Methodologies to assess stem cell differentiation in the culturing state are needed for regenerative medicine and tissue engineering techniques. In recent years, convolutional neural networks (CNNs), a class of deep neural networks, have made impressive advancements in image-based classification, recognition and detection tasks. CNNs have been introduced as a non-invasive cell characterization method by learning features directly from image data of unlabeled cells. Furthermore, this approach serves as a rapid and inexpensive methodology with high performance compared to traditional techniques that require complex laboratory procedures including antibody staining and gene expression analysis. Here, we studied the potential of the CNNs approach to recognize stem cell differentiation based on cell morphology utilizing phasecontrast microscopy images.We have examined the differentiation potential of bone marrow mesenchymal stem cells (BMSCs) into tenocytes, with the treatment of bone morphogenetic protein-12 (BMP-12). After treatment, the phase-contrast images of cells were obtained directly from cell culture flasks to train CNN and the differentiated phenotype of stem cells was characterized by immunostaining. CNN was able to classify the cells into three groups including non-stem cells (chondrocytes), stem cells (BMSCs) and differentiated stem cells (tenocytes) based on their morphology with 92.2 % accuracy. The presented study revealed that CNN performed faster and non-invasive cell classification task compared to traditional methodologies.
© 2020 by Walter de Gruyter Berlin/Boston
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