[Introduction] Fibroblasts are the major cells that consist of connective tissues in animal tissues and are known as the main extracellular matrix (ECM) production cells to maintain the tissue physical properties. Although they are cells that are famous and frequently found in our body, their biological mechanisms, especially the differences from other similar fibroblastic cells are still unclear. From the tissue engineering perspective, it is an important ECM providing cells that contribute to forming solid and matured tissue with in vivo-like physical strength, therefore, for designing tough texture tissues, fibroblasts are essential cells to be included. Moreover, the function of fibroblasts is also important for understanding fibrosis formation, which is still difficult to control with conventional medical treatment. Furthermore, it is known to facilitate vascular network formations in skin-like layered tissues, therefore it is also essential for developing dermal skin models for safety test applications. However, obtaining quality-controlled fibroblasts is still difficult, because fibroblasts decrease their proliferation and factor production potencies by the continuous passages like the other normal cells, and most importantly, their quality decay timing commonly comes at sudden and is extremely difficult to predict accurately in prior. In our group, we have been reporting the effective performance of “morphology-based cell quality evaluation method (=morphometry)” as a non-invasive real-time cell quality control technique [1-3]. In this work, we investigated the morphological profiles of continuously passaged fibroblasts to define whether we can predict its quality decay only from their microscopic images.
[Method] Human normal fibroblasts (3-lots) were continuously passaged for twenty-two passages, and we collected all their time-course morphological changes in each passaged sample with the interval of 6 hours for 5 days. Moreover, we collected their total RNA’s and made RNA-seq reveal the correlation between the morphological changes and their toral expression profiles.
[Results and conclusions] From the continuously passaged fibroblast morphological information analysis, we found that their sudden decrease of proliferation potency can be predicted in prior during their passage process only by their morphological descriptors combined with AI model. Furthermore, we found that there are gene networks correlated to such morphological changes through the passage stress accumulation. Our results show a novel approach using morphological information to advance senescence research.
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