Abstract
Cellular senescence, often defined as a state of permanent cell cycle arrest, is a complex and multifaceted process that arises in diverse contexts. First identified as the end point of replicative exhaustion [1], senescence also arises from DNA damage, mitochondrial dysfunction, oxidative stress, sustained mitogenic signaling through oncogenes, proteostatic stress and other. Senescence is under normal physiological conditions involved in wound healing and embryogenesis. Diverse processes trigger multiple mechanisms that converge into cell cycle arrest and a secretory phenotype. Two key pathways may lead to senescence, including the stress-associated p16/Rb pathway and the p53/p21 damage control mechanism. Senescence has been further characterized by its inflammatory secretome (SASP) that serves to signal immune clearance, although it differs by cell type and method of senescence induction. Despite its variable secretome, the SASP may better define senescence since nondividing cells including neurons and cardiomyocytes may exhibit senescent characteristics, despite being frozen at the G0/G1 stage in the cell cycle
Original language | English |
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Journal | Aging |
Volume | 15 |
Issue number | 9 |
Pages (from-to) | 3219-3220 |
Number of pages | 2 |
ISSN | 1945-4589 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Publisher Copyright:© 2023 Heckenbach and Scheibye-Knudsen. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Keywords
- aging
- cellular senescence
- deep learning
- nuclear morphology
- quantitative senescence