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A workflow for quantifying cell quiescence in 3D spheroids
Maria Rita Assenza1,2, Federica Barbagallo3, Fabiola Moretti2
1Innate Lymphoid Cells Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, 00146, Italy.
Biochemistry and Biophysics Reports
|February 9, 2026
Summary
Researchers developed a new method to detect dormant cancer cells using acridine orange staining. This technique identifies quiescent cells in tumors, which are linked to therapy resistance and relapse, aiding in personalized cancer treatment.
Area of Science:
- Cell Biology
- Cancer Research
- Metabolic Profiling
Background:
- Quiescence is a reversible cell cycle exit crucial in solid tumors, contributing to therapy resistance and relapse.
- Quiescent cancer cells are often found in poorly vascularized tumor regions.
- Identifying these dormant cells is vital for predicting treatment outcomes and personalizing therapies.
Purpose of the Study:
- To develop and validate a fluorescence-based method for assessing individual cell metabolic states.
- To distinguish quiescent cancer cells from proliferating cells in 3D tumor models.
- To establish a reliable readout for cell dormancy and drug resistance.
Main Methods:
- Utilized acridine orange (AO) staining to measure AO-RNA and AO-DNA signals in individual cells.
- Employed algorithm-driven quantification for precise measurement of fluorescence signals.
- Applied the method to 3D non-small cell lung cancer spheroids to analyze cell populations.
Main Results:
- Successfully distinguished metabolic differences between cell states within 3D spheroids.
- Demonstrated that the RNA/DNA staining ratio serves as a reliable indicator of transcriptional activity.
- Validated the AO staining method as an effective tool for identifying quiescent cells.
Conclusions:
- The developed AO staining method reliably identifies quiescent cancer cells based on metabolic state.
- This technique offers a powerful approach to detect drug-resistant, dormant cancer cells in 3D cultures.
- Findings support the potential of this method for predicting therapeutic outcomes and guiding personalized cancer treatment strategies.
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