High-throughput microfluidic 3D biomimetic model enabling quantitative description of the human breast tumor microenvironment

Acta Biomaterialia(2021)

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摘要
Cancer is driven by both genetic aberrations in the tumor cells and fundamental changes in the tumor microenvironment (TME). These changes offer potential targets for novel therapeutics, yet lack of in vitro 3D models recapitulating this complex microenvironment impedes such progress. Here, we generated several tumor-stroma scaffolds reflecting the dynamic in vivo breast TME, using a high throughput microfluidic system. Alginate (Alg) or alginate-alginate sulfate (Alg/Alg-S) hydrogels were used as ECM-mimics, enabling the encapsulation and culture of tumor cells, fibroblasts and immune cells (macrophages and T cells, of the innate and adaptive immune systems, respectively). Specifically, Alg/Alg-S was shown capable of capturing and presenting growth factors and cytokines with binding affinity that is comparable to heparin. Viability and cytotoxicity were shown to strongly correlate with the dynamics of cellular milieu, as well as hydrogel type. Using on-chip immunofluorescence, production of reactive oxygen species and apoptosis were imaged and quantitatively analyzed. We then show how macrophages in our microfluidic system were shifted from a proinflammatory to an immunosuppressive phenotype when encapsulated in Alg/Alg-S, reflecting in vivo TME dynamics. LC-MS proteomic profiling of tumor cells sorted from the TME scaffolds revealed upregulation of proteins involved in cell-cell interactions and immunomodulation in Alg/Alg-S scaffolds, correlating with in vivo findings and demonstrating the appropriateness of Alg/Alg-S as an ECM biomimetic. Finally, we show the formation of large tumor-derived vesicles, formed exclusively in Alg/Alg-S scaffolds. Altogether, our system offers a robust platform for quantitative description of the breast TME that successfully recapitulates in vivo patterns.
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关键词
Biomaterials,Tumor microenvironment,Breast cancer,microfluidics,3D disease models,Proteomic profiling
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