Lee Sanghwa, Tak Eunyoung, Choi Jiwan, Kang Seoon, Lee Kwanhee, Namgoong Jung-Man, Kim Jun Ki
Biomedical Engineering Research Center, Asan Medical Center, Seoul 05505, Republic of Korea.
Department of Convergence Medicine, Brain Korea 21 Project, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.
Biomater Res. 2025 May 7;29:0190. doi: 10.34133/bmr.0190. eCollection 2025.
Technology has been developed to monitor the differentiation process of human mesenchymal stem cells (hMSCs) into hepatocyte-like cells (HLCs) and hepatic progenitor cells (HPCs). These cell lineages, differentiated from MSCs, are ethically unproblematic and are gaining attention as promising cell-based therapies for treating various liver injuries. High-sensitivity, label-free, real-time monitoring technologies integrated with artificial intelligence have been used to evaluate and optimize cell differentiation for enhancing the efficiency of cell therapy delivery. Using an Au-ZnO nanorod array-based surface-enhanced Raman scattering (SERS) sensing chip, cell differentiation from hMSCs to HPCs and HLCs was nondestructively monitored through spectral analysis of cell secretions. Principal component extraction was employed to reduce variables, followed by discriminant analysis (DA). The application of principal component-linear discriminant analysis (PC-LDA), an artificial intelligence algorithm, to spectral data enabled clear grouping of hMSCs, HPCs, and HLCs, with monitoring accuracies of 96.3%, 98.8%, and 98.8%, respectively. Spectral changes observed during the differentiation from hMSCs to HPCs and from HPCs to HLCs over several days demonstrated the effectiveness of SERS combined with machine learning algorithm analysis for differentiation monitoring. This approach enabled real-time, nondestructive observation of cell differentiation with minimal sample labeling and preprocessing, making it useful for sensing differentiation validation and stability. The machine learning- and nanostructure-based SERS evaluation system was applied to the differentiation of ethically sourced MSCs and demonstrated substantial potential for clinical applicability through the use of patient-derived samples.
已开发出用于监测人间充质干细胞（hMSCs）向肝细胞样细胞（HLCs）和肝祖细胞（HPCs）分化过程的技术。这些从间充质干细胞分化而来的细胞谱系在伦理上没有问题，作为治疗各种肝损伤的有前景的细胞疗法正受到关注。已使用与人工智能集成的高灵敏度、无标记、实时监测技术来评估和优化细胞分化，以提高细胞治疗的递送效率。使用基于金 - 氧化锌纳米棒阵列的表面增强拉曼散射（SERS）传感芯片，通过对细胞分泌物的光谱分析，对hMSCs向HPCs和HLCs的细胞分化进行了无损监测。采用主成分提取来减少变量，随后进行判别分析（DA）。将人工智能算法主成分 - 线性判别分析（PC - LDA）应用于光谱数据，能够清晰地将hMSCs、HPCs和HLCs分组，监测准确率分别为96.3%、98.8%和98.8%。在几天内从hMSCs向HPCs以及从HPCs向HLCs分化过程中观察到的光谱变化证明了SERS与机器学习算法分析相结合用于分化监测的有效性。这种方法能够以最少的样本标记和预处理对细胞分化进行实时、无损观察，使其可用于传感分化验证和稳定性研究。基于机器学习和纳米结构的SERS评估系统应用于伦理来源的间充质干细胞的分化，并通过使用患者来源的样本证明了其在临床适用性方面的巨大潜力。
