Related Experiment Video
Updated: Dec 23, 2025
Imaging- and Flow Cytometry-based Analysis of Cell Position and the Cell Cycle in 3D Melanoma Spheroids
Published on: December 28, 2015
Time-lapse imaging of HeLa spheroids in soft agar culture provides virtual inner proliferative activity
Reiko Minamikawa-Tachino1, Kiyoshi Ogura1, Ayane Ito2
1Translational Medical Research Center, Tokyo Metropolitan Institute of Medical Science, Setagaya, Tokyo, Japan.
Abstract:
Cancer is a complex disease caused by multiple types of interactions. To simplify and normalize the assessment of drug effects, spheroid microenvironments have been utilized. Research models that involve agent measurement with the examination of clonogenic survival by monitoring culture process with image analysis have been developed for spheroid-based screening. Meanwhile, computer simulations using various models have enabled better predictions for phenomena in cancer. However, user-based parameters that are specific to a researcher's own experimental conditions must be inputted. In order to bridge the gap between experimental and simulated conditions, we have developed an in silico analysis method with virtual three-dimensional embodiment computed using the researcher's own samples. The present work focused on HeLa spheroid growth in soft agar culture, with spheroids being modeled in silico based on time-lapse images capturing spheroid growth. The spheroids in silico were optimized by adjusting the growth curves to those obtained from time-lapse images of spheroids and were then assigned virtual inner proliferative activity by using generations assigned to each cellular particle. The ratio and distribution of the virtual inner proliferative activities were confirmed to be similar to the proliferation zone ratio and histochemical profiles of HeLa spheroids, which were also consistent with those identified in an earlier study. We validated that time-lapse images of HeLa spheroids provided virtual inner proliferative activity for spheroids in vitro. The present work has achieved the first step toward an in silico analysis method using computational simulation based on a researcher's own samples, helping to bridge the gap between experiment and simulation.
Insights
Researchers developed a novel in silico method to simulate cancer spheroids using personalized experimental data. This approach bridges the gap between computational modeling and real-world experiments for better drug effect assessment.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Cancer Research
Background:
- Spheroid microenvironments are used to assess drug effects in cancer research.
- Current computational models require user-specific parameters, limiting their direct application to experimental conditions.
- Bridging the gap between experimental and simulated cancer models is crucial for accurate predictions.
Purpose of the Study:
- To develop an in silico analysis method that integrates researcher-specific experimental data for virtual spheroid modeling.
- To create a computational simulation based on a researcher's own samples, enabling personalized cancer research.
- To validate the accuracy of in silico spheroid models against experimental data.
Main Methods:
- Developed an in silico analysis method using virtual three-dimensional embodiment computed from researcher's own samples.
- Modeled HeLa spheroids in silico based on time-lapse images of their growth in soft agar culture.
- Optimized in silico spheroids by adjusting growth curves and assigning virtual inner proliferative activity to cellular particles.
Main Results:
- The ratio and distribution of virtual inner proliferative activities in silico mirrored experimental HeLa spheroid profiles.
- Histochemical profiles of in silico spheroids were consistent with previous studies.
- Validated that time-lapse images of HeLa spheroids can provide virtual inner proliferative activity for in vitro spheroids.
Conclusions:
- Achieved the first step toward an in silico analysis method using computational simulation based on a researcher's own samples.
- The developed method helps bridge the gap between experimental and simulated conditions in cancer research.
- This approach enhances the predictive power of computational models by incorporating personalized experimental data.
