In tissue engineering, cell-adhesion peptides (CAPs) such as the ubiquitous arginine–glycine–aspartic acid (RGD) sequence have allowed the functionalization of synthetic materials to mimic macromolecules of the extracellular matrix (ECM). However, the variety of ECM macromolecules makes it challenging to reproduce all of the native tissue functions with only a limited variety of CAPs. Screening of libraries of CAPs, analogous to high-throughput drug discovery assays, can help to identify new sequences directing cell organization. However, challenges to this approach include the automation of cell seeding in three dimensions and characterization methods. Here, we report a method for robotically generating a library of 16 CAPs to identify a microenvironment capable of directing a chain-like morphology in olfactory ensheathing cells (OECs), a cell type of particular interest for guiding axon growth in spinal cord injury repair. This approach resulted in the identification of one CA...

中文翻译：

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超越 RGD：在 3D 细胞培养中筛选细胞粘附肽库。

在组织工程中，细胞粘附肽 (CAP)，例如无处不在的精氨酸-甘氨酸-天冬氨酸 (RGD) 序列，已允许合成材料的功能化以模拟细胞外基质 (ECM) 的大分子。然而，ECM 大分子的多样性使得仅用有限种类的 CAP 重现所有天然组织功能具有挑战性。CAP 文库的筛选类似于高通量药物发现分析，可以帮助识别指导细胞组织的新序列。然而，这种方法面临的挑战包括三个维度和表征方法的细胞接种自动化。在这里，我们报告了一种机器人生成 16 个 CAP 库的方法，以识别能够在嗅鞘细胞 (OEC) 中引导链状形态的微环境，一种在脊髓损伤修复中指导轴突生长的细胞类型。这种方法导致识别出一个 CA...