Image2_The Effect of Hypoxic and Normoxic Culturing Conditions in Different Breast Cancer 3D Model Systems.TIF
The field of 3D cell cultures is currently emerging, and material development is essential in striving toward mimicking the microenvironment of a native tissue. By using the response of reporter cells to a 3D environment, a comparison between materials can be assessed, allowing optimization of material composition and microenvironment. Of particular interest, the response can be different in a normoxic and hypoxic culturing conditions, which in turn may alter the conclusion regarding a successful recreation of the microenvironment. This study aimed at determining the role of such environments to the conclusion of a better resembling cell culture model to native tissue. Here, the breast cancer cell line MCF7 was cultured in normoxic and hypoxic conditions on patient-derived scaffolds and compared at mRNA and protein levels to cells cultured on 3D printed scaffolds, Matrigel, and conventional 2D plastics. Specifically, a wide range of mRNA targets (40), identified as being regulated upon hypoxia and traditional markers for cell traits (cancer stem cells, epithelial–mesenchymal transition, pluripotency, proliferation, and differentiation), were used together with a selection of corresponding protein targets. 3D cultured cells were vastly different to 2D cultured cells in gene expression and protein levels on the majority of the selected targets in both normoxic and hypoxic culturing conditions. By comparing Matrigel and 3DPS-cultured cells to cells cultured on patient-derived scffolds, differences were also noted along all categories of mRNA targets while specifically for the GLUT3 protein. Overall, cells cultured on patient-derived scaffolds closely resembled cells cultured on 3D printed scaffolds, contrasting 2D and Matrigel-cultured cells, regardless of a normoxic or hypoxic culturing condition. Thus, these data support the use of either a normoxic or hypoxic culturing condition in assays using native tissues as a blueprint to optimize material composition.
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References
- https://doi.org//10.3389/fcell.2019.00004
- https://doi.org//10.1186/s12964-020-0530-4
- https://doi.org//10.1242/jcs.079509
- https://doi.org//10.1016/s0753-3322%2803%2900098-2
- https://doi.org//10.1002/ijc.23103
- https://doi.org//10.1038/35025220
- https://doi.org//10.1016/j.molonc.2015.12.006
- https://doi.org//10.1158/1541-7786.mcr-14-0049
- https://doi.org//10.1073/pnas.0709185104
- https://doi.org//10.3389/fbioe.2016.00012
- https://doi.org//10.1016/j.biomaterials.2019.119705
- https://doi.org//10.1016/j.dib.2020.105860
- https://doi.org//10.1242/jcs.146142
- https://doi.org//10.3390/ijms20051195
- https://doi.org//10.1007/s12551-015-0186-2
- https://doi.org//10.1016/j.compositesb.2018.02.012
- https://doi.org//10.1038/nmeth.2089
- https://doi.org//10.1186/s12943-016-0502-x
- https://doi.org//10.1016/j.celrep.2013.11.014
- https://doi.org//10.1088/1748-605X/ac0451
- https://doi.org//10.1016/j.addr.2014.01.001
- https://doi.org//10.2147/hp.s92198
- https://doi.org//10.1038/onc.2016.252
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