کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10159599 | 54 | 2013 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Enhanced differentiation of human neural crest stem cells towards the Schwann cell lineage by aligned electrospun fiber matrix
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بیو مهندسی (مهندسی زیستی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Human pluripotent stem cell-derived neural crest stem cells (NCSCs) provide a promising cell source for generating Schwann cells in the treatment of neurodegenerative diseases and traumatic injuries in the peripheral nervous system. Influencing cell behavior through a synthetic matrix topography has been shown to be an effective approach to directing stem cell proliferation and differentiation. Here we have investigated the effect of nanofiber topography on the differentiation of human embryonic stem cell-derived NCSCs towards the Schwann cell lineage. Using electrospun fibers of different diameters and alignments we demonstrated that aligned fiber matrices effectively induced cell alignment, and that fiber matrices with average diameters of 600 nm and 1.6 μm most effectively promoted NCSC differentiation towards the Schwann cell lineage compared with random fibers and two-dimensional tissue culture plates. More importantly, human NCSCs that were predifferentiated in Schwann cell medium for 2 weeks exhibited higher sensitivity to the aligned fiber topography than undifferentiated NCSCs. This study provides an efficient protocol for Schwann cell derivation by combining an aligned nanofiber matrix and an optimized differentiation medium, and highlights the importance of matching extrinsic matrix signaling with cell intrinsic programming in a temporally specific manner.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Acta Biomaterialia - Volume 9, Issue 8, August 2013, Pages 7727-7736
Journal: Acta Biomaterialia - Volume 9, Issue 8, August 2013, Pages 7727-7736
نویسندگان
Yong-Juan Ren, Shuming Zhang, Ruifa Mi, Qiuyue Liu, Xianmin Zeng, Mahendra Rao, Ahmet Hoke, Hai-Quan Mao,