Developing transcriptomic signatures as a biomarker of cellular senescence

Area of Science:

• Cellular and Molecular Biology
• Aging Research
• Biomarker Discovery

Background:

• Cellular senescence is a state of irreversible cell cycle arrest triggered by various stressors.
• Senescent cells can develop a senescence-associated secretory phenotype (SASP), contributing to aging and disease.
• Identifying senescent cells in vivo is challenging due to their heterogeneity and lack of universal biomarkers.

Purpose of the Study:

• To review current knowledge on transcriptomic signatures of cellular senescence.
• To explore the potential of these signatures as in vivo biomarkers.
• To understand the genetic pathways driving cellular senescence.

Main Methods:

• Analysis of transcriptomic characteristics of senescent cells.
• Identification of signature gene sets (e.g., CellAge, SeneQuest, SenMayo).
• Application of single-cell and spatial RNA sequencing.
• Development of machine learning algorithms (e.g., SenPred, SenSig, SenCID) for senescence discovery.

Main Results:

• Transcriptomic analysis has identified distinct gene sets associated with cellular senescence.
• Advanced sequencing and machine learning enable the study of senescent cell heterogeneity.
• These approaches facilitate the discovery of novel, quantifiable biomarkers for senescent cells.

Conclusions:

• Transcriptomic signatures represent a promising avenue for identifying senescent cells in vivo.
• Understanding these signatures deepens insights into senescence-driven aging and disease.
• Further development of transcriptomic biomarkers could revolutionize senescence research and therapeutic strategies.