An Extracellular Matrix Overlay Model for Bioluminescence Microscopy to Measure Single-Cell Heterogeneous Responses to Antiandrogens in Prostate Cancer Cells

  • 0Centre de Recherche du CHU de Québec, Université Laval, Quebec, QC G1V 4G2, Canada.

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Summary

This summary is machine-generated.

This study introduces an extracellular matrix-Matrigel (ECM-M) culture model for better prostate cancer (PCa) cell tracking. The new model improves monitoring of androgen receptor (AR) activity and treatment responses at the single-cell level.

Area Of Science

  • Oncology
  • Cell Biology
  • Biotechnology

Background

  • Prostate cancer (PCa) exhibits significant intra-tumoral heterogeneity.
  • Understanding dynamic cellular responses to treatment is crucial for improving outcomes.
  • Current cell culture methods may limit detailed analysis of single-cell behavior.

Purpose Of The Study

  • To develop and validate an improved cell culture model for prostate cancer.
  • To enable dynamic, single-cell level monitoring of androgen receptor (AR) activity.
  • To investigate heterogeneous responses to antiandrogen therapies in PCa.

Main Methods

  • Introduction of an extracellular matrix-Matrigel (ECM-M) culture system.
  • Utilizing bioluminescence single-cell imaging for cellular tracking.
  • Employing the PSEBC-TSTA biosensor for AR activity assessment.
  • Testing antiandrogen modulation across diverse PCa cell lines.

Main Results

  • The ECM-M model significantly enhanced traceability and viability of PCa cells, especially poorly adherent ones.
  • Robust single-cell tracking and AR activity monitoring were achieved without compromising substrate permeability.
  • Heterogeneous antiandrogen responses were observed at the single-cell level.
  • A correlation was found between non-responsive cell populations and drug IC50 values.

Conclusions

  • The ECM-M culture model is effective for dynamic, single-cell analysis of PCa.
  • This model facilitates precise characterization of androgen receptor inhibitor (ARi) responsiveness.
  • It offers a promising approach to understand and predict heterogeneous treatment responses in cancer.