A practical guide to promote informatics-driven efficient biotopographic material development

• Main Authors: Yuanlong Guo, Jiaomei Mi, Chen Ye, Yong Ao, Mengru Shi, Zhengjie Shan, Bingzhi Li, Zetao Chen, Zhuofan Chen, Krasimir Vasilev, Yin Xiao
• Format: Article
• Language: English
• Published: KeAi Communications Co., Ltd. 2022-02-01
• Series: Bioactive Materials
• Subjects: Biotopographic materials; Cell culture device; Confounding factor; Database; Informatics
• Online Access: http://www.sciencedirect.com/science/article/pii/S2452199X21002991

Micro/nano topographic structures have shown great utility in many biomedical areas including cell therapies, tissue engineering, and implantable devices. Computer-assisted informatics methods hold great promise for the design of topographic structures with targeted properties for a specific medical application. To benefit from these methods, researchers and engineers require a highly reusable “one structural parameter – one set of cell responses” database. However, existing confounding factors in topographic cell culture devices seriously impede the acquisition of this kind of data. Through carefully dissecting the confounding factors and their possible reasons for emergence, we developed corresponding guideline requirements for topographic cell culture device development to remove or control the influence of such factors. Based on these requirements, we then suggested potential strategies to meet them. In this work, we also experimentally demonstrated a topographic cell culture device with controlled confounding factors based on these guideline requirements and corresponding strategies. A “guideline for the development of topographic cell culture devices” was summarized to instruct researchers to develop topographic cell culture devices with the confounding factors removed or well controlled. This guideline aims to promote the establishment of a highly reusable “one structural parameter – one set of cell responses” database that could facilitate the application of informatics methods, such as artificial intelligence, in the rational design of future biotopographic structures with high efficacy.