Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education), Department of Optical Science and Engineering, Shanghai Engineering Research Center of Ultra-Precision Optical Manufacturing, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
Department of Physics, Fudan University, Shanghai 200433, China.
Cells. 2023 May 31;12(11):1524. doi: 10.3390/cells12111524.
Mesenchymal stem cells (MSCs) play a crucial role in tissue engineering, as their differentiation status directly affects the quality of the final cultured tissue, which is critical to the success of transplantation therapy. Furthermore, the precise control of MSC differentiation is essential for stem cell therapy in clinical settings, as low-purity stem cells can lead to tumorigenic problems. Therefore, to address the heterogeneity of MSCs during their differentiation into adipogenic or osteogenic lineages, numerous label-free microscopic images were acquired using fluorescence lifetime imaging microscopy (FLIM) and stimulated Raman scattering (SRS), and an automated evaluation model for the differentiation status of MSCs was built based on the K-means machine learning algorithm. The model is capable of highly sensitive analysis of individual cell differentiation status, so it has great potential for stem cell differentiation research.
间充质干细胞（MSCs）在组织工程中起着至关重要的作用，因为它们的分化状态直接影响最终培养组织的质量，这对移植治疗的成功至关重要。此外，在临床环境中对 MSC 分化的精确控制对于干细胞治疗至关重要，因为低纯度的干细胞可能导致致瘤问题。因此，为了解决间充质干细胞在向成脂或成骨谱系分化过程中的异质性，使用荧光寿命成像显微镜（FLIM）和受激拉曼散射（SRS）获取了大量无标记的微观图像，并基于 K-均值机器学习算法构建了用于间充质干细胞分化状态的自动评估模型。该模型能够对单个细胞分化状态进行高度敏感的分析，因此在干细胞分化研究中具有很大的潜力。
