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Issue 26, 2016
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Dynamic self-organization of microwell-aggregated cellular mixtures

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Abstract

Cells with different cohesive properties self-assemble in a spatiotemporal and context-dependent manner. Previous studies on cell self-organization mainly focused on the spontaneous structural development within a short period of time during which the cell numbers remained constant. However the effect of cell proliferation over time on the self-organization of cells is largely unexplored. Here, we studied the spatiotemporal dynamics of self-organization of a co-culture of MDA-MB-231 and MCF10A cells seeded in a well defined space (i.e. non-adherent microfabricated wells). When cell-growth was chemically inhibited, high cohesive MCF10A cells formed a core surrounded by low cohesive MDA-MB-231 cells on the periphery, consistent with the differential adhesion hypothesis (DAH). Interestingly, this aggregate morphology was completely inverted when the cells were free to grow. At an initial seeding ratio of 1 : 1 (MDA-MB-231 : MCF10A), the fast growing MCF10A cells segregated in the periphery while the slow growing MDA-MB-231 cells stayed in the core. Another morphology developed at an inequal seeding ratio (4 : 1), that is, the cell mixtures developed a side-by-side aggregate morphology. We conclude that the cell self-organization depends not only on the cell cohesive properties but also on the cell seeding ratio and proliferation. Furthermore, by taking advantage of the cell self-organization, we purified human embryonic stem cells-derived pancreatic progenitors (hESCs-PPs) from co-cultured feeder cells without using any additional tools or labels.

Graphical abstract: Dynamic self-organization of microwell-aggregated cellular mixtures

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Publication details

The article was received on 22 Feb 2016, accepted on 03 Jun 2016 and first published on 06 Jun 2016


Article type: Paper
DOI: 10.1039/C6SM00456C
Soft Matter, 2016,12, 5739-5746

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    Dynamic self-organization of microwell-aggregated cellular mixtures

    W. Song, C. Tung, Y. Lu, Y. Pardo, M. Wu, M. Das, D. Kao, S. Chen and M. Ma, Soft Matter, 2016, 12, 5739
    DOI: 10.1039/C6SM00456C

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