Image-based focused counting of dividing cells for non-invasive monitoring of regenerative medicine products

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Despite the growing numbers of successful applications in regenerative medicine, biotechnologies for evaluating the quality of cells remain limited. To evaluate the cultured cells non-invasively, image-based cellular assessment method holds great promise. However, although there are various image-processing algorithms, very few studies have focused to prove the effectiveness of phase contrast images with risk assessment example that reflects actual difficulties in regenerative medicine products. In this study, we developed a simple image-processing method to recognize the number of dividing cells in time-course phase-contrast microscopic images, and applied this method to assess the irregular proliferation behavior in normal cells. Practically, as a model, rapid proliferating human fibrosarcoma cells were mixed in normal human fibroblasts in the same culture dish, and their sarcoma existence was evaluated. As a result, the existence of sarcoma population in normal cell sample could be feasibly detected within earliest period of cell culture by their irregular rise of accumulated counts of dividing cells. Our image-processing technique also illustrates the technical effectiveness of combining intra-frame and inter-frame image processing to accurately count only the dividing cells. Our concept of focused counting of dividing cells shows a successful example of image-based analysis to quickly and non-invasively monitor the regular state of regenerative medicine products.

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