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Updated: Aug 6, 2026

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells
08:56

Simultaneous Imaging and Flow-Cytometry-based Detection of Multiple Fluorescent Senescence Markers in Therapy-Induced Senescent Cancer Cells

Published on: July 12, 2022

From binary senescence to state-resolved senotherapy in cancer: Therapeutic windows from heterogeneity

Ziyu Xu1, Jiamin Guo1, Zheran Liu1

  • 1Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China; Sichuan Provincial Key Laboratory of Nuclear Physics and Medical Research, Sichuan University, Chengdu, Sichuan, China.

Ageing Research Reviews
|July 23, 2026
PubMed

Insights

Tumor senescence, a cell-cycle arrest, can either hinder or promote cancer. Understanding its diverse states is key to developing targeted senotherapies for better cancer treatment.

Area of Science:

  • Oncology
  • Cell Biology
  • Cancer Therapeutics

Background:

  • Tumor senescence is a cell-cycle arrest crucial for cancer suppression.
  • However, senescence-associated secretory programs can paradoxically drive tumor progression, immune evasion, and treatment resistance.
  • The heterogeneity of these secretory programs across different contexts necessitates advanced detection methods.

Purpose of the Study:

  • To review the molecular drivers of heterogeneity in tumor senescence.
  • To propose an operational state space for understanding senescence dynamics.
  • To explore AI-driven strategies for precision senotherapy development.

Main Methods:

  • Synthesis of molecular determinants of senescence heterogeneity.
  • Consolidation into an operational state space model.
  • Evaluation of AI, single-cell, spatial profiling, and imaging for senescence mapping.

Main Results:

  • Senescence heterogeneity is functionally, temporally, and spatially complex.
  • Binary detection of senescent cells is insufficient for therapeutic decisions.
  • An operational state space can guide phase-aware intervention logic.

Conclusions:

  • Precision senotherapies require a nuanced understanding of senescence states.
  • AI and multi-modal profiling can enable state-resolved mapping and stratification.
  • Staged precision senotherapies hold promise for improved cancer treatment outcomes.
Keywords:
Artificial intelligenceSenescence-associated secretory phenotype (SASP)SenotherapySpatiotemporal heterogeneityTumor senescence

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