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Updated: Jan 15, 2026

Techniques to Induce and Quantify Cellular Senescence
Published on: May 1, 2017
The interactome era: Integrating RNA-seq, proteomics, and network biology to decode cellular senescence
Mohd Shahzaib1, Domenico Aprile1, Tiziana Squillaro2
1Department of Experimental Medicine, Biotechnology and Molecular Biology Section, Luigi Vanvitelli Campania University, Naples 80138, Italy.
Abstract:
Cellular senescence is a dynamic state in which cells permanently withdraw from the cell cycle while continuing to reshape their internal and external environment. It is characterized by persistent DNA damage responses, chromatin reorganization, and the secretion of a complex mixture of cytokines and proteases collectively known as the senescence-associated secretory phenotype (SASP). Transcriptomic and proteomic studies have defined key markers, including CDKN2A, CDKN1A, TP53, and SASP factors, but these approaches provide only static inventories. They do not explain how regulatory programs are executed through protein interactions that assemble, dissolve, and reorganize over time. Interactomics now fills this gap. Advances such as affinity purification mass spectrometry (AP-MS), proximity labeling (BioID/TurboID), and cross-linking mass spectrometry (XL-MS) reveal that senescence is driven not by single molecules but by the rewiring of protein-protein interactions (PPIs). These dynamic networks stabilize DNA damage response hubs, restructure chromatin and the nuclear lamina, regulate SASP secretion, and remodel metabolism. By integrating interactomic data with transcriptomic and proteomic profiles, it is now possible to uncover therapeutic vulnerabilities and guide the design of senolytics, senomorphics, and strategies that block senescence escape. Important challenges remain. Weak or transient interactions are often lost, background signals can obscure specificity, and membrane complexes are under-represented. Emerging single-cell and spatial technologies are beginning to overcome these limitations, revealing how senescence differs across tissues, contexts, and disease states. In essence, senescence is not just a change in gene expression but a reorganization of the cell's communication networks. Interactomics offers the framework needed to decode this complexity and to design precision therapies for aging and age-related disease.
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