Assessing Biomaterial-Induced Stem Cell Lineage Fate by Machine Learning-Based Artificial Intelligence

Assessing Biomaterial-Induced Stem Cell Lineage Fate by Machine Learning-Based Artificial Intelligence

© 2023 Wiley-VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 35(2023), 19 vom: 01. Mai, Seite e2210637
1. Verfasser: Zhou, Yingying (VerfasserIn)
Weitere Verfasser: Ping, Xianfeng, Guo, Yusi, Heng, Boon Chin, Wang, Yijun, Meng, Yanze, Jiang, Shengjie, Wei, Yan, Lai, Binbin, Zhang, Xuehui, Deng, Xuliang
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article artificial intelligence gene expression pattern lineage fate machine learning mesenchymal stem cells regenerative biomaterials Biocompatible Materials
Beschreibung
Zusammenfassung:© 2023 Wiley-VCH GmbH.
Current functional assessment of biomaterial-induced stem cell lineage fate in vitro mainly relies on biomarker-dependent methods with limited accuracy and efficiency. Here a "Mesenchymal stem cell Differentiation Prediction (MeD-P)" framework for biomaterial-induced cell lineage fate prediction is reported. MeD-P contains a cell-type-specific gene expression profile as a reference by integrating public RNA-seq data related to tri-lineage differentiation (osteogenesis, chondrogenesis, and adipogenesis) of human mesenchymal stem cells (hMSCs) and a predictive model for classifying hMSCs differentiation lineages using the k-nearest neighbors (kNN) strategy. It is shown that MeD-P exhibits an overall accuracy of 90.63% on testing datasets, which is significantly higher than the model constructed based on canonical marker genes (80.21%). Moreover, evaluations of multiple biomaterials show that MeD-P provides accurate prediction of lineage fate on different types of biomaterials as early as the first week of hMSCs culture. In summary, it is demonstrated that MeD-P is an efficient and accurate strategy for stem cell lineage fate prediction and preliminary biomaterial functional evaluation
Beschreibung:Date Completed 12.05.2023
Date Revised 12.05.2023
published: Print-Electronic
Citation Status MEDLINE
ISSN:1521-4095
DOI:10.1002/adma.202210637