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用于 3D 建模神经组织的生物墨水成分和打印参数。

Bioink Composition and Printing Parameters for 3D Modeling Neural Tissue.

机构信息

Department of Brain and Behavioural Sciences, University of Pavia, Via Forlanini 6, 27100 Pavia, Italy.

Laboratory of Neurobiology and Neurogenetic, Golgi-Cenci Foundation, Corso S. Martino 10, 20081 Abbiategrasso, Milan, Italy.

出版信息

Cells. 2019 Aug 5;8(8):830. doi: 10.3390/cells8080830.

DOI:10.3390/cells8080830
PMID:31387210
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6721723/
Abstract

Neurodegenerative diseases (NDs) are a broad class of pathologies characterized by the progressive loss of neurons in the central nervous system. The main problem in the study of NDs is the lack of an adequate realistic experimental model to study the pathogenic mechanisms. Induced pluripotent stem cells (iPSCs) partially overcome the problem, with their capability to differentiate into almost every cell types; even so, these cells alone are not sufficient to unveil the mechanisms underlying NDs. 3D bioprinting allows to control the distribution of cells such as neurons, leading to the creation of a realistic in vitro model. In this work, we analyzed two biomaterials: sodium alginate and gelatin, and three different cell types: a neuroblastoma cell line (SH-SY5Y), iPSCs, and neural stem cells. All cells were encapsulated inside the bioink, printed and cultivated for at least seven days; they all presented good viability. We also evaluated the maintenance of the printed shape, opening the possibility to obtain a reliable in vitro neural tissue combining 3D bioprinting and iPSCs technology, optimizing the study of the degenerative processes that are still widely unknown.

摘要

神经退行性疾病(NDs)是一类广泛的病理学,其特征是中枢神经系统中神经元的进行性丧失。在 NDs 研究中主要存在的问题是缺乏一个合适的现实实验模型来研究发病机制。诱导多能干细胞(iPSCs)部分解决了这个问题,其具有分化为几乎所有细胞类型的能力;即便如此,这些细胞本身还不足以揭示 NDs 背后的机制。3D 生物打印可以控制神经元等细胞的分布,从而创建一个现实的体外模型。在这项工作中,我们分析了两种生物材料:海藻酸钠和明胶,以及三种不同的细胞类型:神经母细胞瘤细胞系(SH-SY5Y)、iPSCs 和神经干细胞。所有细胞都被封装在生物墨水中,打印并培养至少七天;它们都表现出良好的活力。我们还评估了打印形状的保持情况,为结合 3D 生物打印和 iPSCs 技术获得可靠的体外神经组织开辟了可能性,从而优化了对仍广泛未知的退行性过程的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e01/6721723/0dc834dc79da/cells-08-00830-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e01/6721723/0dc834dc79da/cells-08-00830-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e01/6721723/0dc834dc79da/cells-08-00830-g004.jpg

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