Evaluating Differentiation Status of Mesenchymal Stem Cells by Label-Free Microscopy System and Machine Learning

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 st...

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Bibliographic Details
Published in:Cells (Basel, Switzerland) Switzerland), 2023-05, Vol.12 (11), p.1524
Main Authors: Kong, Yawei, Ao, Jianpeng, Chen, Qiushu, Su, Wenhua, Zhao, Yinping, Fei, Yiyan, Ma, Jiong, Ji, Minbiao, Mi, Lan
Format: Article
Language:English
Subjects:
Analysis
Breast cancer
Cell differentiation
Cell therapy
Enzymes
FLIM
Fluorescence microscopy
Health aspects
Investigations
label-free
Labeling
Learning algorithms
Lipids
Machine learning
Mesenchymal stem cells
Metabolism
Methods
Microscopy
Morphology
MSCs
Phosphorylation
Proteins
Respiration
Spectrum analysis
SRS
Statistical analysis
Stem cells
Tissue engineering
Transplantation
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Summary: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.
ISSN:2073-4409
2073-4409
DOI:10.3390/cells12111524