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Updated: Jul 9, 2025
Co-culture of Glioblastoma Stem-like Cells on Patterned Neurons to Study Migration and Cellular Interactions
Published on: February 24, 2021
Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma
Anna Giczewska1, Krzysztof Pastuszak1,2,3, Megan Houweling4,5,6
1Laboratory of Translational Oncology, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Gdańsk, Poland.
Background:
In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays.
Methods:
We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15).
Results:
Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time.
Conclusions:
Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.
Insights
This study introduces a novel method using machine learning and neurosphere imaging to monitor drug interactions over 18 days, revealing persistent synergistic effects for glioblastoma drug combinations.
Area of Science:
- Oncology
- Pharmacology
- Biotechnology
Background:
- Drug combinations are crucial for treating glioblastoma, a challenging brain cancer.
- Assessing drug interactions over time is vital for predicting efficacy and preventing resistance.
- Current methods for longitudinal drug interaction monitoring are limited.
Purpose of the Study:
- To develop and validate a novel method for massive parallel monitoring of drug interactions in glioblastoma models.
- To assess the temporal dynamics of drug interactions in 3D neurospheres over an 18-day period.
- To identify synergistic drug combinations with persistent effects against glioblastoma.
Main Methods:
- A method was developed for monitoring 16 drug combinations in 3 glioblastoma models over 18 days.
- Neurosphere viabilities were estimated using image information at multiple time points (days 8, 11, 15, and 18).
- Machine learning was employed to decode image data into viability values, correlating with CellTiter-Glo 3D measurements.
Main Results:
- The developed method successfully predicted cell viability from neurosphere images, enabling longitudinal assessment.
- Drug interactions were monitored over an 18-day time window.
- Several drug combinations demonstrated clear and persistent synergistic interactions over time.
Conclusions:
- The novel method facilitates longitudinal drug-interaction assessment in 3D neurospheres.
- This approach provides new insights into the temporal effects of drug combinations.
- The findings can aid in identifying more effective glioblastoma therapies.
