Research Sarah De Beuckeleer

Abstract

Glioblastoma is the predominant form of brain cancer in adults. Due to its aggressive nature, less than 10% of patients survive longer than 5 years post-diagnosis. To date, no treatment is available that can eradicate all tumor cells and avoid relapse. This is in part due to the presence of stem-like glioma cells (GSCs), which can self-renew, invade and communicate with each other and the tumor microenvironment (TME). This heterogeneity and interaction with the TME has been linked to treatment resistance and relapse. We therefore aim to better understand the identity, migration and interaction of these GSCs in a relevant environment. To that end, we will make use of more complex 3D models to better mimic the brain environment and retain heterogeneity within the GSC population to reliably study the GSC invasion patterns. Cerebral organoids provide a valuable avenue for this purpose as they provide a scaffold for diffuse tumor invasion that resembles GSC migration in vivo. We will use the 3D assembloid invasion model alongside in-flow light-sheet imaging to observe infiltration in a panel of patient-derived GSCs into isogenic organoids in a scalable manner.

Researcher(s)

• Promoter: De Beuckeleer Sarah

Research team(s)

• Laboratory of cell biology and histology

Project type(s)

• Research Project

Abstract

The cerebral organoid is an emerging model system with high potential for both fundamental and applied neuroscience research. However, batch-to-batch variability and the inability to characterize these specimens at the cellular level with high throughput, hampers their integration in an industrial setting. With this project, we intend to develop a pipeline that enables unbiased cellular phenotyping of intact cerebral organoids by using a combination of multiplex fluorescent labelling, light-sheet microscopy, and deep learning. Our approach builds on the concept that cells can be accurately identified by means of sheer morphological information. First, we will perfection cell profiling in co-cultures of different brain cell lines by training machine learning-based classifiers. Then, we will translate the concept to 3D, using spheroids from the same cells. To this end, we will render the staining compatible with chemical clearing and conceive a sample mounting procedure for serial, isotropic image acquisition. Finally, we will deploy the optimized method to recognize cell type and state in iPSC-derived cerebral organoids that have been challenged with selected compounds or seeded with glioblastoma cells. The approach will add to a more standardized quality-control of organoids and will facilitate the adoption of this model in drug-screening pipelines. Ultimately, this will boost the translatability of preclinical research and lower attrition rates in late-stage clinical trials.

Researcher(s)

• Promoter: De Vos Winnok
• Co-promoter: Ponsaerts Peter
• Fellow: De Beuckeleer Sarah

Research team(s)

• Laboratory of cell biology and histology

Project type(s)

• Research Project