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Mizuho ISHIKAWA
"Defining epithelial stem cell heterogeneity through undulating structures of the skin and oral mucosa" Division of Skin Regeneration and Aging, Medical Institute of Bioregulation, Kyushu University, Japan |
Abstract
The human skin has a characteristic undulating structure at the epidermal-dermal junction, which supports physical strength and can correlate to epithelial stem cell localization. However, the lack of undulating structures in mouse skin makes it difficult to experimentally demonstrate how tissue structure regulates the heterogeneous localization of epithelial stem cells. Here, we take advantage of mouse oral mucosa, which harbors a similar undulating structure to human skin, and define stem cell dynamics. We find that slow- and fast-cycling stem cells in the oral epithelium lie in a specific anatomical location relative to the undulating structure and replenish their compartments during homeostasis. A 3-D culture using micropatterned collagen scaffolds shows that the mechanical forces generated by the undulation partially induce proliferative heterogeneity of epithelial stem cells. This study proposes tissue surface structure as a common principle as a niche component that defines the localization of compartmentalized epithelial stem cell populations.
The human skin has a characteristic undulating structure at the epidermal-dermal junction, which supports physical strength and can correlate to epithelial stem cell localization. However, the lack of undulating structures in mouse skin makes it difficult to experimentally demonstrate how tissue structure regulates the heterogeneous localization of epithelial stem cells. Here, we take advantage of mouse oral mucosa, which harbors a similar undulating structure to human skin, and define stem cell dynamics. We find that slow- and fast-cycling stem cells in the oral epithelium lie in a specific anatomical location relative to the undulating structure and replenish their compartments during homeostasis. A 3-D culture using micropatterned collagen scaffolds shows that the mechanical forces generated by the undulation partially induce proliferative heterogeneity of epithelial stem cells. This study proposes tissue surface structure as a common principle as a niche component that defines the localization of compartmentalized epithelial stem cell populations.
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Kenshiro TAGUCHI
"Next-Generation-Dense-Cell tracking and beyond: The DeepACT framework" Graduate Degree Program of Health Data Science, Teikyo University, Japan |
Abstract
Cell tracking is a fundamental technique for understanding the dynamics of cultured cells and assessing their stemness. In particular, the ability to track a cultured epidermal sheet enriched in keratinocyte stem cells plays a crucial role in evaluating the therapeutic efficacy of cell-based transplants for severe burn injuries. Notably, Nanba et al. (J Cell Biol 2015 & 2021) reported that there was a positive correlation between the stemness and cell migration velocity in human epidermal cells, it is possible to infer the stemness of cultured cells by measuring that behavior. Although it is potential, the conventional approaches to measuring cell behavior have relied heavily on manual annotations, making the process time-consuming, labor-intensive, and cost-inefficient. While cell tracking offers an automated alternative, existing methods are typically optimized for sparsely distributed cells and perform poorly in dense cellular environments, such as human epidermal keratinocyte cultures. To address these challenges, our research group has developed DeepACT, a novel tracking framework that integrates deep learning-based cell detection with a state-space model for robust tracking and automated quantification of cell migration in densely packed colonies (Hirose et al., Stem Cells 2021). In this seminar, we will present the development and validation of DeepACT, discuss its strengths and limitations, and explore emerging directions in cell tracking research. In particular, we will share our findings from benchmarking state-of-the-art tiny object detection models and introduce a newly developed state-space model tailored for high-density cell tracking applications.
Cell tracking is a fundamental technique for understanding the dynamics of cultured cells and assessing their stemness. In particular, the ability to track a cultured epidermal sheet enriched in keratinocyte stem cells plays a crucial role in evaluating the therapeutic efficacy of cell-based transplants for severe burn injuries. Notably, Nanba et al. (J Cell Biol 2015 & 2021) reported that there was a positive correlation between the stemness and cell migration velocity in human epidermal cells, it is possible to infer the stemness of cultured cells by measuring that behavior. Although it is potential, the conventional approaches to measuring cell behavior have relied heavily on manual annotations, making the process time-consuming, labor-intensive, and cost-inefficient. While cell tracking offers an automated alternative, existing methods are typically optimized for sparsely distributed cells and perform poorly in dense cellular environments, such as human epidermal keratinocyte cultures. To address these challenges, our research group has developed DeepACT, a novel tracking framework that integrates deep learning-based cell detection with a state-space model for robust tracking and automated quantification of cell migration in densely packed colonies (Hirose et al., Stem Cells 2021). In this seminar, we will present the development and validation of DeepACT, discuss its strengths and limitations, and explore emerging directions in cell tracking research. In particular, we will share our findings from benchmarking state-of-the-art tiny object detection models and introduce a newly developed state-space model tailored for high-density cell tracking applications.
Chair
Oliver DREESEN, ASRL, Singapore
Oliver DREESEN, ASRL, Singapore