Glioblastoma is an aggressive form of brain cancer characterized by diffuse infiltration into healthy neural architecture.
Traditional imaging techniques often capture only small regions of the brain or static snapshots, obscuring the dynamic mechanisms of tumor migration. GlioTrace overcomes this limitation by utilizing standard microscopes paired with a specialized computational analysis system.
To build the tracking dataset, scientists obtained glioblastoma samples from six patients and grew them as tumors in the brains of mice, according to UA.News. Researchers then cut the mouse brains into thin slices and kept the tissues alive under laboratory conditions.
An automated microscope photographed these tissue slices at intervals of no more than two hours over a period of up to five days. A machine-learning system subsequently identified and tracked individual tumor cells, while statistical models evaluated how the cells transition between different states, UA.News noted. This automated pipeline processes microscopy images, identifies distinct cell shapes, and estimates morphological changes over time.
The tracking data revealed distinct invasion patterns across different patient-derived tumors. UA.News reported that some tumors spread primarily along blood vessels, whereas others dispersed more diffusely through the surrounding brain tissue. Individual cells demonstrated the capacity to switch their movement mode over several days, shifting from moving along blood vessels to moving independently through brain tissue.
Using GlioTrace, the research team also tested the effects of two medicinal substances on tumor cell movement within the brain slices. Both substances reduced cell mobility and spread, though the exact effects varied depending on the patient-derived tumor and the specific type of migrating cells.
Additional experiments involving a combination of radiation therapy and chemotherapy showed an increased proportion of cells with a specific shape in certain tumor models. These particular cells moved the fastest and occupied a central position in the network of transitions between cell states, according to findings from Sven Nelander.
“Using this method allows us to monitor, for the first time, how thousands of individual tumour cells move through living brain tissue and to see how they adapt their behaviour in real time. It opens up new possibilities to understand and, in the long term, halt the invasion process,” said Sven Nelander, Professor of Integrative Cancer Research at Uppsala University.
While the findings raise the possibility that invasive cell subpopulations may persist during conventional treatment, further research is required to determine whether these cells exhibit higher treatment resistance or directly contribute to tumor recurrence. The current experiments utilized animal tissue samples rather than human patients.
The analysis software developed for GlioTrace has been made freely available to other research groups. However, the developers emphasize that the technology remains strictly a research method at this stage and cannot yet guide clinical treatment decisions.
“We hope that the method will help us understand how tumour cells interact with their surroundings and identify treatments that can prevent cancer from spreading through the brain. However, for now, GlioTrace is a research method, and further studies are needed before it could be used to guide treatment decisions in patients,” said Hitesh Mangukiya.
