Biosensor-Enhanced Organ-on-a-Chip Models for Investigating Glioblastoma Tumor Microenvironment Dynamics

Author:

Thenuwara Gayathree12ORCID,Javed Bilal13,Singh Baljit4ORCID,Tian Furong13ORCID

Affiliation:

1. School of Food Science and Environmental Health, Technological University Dublin, Grangegorman Lower, D07 H6K8 Dublin, Ireland

2. Institute of Biochemistry, Molecular Biology, and Biotechnology, University of Colombo, Colombo 00300, Sri Lanka

3. Nanolab Research Centre, FOCAS Research Institute, Technological University Dublin, Camden Row, D08 CKP1 Dublin, Ireland

4. MiCRA Biodiagnostics Technology Gateway, Technological University Dublin (TU Dublin), D24 FKT9 Dublin, Ireland

Abstract

Glioblastoma, an aggressive primary brain tumor, poses a significant challenge owing to its dynamic and intricate tumor microenvironment. This review investigates the innovative integration of biosensor-enhanced organ-on-a-chip (OOC) models as a novel strategy for an in-depth exploration of glioblastoma tumor microenvironment dynamics. In recent years, the transformative approach of incorporating biosensors into OOC platforms has enabled real-time monitoring and analysis of cellular behaviors within a controlled microenvironment. Conventional in vitro and in vivo models exhibit inherent limitations in accurately replicating the complex nature of glioblastoma progression. This review addresses the existing research gap by pioneering the integration of biosensor-enhanced OOC models, providing a comprehensive platform for investigating glioblastoma tumor microenvironment dynamics. The applications of this combined approach in studying glioblastoma dynamics are critically scrutinized, emphasizing its potential to bridge the gap between simplistic models and the intricate in vivo conditions. Furthermore, the article discusses the implications of biosensor-enhanced OOC models in elucidating the dynamic features of the tumor microenvironment, encompassing cell migration, proliferation, and interactions. By furnishing real-time insights, these models significantly contribute to unraveling the complex biology of glioblastoma, thereby influencing the development of more accurate diagnostic and therapeutic strategies.

Publisher

MDPI AG

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