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Manipulating the three-dimensional (3D) structures of cells is important for facilitating to repair or regenerate tissues. A self-assembly system of cells with cellulose nanofibers (CNFs) and concentrated polymer brushes (CPBs) has been developed to fabricate various cell 3D structures. To further generate tissues at an implantable level, it is necessary to carry out a large number of experiments using different cell culture conditions and material properties; however this is practically intractable. To address this issue, we present a graph neural network-based simulator (GNS) that can be trained by using assembly process images to predict the assembly status of future time steps. A total of 24 (25 steps) time-series images were recorded (four repeats for each of six different conditions), and each image was transformed into a graph by regarding the cells as nodes and the connecting neighboring cells as edges. Using the obtained data, the performances of the GNS were examined under three scenarios (i.e. changing a pair of the training and testing data) to verify the possibility of using the GNS as a predictor for further time steps. It was confirmed that the GNS could reasonably reproduce the assembly process, even under the toughest scenario, in which the experimental conditions differed between the training and testing data. Practically, this means that the GNS trained by the first 24 hour images could predict the cell types obtained three weeks later. This result could reduce the number of experiments required to find the optimal conditions for generating cells with desired 3D structures. Ultimately, our approach could accelerate progress in regenerative medicine.

権利情報:

• In Copyright
  This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in ACS Biomaterials Science & Engineering, copyright © 2024 American Chemical Society] after peer review. To access the final edited and published work see https://doi.org/10.1021/acsbiomaterials.3c01888

キーワード: cellulose nanofiber, concentrated polymer brush, hMSC, self-assembly, machine learning

刊行年月日: 2024-04-08

出版者: American Chemical Society (ACS)

掲載誌:

• ACS Biomaterials Science & Engineering (ISSN: 23739878) vol. 10 issue. 4 p. 2165-2176

研究助成金:

• Core Research for Evolutional Science and Technology JPMJCR21N7
• Japan Society for the Promotion of Science 19H04169
• Japan Society for the Promotion of Science 20F20809
• Japan Society for the Promotion of Science 21H05027
• Japan Society for the Promotion of Science 22H02133
• Japan Society for the Promotion of Science 22H03645
• NIMS Joint Research Hub Program

原稿種別: 査読前原稿 (Author's original)

MDR DOI: https://doi.org/10.48505/nims.4691

公開URL: https://doi.org/10.1021/acsbiomaterials.3c01888

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更新時刻: 2024-08-27 08:30:31 +0900

MDRでの公開時刻: 2024-08-27 08:30:31 +0900