Matrix composition modulates cancer cell phenotypes and secretions in a blood–brain barrier organ-on-a-chip model: Comparison of collagen and hyaluronic acid
Source abstract
Abstract
Recent advances in organ-on-a-chip (OOC) blood-brain barrier (BBB) models for oncology research have revealed key insights into the metastatic cascade. However, in most 3D BBB systems, type I collagen is commonly selected as the brain-side extracellular matrix (ECM) due to its ease of use and affordability. This selection overlooks the native ECM composition of the brain, which is primarily composed of hyaluronan/hyaluronic acid (HyA). Given that the ECM occupies approximately 20% of the brain volume and actively modulates tumor progression, substrate selection is an important yet frequently under-examined component in 3D BBB systems. Using our published blood-brain barrier niche (BBN), we compared HyA and type I collagen matrices to evaluate their impact on cancer cell phenotypes and the tumor microenvironment (TME) to establish which substrate affords the greatest versatility across different lines of investigation. Using a TA DHR-3 rheometer, we examined biophysical variations of these matrices in response to oscillatory shear. We then assessed phenotypic and migratory differences in human breast cancer cells, and their brain-seeking subclones in an ECM of HyA with media versus type I collagen, within our BBN device. Finally, ELISA analysis revealed significant matrix-dependent impact on key chemokine alterations, with collagen matrices promoting CXCL5 and DKK1 secretion across all conditions relative to HyA. We conclude that ECM selection to mimic the patient's brain microenvironment in OOC systems is a critical decision due to inherent substrate differences and either can be appropriate depending on the study goals.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Proteoglycans and glycosaminoglycans research · 3D Printing in Biomedical Research · Cancer Cells and Metastasis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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