Deciphering Cellular Complexity of Brain Tumoroid by High Definition MEA Chip Biosensing for Cell-Targeted Drug Discovery and Personalized Medicine
PhD - Leuven | More than two weeks ago
Brain tumors are among the most malignant tumors of human with a 5-year survival rate of only ~20%. This devastating disease imposes a serious health and social issue. The most common and malignant human primary brain tumor is Glioblastoma multiforme (GBM), which is still largely incurable with a ~90% mortality rate and a mean survival rate of 12-15 months. Current clinical intervention of brain tumors is by surgical removal of the malignant tissue which often relapses, and drug administration is one of the most effective treatments for brain tumors which requires high throughput drug screening methods. Nonetheless, the development of effective drugs for brain tumors is complicated by five main challenges: (1) inefficient crossing of drugs through the blood-brain barrier; (2) large genetic diversity; (3) high inter-patient and intratumoral heterogeneity; (4) > 90 subtypes of central nervous system (CNS) malignancies; and (5) complex cellular composition and tumor microenvironment. Therefore, the development of an innovative biosensing platform with single cell precision and high throughput drug screening capability will allow gaining deeper understanding of the cellular complexity of brain tumors and facilitating drug discovery.
This PhD project aims at exploring the potential of IMEC’s MEA chip technology to decipher the cellular complexity of brain tumoroids by characterizing the electrophysiology dynamics of the different GBM-composing cell types at single cell resolution upon drug perturbations. These cell type-specific electrophysiology characteristics will be translated into real-time non-invasive biosensing platform for high throughput on-chip screening of cell type-targeted therapeutics. The PhD candidate will be trained on the application of existing IMEC’s MEA chip platform (with adaptations where necessary), the fabrication of microfluidic-based drug gradient generator and incorporation into the MEA chip platform, as well as 2D and 3D cell culture of mainly GBM patient-derived cell lines and the subsequent molecular biology analytical techniques, including immunostaining, confocal microscopy, RNA sequencing and in silico bioinformatic analysis.
Required background: Bioengineering, Biomedical Sciences, Biomedical engineering
Type of work: 30% development, 10% modeling/simulation, 60% experimental
Supervisor: Liesbet Lagae
Co-supervisor: Dries Braeken
Daily advisor: Yoke Chin Chai
The reference code for this position is 2023-155. Mention this reference code on your application form.