Precise multiphase hydrogel engineering of miniaturized 3D cancer architectures via computationally informed microfluidics
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CellPress
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Understanding cancer biology and responses to new therapies requires accurate in vitro models that mimic the complexity of tumors. This study introduces a multiphase microfluidic biofabrication platform that enables the creation of self-standing 3D tumor configurations within hydrogel microfiber boundaries. Using one single framework, different in vitro models were generated, focusing on the creation of discrete spheroids in size-limited liquid pockets and continuous multicellular fiberoids. These models incorporate essential features of in vivo tumors, including tissue-like solid stress and microenvironment interactions, which contribute to a more physiologically relevant replication of tumor responses. Computational simulations were applied to fine-tune the biofabrication process, predict outcomes, and ensure that the in silico models exhibit the desired characteristics, reducing the time and cost associated with further experimental iterations. In vitro testing demonstrated drug responsiveness in all configurations, underlining the platform’s potential for drug screening, with greatly enhanced manipulation of soft 3D cell constructs. The fiberoid models further emulated intercellular dynamics within the tumor networks, herein explored in the glioblastoma-astrocyte context, expanding the versatility of our technology for cancer research. Ultimately, the scalability and adaptability of this versatile method make it a very promising tool for advancing cancer biology, drug discovery, and precision medicine strategies.
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Matter Volume 8, Issue 10, 1 October 2025, 102196
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https://doi.org/10.1016/j.matt.2025.102196Sponsors
R.R. acknowledges the Program for the requalification, international mobility, and attraction of talent in the Spanish university system, modality Margarita Salas (grant UP2021-042). R.R.C. acknowledges Fundação para a Ciência e Tecnologia (FCT) for support through grant 2022.00764.CEECIND/CP1718/CT0020 (DOI: 10.54499/2022.00764.CEECIND/CP1718/CT0020). CFG acknowledges support from the Fundação para a Ciência e Tecnologia (FCT) through grant 10.54499/2022.05711.CEECIND/CP1718/CT0012. The authors also acknowledge support from the Fundação para a Ciência e Tecnologia (FCT) through grant 10.54499/2022.05737.PTDC
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Attribution-NonCommercial-NoDerivatives 4.0 International








