Systematic transcriptomic analysis and temporal modelling of human fibroblast senescence.

Area of Science:

• Cellular and Molecular Biology
• Aging Research
• Immunology

Background:

• Cellular senescence, marked by cell cycle arrest and a secretory phenotype (SASP), typically resolves via immune clearance.
• Age-related immune dysregulation leads to senescent cell accumulation and chronic inflammation.
• Heterogeneity of senescence phenotypes complicates identification and therapeutic targeting (senotherapy).

Purpose of the Study:

• To systematically collect and analyze transcriptomic data to understand senescence heterogeneity.
• To identify reliable biomarkers for different types of senescence.
• To build a framework for more reproducible senescence research.

Main Methods:

• Compiled a database of 119 human fibroblast transcriptomic datasets.
• Analyzed gene expression across four senescence types: DNA damage-induced (DDIS), oncogene-induced (OIS), replicative, and bystander-induced.
• Utilized computational modeling and knockdown interventions to validate transcriptomic findings.

Main Results:

• Identified 28 genes significantly altered across the four senescence types.
• Confirmed that conventional senescence markers (e.g., p16, p21) show specific and reliable expression patterns.
• Observed rapid p16 and p21 mRNA increases (8-11 days) and found limited evidence for an early TGFβ-centric SASP.

Conclusions:

• Universal biomarkers for cellular senescence remain elusive due to phenotype heterogeneity.
• Conventional senescence markers exhibit predictable expression profiles, aiding in senescence type identification.
• A structured framework for senescence data analysis can improve research reproducibility and understanding of senescence complexity.