Morphology-based detection of senescence in expanded mesenchymal stem cells


Takemoto, Yuto (Graduate School of Pharmaceutical Sciences, Nagoya University )


The expansion culture of cells is an essential process for manufacturing cells for therapeutic use. However, it is also an activity with a huge dilemma. This is because for clinical cell therapy treatment, and also for preparing cells for establishing cell bank, it is strongly required to expand cell number by passage culture to prepare a sufficient number of cells for applications, however at the same time, it is known that such over passage culture critically damages cell quality, especially in human mesenchymal stem cells (MSCs), therefore there is always a risk of establishing cell bank with quality decayed cells. Such balancing of production efficiency and cell quality is a critical issue to produce high-quality MSCs, however, the decision of such balance has long been relying on human experiences. To maximize the efficiency of obtaining high-quality MSCs for various applications, a more practical but efficient and quantitative method to enable non-invasive continuous monitoring of MSC’s condition has been expected.
Our group has been developing “morphology-based cell quality prediction method (= morphometry)” by combining the recent image processing technology together with machine learning techniques [1,2]. We here propose the high detection performance of such morphology-based cell quality prediction method applied to evaluate senescence status in the expanded mesenchymal stem cells. In this work, we have intentionally prepared the over-passaged mesenchymal stem cells and measured their morphological descriptor profiles from the time-course microscopic images and their total expression profile by RNA-seq. From the machine learning of passage numbers and their morphological profiles, especially their population heterogeneity information, we succeeded in clearly discriminating the “over-passaged MSCs” which lost their growth potency and differentiation status. Moreover, our expression profile analysis indicated some novel marker gene expression profiles to target for understanding the quality decay in over-passaged MSCs. We also developed a novel image-based single-cell cytometry analysis method, to profile the heterogeneous cell populations in expanded MSCs, and show that there are several morphological categories to be detected for understanding the senescence type in MSCs.


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