Construction of vascular tissues with macro-porous nano-fibrous scaffolds and smooth muscle cells enriched from differentiated embryonic stem cells.

Construction of vascular tissues with macro-porous nano-fibrous scaffolds and smooth muscle cells enriched from differentiated embryonic stem cells.
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DOI:
10.1371/journal.pone.0035580
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发表时间:
2012
期刊:
影响因子:
3.7
通讯作者:
Chen YE
Chen YE
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Hu J;Xie C;Ma H;Yang B;Ma PX;Chen YE

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Vascular smooth muscle cells (SMCs) have been broadly used for constructing tissue-engineered blood vessels. However, the availability of mature SMCs from donors or patients is very limited. Derivation of SMCs by differentiating embryonic stem cells (ESCs) has been reported, but not widely utilized in vascular tissue engineering due to low induction efficiency and, hence, low SMC purity. To address these problems, SMCs were enriched from retinoic acid induced mouse ESCs with LacZ genetic labeling under the control of SM22α promoter as the positive sorting marker in the present study. The sorted SMCs were characterized and then cultured on three-dimensional macro-porous nano-fibrous scaffolds in vitro or implanted subcutaneously into nude mice after being seeded on the scaffolds. Our data showed that the LacZ staining, which reflected the corresponding SMC marker SM22α expression level, was efficient as a positive selection marker to dramatically enrich SMCs and eliminate other cell types. After the sorted cells were seeded into the three-dimensional nano-fibrous scaffolds, continuous retinoic acid treatment further enhanced the SMC marker gene expression level while inhibited pluripotent maker gene expression level during the in vitro culture. Meanwhile, after being implanted subcutaneously into nude mice, the implanted cells maintained the positive LacZ staining within the constructs and no teratoma formation was observed. In conclusion, our results demonstrated the potential of SMCs derived from ESCs as a promising cell source for therapeutic vascular tissue engineering and disease model applications.
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