Isolation methodology is essential to the evaluation of the extracellular vesicle component of the senescence-associated secretory phenotype.

A hallmark of senescence is the acquisition of an enhanced secretome comprising inflammatory mediators and tissue remodelling agents - the senescence-associated secretory phenotype (SASP). Through the SASP, senescent cells are hypothesised to contribute to both ageing and pathologies associated with age. Whilst soluble factors have been the most widely investigated components of the SASP, there is growing evidence that small extracellular vesicles (EVs) comprise functionally important constituents. Thus, dissecting the contribution of the soluble SASP from the vesicular component is crucial to elucidating the functional significance of senescent cell derived EVs. Here, we take advantage of a systematic proteomics based approach to determine that soluble SASP factors co-isolate with EVs following differential ultracentrifugation (dUC). We present size-exclusion chromatography (SEC) as a method for separation of the soluble and vesicular components of the senescent secretome and thus EV purification. Furthermore, we demonstrate that SEC EVs isolated from senescent cells contribute to non-cell autonomous paracrine senescence. Therefore, this work emphasises the requirement for methodological rigor due to the propensity of SASP components to co-isolate during dUC and provides a framework for future investigations of the vesicular component of the SASP.

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.

The bright and dark side of extracellular vesicles in the senescence-associated secretory phenotype.

Senescence is a state of proliferative arrest which has been described as a protective mechanism against the malignant transformation of cells. However, senescent cells have also been demonstrated to accumulate with age and to contribute to a variety of age-related pathologies. These pathological effects have been attributed to the acquisition of an enhanced secretory profile geared towards inflammatory molecules and tissue remodelling agents - known as the senescence-associated secretory phenotype (SASP). Whilst the SASP has long been considered to be comprised predominantly of soluble mediators, growing evidence has recently emerged for the role of extracellular vesicles (EVs) as key players within the secretome of senescent cells. This review is intended to consolidate recent evidence for the roles of senescent cell-derived EVs to both the beneficial (Bright) and detrimental (Dark) effects of the SASP.

The relative contributions of psychiatric symptoms and psychotropic medications on the sleep-wake profile of young persons with anxiety, depression and bipolar disorders.

This study investigated the relative contribution of psychiatric symptoms and psychotropic medications on the sleep-wake cycle. Actigraphy and clinical assessments (Brief Psychiatric Rating Scale) were conducted in 146 youths with anxiety, depression or bipolar disorders. Independently of medications, mania symptoms were predictive of lower circadian amplitude and rhythmicity. Independently of diagnosis and symptoms severity: i) antipsychotics were related to longer sleep period and duration, ii) serotonin-norepinephrine reuptake inhibitors to longer sleep period, and iii) agomelatine to earlier sleep onset. Manic symptoms and different subclasses of medications may have independent influences on the sleep-wake cycle of young people with mental disorders.